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Improving Reading Outcomes
As previously described, improving reading capacity and comprehension is predicated on improving student fluency and their vocabulary. Therefore, we will describe three methods that have a strong research base testifying to their effects on improving student reading. First we will discuss repeated reading which has been shown to improve student fluency. Next, we will discuss a vocabulary instruction technique known as the Keyword Method. Third, we will discuss a reading comprehension strategy known as collaborative strategic reading. Finally, we will discuss a method that has been found to improve reading in the content area, TWA.
Repeated Reading
One of the most widely used and easy to administer interventions to increase fluency is repeated reading (RR). The intervention practice has been the subject of a number of reviews of effectiveness that showed it to be effective particularly at the elementary level (Chard, Ketterlin-Geller, Baker, Doabler, & Apichatabutra, 2009; Lee & Yoon, 2017; What Works Clearinghouse, 2014). In general, RR requires a student to sit in a quiet location with a mentor (e.g., teacher, para, peer) and reads a passage aloud until they meet a fluency goal (Therrien, Gormley, & Kubina, 2006). The following are procedures for performing the intervention.
Step 1: Prompt Student. “Read this story the best you can and as quickly as you can. Pay attention to what you are reading, as you will need to answer a few questions.”
Step 2: Read Prompts. Ask student to read question-generation prompts (“who, what, where, when, how” questions, such as “Who is the main character?” “Where does the story take place?”).
Step 3: Reread. Ask student to reread passage aloud until reaching goal-
• No less than 2 times.
• No more than 4 times.
Step 4: Correct Errors.
• If student pauses during reading, correct word and have student repeat.
• Correct all other errors after passage read and ask student to repeat them.
Step 5: Praise. Provide feedback to student on improvements in speed and accuracy.
Step 6: Adapt and Answer. Ask student to adapt and answer questions you have placed on cue cards.
Error correction process:
1. If no answer or incorrect answer first time, prompt student to look for information in the passage: “See if you can find the answer in the passage.”
2. If no or incorrect answer second time, point to sentence(s) where answer can be found and prompt: “See if you can find the answer in this sentence.”
3. If no or incorrect answer third time, provide answer and point to where you found the answer.
Step 7: End and Adjust. When session ends, adjust the reading material for next time:
Adjust the difficulty of the reading material for use in the subsequent session using the following guidelines.
• If, for three sessions in a row, the student was unable to reach the fluency goal in four readings, lower the reading material to be used in the subsequent session by one grade level.
• If, for three sessions in a row, the student reached the fluency goal in two readings or less, raise the reading material to be used in the next session by one grade level (Therrien, Gormley, & Kubina, 2006, p. 25).
The Keyword Method
Mnemonic instruction has been a widely used method to improve comprehension and vocabulary acquisition (Bryant, Goodwin, Bryant, & Higgins, 2003; Scruggs, Mastropieri, & Berkeley, 2010). According to Scruggs and colleagues, “A mnemonic, then, is any procedure or operation designed to improve one’s memory” (p. 79). The specific mnemonic strategy described here is the keyword method. Essentially, the keyword method uses a similar sounding proxy for the target word to aid in acquisition of the new word. For example, if we were to use this method to teach Levi the meaning of the word depredation, from The Hobbit (Tolkien, 1937, 1938, 1966), which means, “the act of preying upon or plundering; robbery; ravage” (Depredation, 2017), we may use the keyword predator. Next, we may show a picture from the movie The Predator to help build a concrete representation in his mind. The following is a step-by-step instruction of the keyword method by Uberti, Scruggs, & Mastropieri (2003).
1. 1. Carefully examine the class reading materials.
2. Identify important and challenging vocabulary words.
3. Make a list of those vocabulary words and their definitions.
Vocabulary Word Definition
Aloft High up in the sky
Specimen Part of a sample to be studied
Daze In a state of confusion
Abandon To leave behind
4. Examine each vocabulary word that will be challenging and recode that word to an acoustically similar, but concrete and familiar word or what we call a keyword or cue word. For example, “leaf” sounds like “aloft.”
5. Take that keyword and relate it in an interactive picture with the to-beremembered information. In this case, a leaf floating high up in the sky.
6. Use clip art and make the picture.
7. Think up some relevant teacher instructions for your target student population. In this case, something like the following: Here is a new way to help you remember the definition of some vocabulary words. When you hear the word “aloft,” think of the keyword “leaf.” Leaf sounds like aloft, and it is easily pictured. What is the keyword for aloft? “Leaf,” correct! Now remember this picture of a leaf high up in the sky. When I ask you what aloft means, first think of the keyword that sounds like aloft. In this case it is what? Right, leaf. Now think back to the picture with the leaf in it and think about what was happening in that picture. Right, a leaf was high up in the sky. That should help you with the definition of aloft, that is what? Correct, high up in the sky.
8. Remember, when using the keyword method:
• First learn the keyword.
• Second, remember the picture of the keyword and the definition doing some thing together.
• Third, when asked the definition, think of the keyword and what was happening in that picture and retrieve the definition.
Collaborative Strategic Reading
Collaborative strategic reading (CSR) is a small group intervention that has been demonstrated to improve the reading comprehension outcomes of students with disabilities (Boardman, et al., 2016). CSR utilizes strategy instruction (Reid, Lienemann, & Hagaman, 2013) within groups of five students of different achievement levels (Klingner & Vaughn, 1998). After teacher instruction of the methods of conducting CSR, groups are formed with the following revolving roles: (a) leader - discusses the text to read and strategy to use; (b) clunk expert - uses clunk cards to remind students of the strategy being used (See Figure 10)); (c) announcer – calls on different group members; (d) encourager - gives positive feedback; (e) reporter – reports the groups efforts to the class after finished; and (f) time keeper – keeps time The CSR strategy is implemented as follows:
BEFORE READING
PREVIEW:
S: We know that today’s topic is _____.
S: Let’s brainstorm and write everything we already know about the topic in our Learning Logs.
S: Announcer, please call on people to share their best ideas.
S: Now let’s predict and write everything we think we might learn about from reading today.
S: Announcer, please call on people to share their best ideas.
DURING READING
READ:
S: Who would like to read the next section? Announcer, please call on someone to read.
CLICK AND CLUNK:
S: Did everyone understand what we read? If you did not, write your clunks in your learning log.
S: (if someone has a clunk): Announcer, please call on someone to say their clunk.
S: (if someone has a clunk): Clunk Expert, please help us out. GET THE GIST:
S: What is the most important idea we have learned about the topic so far? Everyone think of the gist.
S: Now we will go around the group and each say the gist in our own words. Announcer, please call on someone to share their answer. Go back and do all of the steps in this column over for each section.
AFTER READING
WRAP UP:
S: Now let’s think of some questions to check if we really understood what we read. Everyone write your questions in your Learning Log. Remember to start your questions with who, when, what, where, why, or how.
S: Announcer, please call on people to share their best questions.
S: In our Learning Logs, let’s write down as many statements as we can about what we learned.
S: Announcer, please call on people to share something they learned.
Compliments and Suggestions:
S: The Encourager has been watching carefully and will now tell us two things we did really well as a group today.
S: Is there anything that would help us do even better next time? (Klingner & Vaughn, 1998, p. 35).
Click and Clunk Cards
CLUNK CARD #1
Reread the sentence without the word. Think about what would make sense.
CLUNK CARD #2
Reread the sentence with the clunk and the sentences before or after the clunk looking for clues.
CLUNK CARD #3
Look for a prefix or suffix in the word that might help.
CLUNK CARD #4
Break the word apart and look for smaller words that you know. (Klingner & Vaughn, 1998, p. 34)
Think Before Reading, Think While Reading, Think After Reading
Reading in the content area has received growing attention in recent years, particularly with respect to implementing the maligned Common Core State Standards. Self-regulated strategy development (SRSD) has become a heavily researched and validated method for improving the reading (Mason, Reid, & Hagaman, Building comprehension in adolescents: Powerful strategies for improving reading and writing in content areas, 2012) and writing (Losinski, Cuenca-Carlino, Zablocki, & Teagarden, 2014) skills of students with disabilities. A particularly effective SRSD intervention to improve comprehension of subject area content is the think before reading, think while reading, think after reading (TWA) intervention (Mason, 2013; Mason, Reid, & Hagaman, 2012). The intervention is taught in six lessons based on explicit instruction and include: goal setting, self-instruction, self-monitoring and selfreinforcement. The actual TWA strategy follows the following process which was adapted from lesson 2, the teacher modeling lesson in Mason, Reid, and Hagaman (2012):
SAY, “I’ve gotta read this book for social studies class. The TWA strategy is going to help me figure out what is going on and remember it. So, what should I do first? Procrastinate? No. Mr. L. said I should do three things before I start reading. First, I need to think about what the author is trying to say. Right. The title is ‘Being and Nothingness. (Sartre, 1956)’ Ugh. OK, let’s read the first sentence.
(Read the first sentence.)
Wow. Heavy. I think I may need to read the second sentence.
(Read the second sentence.)
Okay. So, let me try to figure this out. Sartre is saying that people have been thinking about what it means to exist and that they’ve been moving from sort of conflicting ideas of spirituality, or heaven and the reality of the world we live in towards the idea that existence is based simply on the experience. He used the word ‘monism’. We learned the term monism, it means not believing in the distinction between mind and matter, or God and the world. So, Sartre’s purpose is to describe this idea of monism. When an author is describing something, he will give main ideas and details.
(Put a big ol’ CHECK! on the self-monitoring sheet; Figure 2).
So, step 2 is to think about what I know about monism.”
Talk to the class about monism. Be sure to discuss vocabulary to be used and define it.
(Put another big ol’ CHECK! on the self-monitoring sheet)
SAY,” OK, step 3, I need to think about what I want to learn from this huge book.
(Discuss with the class some questions you have about existentialism. CHECK!)
OKAY! I’ve completed the first three steps of the think before reading part, and I’m ready to get my read on!”
Read the second paragraph at normal speed, then, speed up. Then SAY, “Holy Gucamole, this does NOT compute! Take a breath. I need to slow down. The TWA check sheet says I need to remind myself to slow down otherwise I won’t be able to understand what I’m reading.”
Discuss with students that taking healthy pauses at punctuation marks can help with going too fast.
Start reading again at a prudent speed and stop when you get to something that can help link to prior knowledge.
(model linking information)
Read again until you hit another spot you don’t understand.
SAY, “Goodness gracious, this is DEEP and confusing…
(model reading it again and checking the vocabulary journal for the definition of a word you don’t understand.)
SAY, “Duuuuuuude, I tots get it now. TWA is helping me understand this!”(Model reading the rest of the passage using these procedures, paying attention to vocab words.)
SAY, “Wow, I know a lot more about existentialism now and am totally questioning what it means to exist. Like, do other people really exist or are they just there because I am here to experience them? OK, what’s next? Think After reading… The first step is finding the main idea and supporting details.
(Present markers.)
These markers are gonna help me isolate main ideas and supporting details. I’m gonna do this in the first passage.”
(highlight main ideas in yellow. CHECK! Highlight supporting details in blue.)
“Right, what’s next? Strike out anything that’s not important.”
(Cross out with pencil. Model summarizing the information. CHECK!)
Repeat for each paragraph. Every once in a while, reassure yourself by saying things like, “Great Googlymoogly, this is taking forever! But the more I do it, the faster it’ll get,”
Every once in a while, SAY, “This is making it so I can retell what I’m reading. I’ve got all the good stuff highlighted!”
(Model retelling the paragraph. CHECK!)
Summary
Reading is a necessary skill to allow a person to become connected with society, particularly in out increasingly digital society. Recent efforts in improving the reading of all students (e.g., Every Student Succeeds Act, 2015) have unearthed many practices to improve reading skills. Particular attention has been applied to the evidence that many of the reading difficulties experienced by our students are a result of poor instruction, and not a disabling condition. Thus, the utilization of practices based on validated research practices has been mandated and those practices have begun to be identified. The use of the practices outlined in this chapter within a framework of data-based decision making (as outlined in Chapter 2) should help students access the curriculum and life. | textbooks/socialsci/Education_and_Professional_Development/Special_Education_in_Secondary_Schools_(Losinksi)/06%3A_Strategies_for_Improving_Student_Outcomes_in_Reading/6.05%3A_Section_5-.txt |
General Outline
• Issues Related to Writing
• Improving Written Expression with STOP + DARE
Much of the current chapter has been reproduced from Self-Regulated Strategy Development for Students with Emotional/Behavioral Disorders in a Residential School with permission from Robin P. Ennis (2013). While the text of the following includes refences specific to improving the writing of students with emotional or behavioral disorders (E/BD), the statements apply equally to all students with disabilities.
Writing is a complex activity requiring multiple cognitive processes (Graham & Harris, 2003). The National Assessment of Educational Progress writing assessment of 2007 found that fewer than 6% of students with disabilities in grades 8 and 12 demonstrated proficient writing skills (Institute of Education Sciences, 2007). Additionally, writing is required for most living-wage jobs with both public and private employers citing a need for writing proficiency for occupational success (National Commission on Writing, 2004). Students with difficulties in the area of writing have difficulty generating and organizing ideas, setting personal writing goals, self-monitoring written performance, and revising written work (Harris & Graham, 1996). One evidence-based intervention that addresses all of these difficulties is self-regulated strategy development (SRSD). (Ennis, 2013, pp. 44-48).
7.02: Section 2-
Self-Regulated Strategy Development
SRSD is designed to address difficulties with writing as well as attitudes, beliefs, and motivation related to the writing process. The SRSD model includes procedures for goal setting, self-monitoring, self-instruction, and selfreinforcement, and can be generalized to other settings and maintained over time once taught to mastery in whole- class, small group, or individual settings (Harris, Graham, Mason, & Friedlander, 2008). The six-stage SRSD model is well-aligned with interventions successful in improving the academic and behavioral skills of students with or at-risk for E/BD, as it incorporates self-monitoring and goal setting, strategies shown to be effective for students with E/BD (McDougall, 1998; Mooney, Ryan, Uhing, Reid, & Epstein, 2005).
Stage 1: Develop background knowledge. Stage 1 of SRSD includes developing preskills/background knowledge needed for the genre of writing being taught. Teachers lead student(s) through reading examples of the genre of writing and teach any related vocabulary (e.g., arguments and counterarguments in persuasive writing). During this stage, the teacher also introduces the skills of goal setting and self-monitoring (Harris et al., 2008).
Stage 2: Discuss it. Stage 2 includes discussing the benefits of being a good writer with particular focus on the genre being taught. The teacher discusses the benefits of using a strategy to have a systematic plan to use when writing. Then the teacher leads the students in examining their current writing performance with regard to the essential elements of the targeted genre of writing. This allows the students to self-monitor their progress over the course of the intervention. During this stage, the teacher introduces the mnemonic strategy to be used and helps students identify opportunities to use the strategy (Harris et al., 2008). These opportunities may include writing for other subject areas (i.e., science and social studies) using expository writing (Mason, Snyder, Sukhram, & Kedem, 2006) or self-advocating using persuasive writing (Cuenca-Sanchez, Mastropieri, Scruggs, & Kidd, 2012).
Stage 3: Model it. During Stage 3, the teacher uses the strategy by modeling self-talk while moving through the writing process. Modeling of self-talk, including self- instructions, self-questioning, and self-reinforcement, serves as a verbal demonstration of the process that skilled writers engage in internally. Selftalk models should address all skills in the writing process including: defining a problem, focusing attention, planning, strategy, and statements. The modeling of these behaviors should be natural and enthusiastic by the teacher. The metascripted SRSD lessons include modeling scripts to assist teachers in addressing all components while still allowing teachers to adapt the presentation to fit their teaching style and the needs of their students (e.g., Harris et al., 2008).
Stage 4: Memorize it. Stage 4 involves memorizing the mnemonic device to guide the student(s) through the entire writing process. Memorization also involves the student gaining a full understanding of the meaning of each step of the mnemonic. There are many mnemonics found in the SRSD literature. An example mnemonic for persuasive writing is STOP and DARE, which stands for Suspend judgment, Take a side, Organize ideas, Plan more as you write and Develop your topic sentence, Add supporting ideas, Reject an argument for the other side, End with a conclusion. An example mnemonic for narrative writing is POW + WWW What2 How2, which stands for Pick my idea, Organize my notes, Write and say more, Who is the main character? When does the story happen? Where does the story happen? What does the main character do? What happens then? How does the story end? How does the main character feel?
An example mnemonic for expository writing is TWA + PLANS, which stands for Think before reading, think While reading, think After reading and Pick goals, List ways to meet goals, And, make Notes, Sequence notes. Teachers may provide additional scaffolded supports and opportunities for practice to students having difficulty memorizing the mnemonic (Harris et al., 2008).
Stage 5: Support it. During Stage 5, teachers support student(s) in their use of the strategy during writing. Teachers support student(s) by providing assistance and reminders. This stage continues until the students are able to apply the strategies independently. During this stage, teachers lead students in generalizing the strategy to other settings and writing tasks to promote its maintained use over time. Stage 5 is essential for struggling writers, and may take longer for students who have weakness in the area of writing (Harris et al., 2008).
Stage 6: Independent performance. During Stage 6, student(s) should be using the strategy fully independently, thus self-regulating their own writing. At this time, student(s) who are engaging in self-talk orally (as observed by the model) are encouraged to self-talk in their heads as they utilize the mnemonic during the writing process. This stage also involves presenting student(s) with opportunities to generalize the strategy learned (e.g., using the mnemonic for writing in social studies) as well as presenting any needed booster sessions to promote maintenance of strategy use (Harris et al., 2008). (Ennis, 2013, pp. 44-48). | textbooks/socialsci/Education_and_Professional_Development/Special_Education_in_Secondary_Schools_(Losinksi)/07%3A_Strategies_Improving_Student_Outcomes_in_Writing/7.01%3A_Section_1-.txt |
Using STOP and DARE
One SRSD mnemonic for teaching persuasive writing that has not been widely investigated with students with E/BD is STOP and DARE. STOP and DARE is an ideal mnemonic for use for students with E/BD for several reasons. To begin, STOP and DARE mirrors language that is common in mindfulness or anger management training commonly used with students with E/BD (i.e., encouraging students to stop and think, developing possible solutions for both sides in an argument). In addition, STOP and DARE includes elements of persuasive writing, such as including a counterargument that is not a component of the POW+TREE mnemonic. This is essential given that in many states the high school level writing competency tests focus solely on persuasive writing. Further, with the move to common core standards in academic content areas, the mnemonic STOP and DARE includes essential elements required for writing an argument, which is a standard element of the common core. Finally, as with POW+TREE there is research to suggest that STOP and DARE is effective for students with learning disabilities (e.g., Kiuhara, O’Neill, Hawken, & Graham, 2012), suggesting that investigations are needed with students with E/BD (Ennis, 2013, pp. 51-52).
STOP + DARE Scripted Lesson
The following is adapted from lesson 2, the modeling exercise, of STOP + DARE, (Harris, Graham, Mason, & Friedlander, 2008).
Step 1 Present Cue Cards and Brainstorming Sheet (See Figure 11).
(Approximately 5 minutes)
(Give ‘teacher’ cue cards to eight-ish students. )
SAY, “You’ll take turns placing cue cards on the wall as you start each step.” Step 2. Model It! ( Approximately 20 minutes)
(Remember: You don’t have to copy what I say word-for-word, and it is important to be ENTHUSIASTIC!)
ESSAY TOPIC: Should DC give up on making live action movies given how awful they are compared to the Marvel Cinematic Universe (MCU)?
SAY, “I’m gonna model for you how to use the STOP and DARE method to write an essay. I’m going to talk out loud while I go so you can witness, hand, the rat’s nest that is the inside of my brain. Also, I’m going to show how I work out my essays using STOP + DARE. All you gotta do is sit back and watch magic at work!”
SAY, “First, I need to remember not to be too judge-y. Remember, ‘haters gonna hate’. So, I’m going to try and forget about the Green Lantern movie and brainstorm pros and cons of this question.”
(Hold up brainstorm sheet. Come up with two ideas for each side – pro/con).
SAY, “There are three cue cards for Step 1, that basically say, ‘Judge not, lest ye be Judge Dredd (the Stallone one)’. Card 1 says, ‘Did I list ideas for both sides? If not, do this?’ YES! I did it! This is so freaking easy! OK, what’s card 2 got for me? ‘Can I think of anything else? Try to write more.’ Right, come up with more juicy goodness”
(Add another idea or two to each side of the brainstorming sheet. Let students help.)
SAY, “Card 3 says, ‘Another point I haven’t yet considered is… Think of possible arguments.’ Can I argue? Yes I can! Arguing is, like, totally something I’m good at!” (Pause)
SAY, “Is there anything I haven’t thought of? I’ve got so much already, what more could there possibly be? OK, need to chill and think of something, something a fanboy would say.”
(Add something, preferably a ‘pro’.)
SAY, “SWEET! Step 1 is done, and this is fun! Now, I gots to move on down the line to step number two. Only one card… #4. Says, ‘Take a side.’ So, I pick a side. Which side, which side, which side? Duh, they should stop! Mr. Cue-Card says, ‘Place a “+” at the top of one box to show the side you will take in your essay.’ I should be able to remember this, cause it’s on the brainstorming sheet… OK, Step 3… ‘Organize Ideas.’ I need to figure out which ideas are solid, and which ones have holes in them... So, let’s examine these ideas...”
(Read the pros and decide if they’re any good. Find at least one that isn’t and decide to skip it.)
SAY, “OK, all of my stuff is solid. So, what can I argue? OK, so I need to find something I can easily poke holes in.”
(Pick something from the con side of the brainstorming sheet and come up with one more con.)
SAY, “OK, gotta choose something good… It’s gotta be something that makes it crystal clear why DC should throw in the towel… I’m rocking on this thing! My ideas RULE! OK, Let’s look at the cards for step 3… Card five says, ‘Put a star next to ideas you want to use.’ OK, rule of three… Pick three arguments I wanna use... “
(place three stars next to ideas you like).
SAY, “What’s card six say?, ‘Did I star ideas on both sides? Choose at least ___ argument(s) that you can dispute.’ OK, I’ve figured out two arguments...
(place stars next to them).
SAY, “Card seven, says, ‘Number your ideas in the order you will use them.’ OK, let’s think about this... How should I order things? I heard I should always put the weakest one in the middle, and finish with the best… But I could also work it like a map and do them in some type of order so…”
(Go through a thought process on coming up with the best order).
SAY, “This is gonna be awesome! Planning makes perfect… OK, last step, ‘Plan more as you write. Remember to use all four essay parts and continue planning. OK, I need to remember to not shut my brain off while I’m working… OK, step 4… Moving on to DARE… I remember this.”
(Read the card, ‘Develop your topic sentence. Add supporting ideas. Reject possible arguments. End with a conclusion.’)
SAY, “OK, let’s get to it! Gotta think of DARE while I’m writing… So, in the next lesson, we’ll work on writing the essay.”
This is followed by a guided reflection and practice.
Brainstorming Sheet
Suspend Judgment. Brainstorm ideas for and against the topic.
For (Pro) Against (Con)
1. Batman v. Superman was not good. AT ALL. Too many story lines, Darkseid looked stupid. Batman was played by Ben Affleck. 1. Marvel didn’t always make good movies (see Daredevil with Ben Affleck, or rather, don’t!), so there’s the possibility of turning it around…
2. Green Lantern was terrible. Just terrible. 2. Man of Steel wasn’t terrible, and set up the DCU for something good.
3. Trying to include Cyborg is a very, very, very bad idea. It just looked so incredibly cheesy in the brief part of Batman v. Superman…. He wasn’t in all of the Justice Leagues so, they should have let it go. But they didn’t. 3. The casting of Aquaman is actually pretty awesome.
4. Suicide Squad was pretty darn bad. Outside of the good casting, the story was just plain not developed and stupid. And Jared Leto’s Joker was ridiculously BAD. In a bad way, not a good way.
Take a Side. Place a “+” at the top of the box that shows the side you will take.
Organize Ideas. Decide which ideas are strong and which ideas you can dispute.
Plan More as You Write. Remember to use all four essay parts and continue planning.
Now write your essay on another piece of paper.
Summary
Writing is a critical aspect of schooling, and one that students are continuously unprepared for. Indeed, with the proliferation of social media and texting, even “educated” students are finding it difficult to use key skills when necessary due to the continued use of slang, improper grammar, and limited/improper use of punctuation. Additionally, we have witnessed a reliance on technology to aide in the spelling and grammar of written materials, though technology can only do so much. Take for instance the increasing number of students in college level classes who interchangeably use the words their, there, and they’re. STOP+DARE won’t solve many of these issues, but research has continually shown that it will increase the student’s ability to organize their thoughts into cogent text. | textbooks/socialsci/Education_and_Professional_Development/Special_Education_in_Secondary_Schools_(Losinksi)/07%3A_Strategies_Improving_Student_Outcomes_in_Writing/7.03%3A_Section_3-.txt |
General Outline
• ematics
• SOLVE-IT
• Algebra
Alarge percentage of the population, between 5% and 9%, experience mathematics disabilities which presents chronic challenges (Fuchs, et al., 2011). Mathematics achievement is vital to attaining post-secondary trajectories including entrance into college and meaningful careers (Bryant, Bryant, Williams, Kim, & Shin, 2013; Geary, 2013). Early intervention is therefore critical, though no one intervention has been shown to be effective for all students. For example, a study by Fuchs and colleagues (2005) showed that early intervention in first grade provided significant reductions in math difficulties that persisted over the following year, however 3 to 6% of the population continued to have math related deficits (Fuchs, et al., 2005). Current emphases on real-world applications such as those posited in the common core state standards (CCSS) has drawn increased attention to word problem solving skills. As Fuchs et al. (2011) describe, there is a distinction between arithmetic and word problem skills. Arithmetic refers to “computations problems (e.g., 5 + 6 = 11; 12 – 5 = 7) that cannot be solved via algorithms,” (Fuchs, et al., 2011, p. 434). Word problems require the student to be able to read and comprehend text to identify information to be used in calculatinga problem, thus it requires different skills at the outset.
8.02: Section 2-
Research has suggested that under achievement in mathematics of students with disabilities can be traced to a lack of foundational knowledge (Bryant, Bryant, Williams, Kim, & Shin, 2013). Specifically, students should have achieved automaticity of addition and subtraction by the end of the third grade and multiplication and division by the end of the fifth grade. Lower fluency with the skills results in increased use of working memory to accomplish these constructs when trying to solve problems in later math classes. At present, there is no reliable and valid measure that is universally accepted to describe math learning disability (MLD; Geary, 2013) as opposed to simply low achievement. Part of this may be a result of math having different semi-unrelated facets (e.g., number sense, geometry) as compared to reading which is based on a more easily represented learning trajectory (letter recognition –> sound recognition –> phoneme recognition etc.). What is known is that between 57 and 64% of individuals with MLD also have a reading disability (Bararesi, Katusic, Colligan, Weaver, & Jacobsen, 2005) which suggests that the same environmental genetic factors may be at work in both disabilities (Geary, 2013). Research about MLDs is generally focused on three areas: (a) numbers, (b) accounting, and (c) arithmetic with little attention paid to spatial mathematics (e.g., geometry) and statistics. However this is likely to change with growing attention paid to these areas in schools to improve college and career readiness.
Therefore, when we speak of improving student mathematics outcomes, the Institute of Education Sciences’ (IES) practice guide on teaching strategies for improving algebra, suggested a focus on developing deeper understanding of algebra, emphasizing process over outcomes, and encouraging precise math language (Star, et al., 2015). In light of these suggestions, and the current focus on “real world applications” the remainder of this chapter will be devoted to strategies designed to improve the mathematics of students with disabilities in the secondary schools. First, we will discuss a strategy for improving story problem outcomes, Solve-It! (Montague, 2010), followed by recommendations from the IES practice guide. | textbooks/socialsci/Education_and_Professional_Development/Special_Education_in_Secondary_Schools_(Losinksi)/08%3A_Strategies_for_Improving_Student_Outcomes_in_Math/8.01%3A_Section_1-.txt |
Solve It! is a cognitive strategy instruction that works on the framework that successful problem-solving in mathematics is predicated upon a person’s ability to select and utilize appropriate strategies for understanding and solving problems (Montague, 2010). Solve It! teaches students to solve math problems through a seven-step, explicit instruction approach wherein they:
1. read for understanding,
2. paraphrase the problem,
3. visualize the problem,
4. hypothesize the process for solving the problem,
5. estimate the answer,
6. compute the problem, and
7. check the answer.
As with any strategy I would recommend, data should always be the primary indicator of whether or not you should use, or continue to use, the program. Therefore, it is important that frequent assessments are utilized. The Solve It! manual provides all of the materials necessary to get started with implementing this program including practice sheets. Therefore, we are going to provide a quick outline of the strategies utilizing an adapted version of the cognitive processes and self-regulation strategies and first lesson.
Cognitive Processes and Self-Regulation Strategies
Read (for understanding)
Say: Read the problem. If I don’t understand, read it again.
Ask: Have I read and understood the problem?
Check: For understanding as I solve the problem.
Paraphrase (your own words)
Say: Underline the important information. Put the problem in my own words.
Ask: Have I underlined the important information? What is the question? What am I looking for?
Check: That the information goes with the question.
Visualize (a picture or a diagram)
Say: Make a drawing or a diagram.
Ask: Does the picture fit the problem?
Check: The picture against the problem information.
Hypothesize (a plan to solve the problem)
Say: Decide how many steps and operations are needed. Write the operations symbols (+, -, x, and /).
Ask: If I do___, what will I get? If I do____, then what do I need to do next? How many steps are needed?
Check: That the plan makes sense. Estimate (predict the answer)
Say: Round the numbers, do the problem in my head, and right the estimate.
Ask: Did I round up or down? Did I write the estimate?
Check: That I used the important information.
Compute (do the arithmetic)
Say: Do the operations of the right order.
Ask: How does my answer compare with my estimate? Does my answer makes sense? Are the decimals or money signs in the right places?
Check: That all the operations were done in the right order (Montague, 2010, pp. 150-151).
Lesson 1: Introduction, a Play in One Act (adapted from Montague, 2010).
Prep. Make folders with a graph for student scores and room for all work. Make class charts for either transparencies or in a PowerPoint, also post them on the wall. Make cue cards out of index cards.
(A crowded classroom in a small town in Kansas. Mr. Losinski is at the head of the class getting ready to teach students all about Solve It! Kids in the class include, Timmy, Levi, and Willow.)
LOSINSKI
Alright, everyone sit down.
Alright, thank you. So... for the next two weeks I’m gonna be teaching you guys a strategy to help figure out working out word problems. Y’all haven’t been doing a great job with them, so... I figure we’ll try this new thing, Solve It! I know y’all don’t like math, but it’s something you need to learn. So, one of you tell me why you want to improve your math?
(no one responds)
Come on, somebody’s got to tell me something. Willow?
(she doesn’t look up, but shakes her head).
Thank you for responding to me calling your name, Willow. Alright, Timmy why do you want to learn math?
TIMMY
I don’t.
LOSINSKI
Okay. Pretend that you do and tell me why you would want to learn.
TIMMY
Uh. So, I can figure out how many pieces of pizza to cut when someone orders one?
Losinski jots this down on the board
LOSINSKI
OK. Sure, cutting pizza takes an understanding of fractions. And sometimes when people order pizza they are gonna tell you all kinds of stuff they want and how much pepperoni to put on 1/3 of it, and then 1/3 with pineapple, etc. Now, Y’all have decent math skills, but again we are going to transfer the skills you already have over to working out these word problems.
(Losinski hands out folders)
Alright, let’s look at the tests you’ve taken. Right now, I want to discuss the graph and what a baseline score is. If you look at the first dot, that’s how you did on the first test, how many correct out of 10 you got. Some of you guys did alright. Some didn’t, but we want everybody to do good on all the problems, all the time. So, for a goal, let’s say we want everybody to get seven problems correct out of 10 on each of the measures for the rest of time. I’m pretty confident that if you guys apply yourselves, you’ll be able to do that.
(To Levi)
Any questions? Levi, got anything to ask?
LEVI
Nope.
LOSINSKI
How many people like doing word problems?
(nobody raises hand)
Alright. I get this, most people don’t. But I think it may be because they haven’t been successful at it. If you become a better story problem solver, I think you might change your mind. How do you feel about that Willow?
WILLOW
Bad.
LOSINSKI
Thank you for answering me, Willow.
WILLOW
Whatever.
LOSINSKI
I appreciate you answering again, Willow.
WILLOW
Can we move on please?
LOSINSKI
Yup. So, everyone, what is our goal?
EVERYONE
Seven.
LOSINSKI
Perfect.
(collects folders)
Alright. First we’re to work on the seven part strategy for Solve It! we’re going to practice the strategy, then take a test, practice a little bit more, take another test... These aren’t really tests, because they’re not going to count for your grade, they’re only to see our improvement. That’s all we’re doing today. Then, the next couple of weeks we’re going to keep doing these tests and track our progress in our folders. Does anyone have any questions? Smashing! Let’s get started.
(pause)
OK, some people who do good on story problems do a lot of stuff in their heads when they solve these problems. These are called metacognitive processes. Someone raise your hand if you know what a process is.
Timmy raises hand
WILLOW
Read for understanding.
LOSINSKI
Very good. willow. Next, they paraphrase the problem in their own words. What do they do next?
CLASS
Paraphrase.
LOSINSKI
That’s right paraphrase. Levi, what do they do next?
LEVI
Parasail.
LOSINSKI
Paraphrase.
LEVI
Oh, right... phrase...
LOSINSKI
Uh-huh. what does paraphrase mean?
TIMMY
Is it like parasailing? My mom went parasailing once. Said it was awesome.
LOSINSKI
No. It is not even a little like parasailing, Timmy. It is shortening a long passage into it’s main parts in your own words. What is paraphrasing?
CLASS
Shortening stuff in your own words.
LOSINSKI
Shortening stuff in your own words that’s right. Timmy what is paraphrasing?
TIMMY
Making stuff shorter in your own words.
LOSINSKI
That’s right, good job, Timmy. Next, visualizing. They use objects in some kind of picture or diagram on paper or in their head. What is visualizing?
CLASS
Making a picture in their head.
LOSINSKI
That’s right, making a picture in their head. Willow, what is visualizing?
WILLOW
Imagining I am not in this class.
LOSINSKI
Very good, Willow that is a form of visualizing. Not of a math problem, but still visualizing. Next, they hypothesize. Anybody know what: to hypothesize is?
TIMMY
Is that like those lotions they make so you don’t have to take Benadryl?
LOSINSKI
That is hypoallergenic. Not hypothesize. Anyone else? A hypothesis is an educated guess. What’s a hypothesis?
CLASS
An educated guess.
LOSINSKI
That’s right, an educated guess. Levi what is a hypothesis?
LEVI
Educated guess. Like, this class is never gonna end.
LOSINSKI
Very good. An educated guess. So, then people estimate the answer. Raise your hand if you know what an estimate means...
(crickets)
Estimating means making a prediction...
WILLOW
Isn’t that the same thing as a hypothesis?
LOSINSKI
Essentially, yes. However, the hypothesis in this case it’s more about establishing a plan to solve the problem, where as the estimation is our guess at an answer.
WILLOW
That’s not really what hypothesis means.
LOSINSKI
I appreciate that you understand the semantic lack of differences between hypothesis and estimation, Willow. However, I think we can move on... People tend to estimate the answer before they even start doing math. Then they do the math get an answer and compare it. So after they estimate they compute, which means doing the math. What does compute mean?
CLASS
Doing the math.
LOSINSKI
Doing the math. That’s right. Levi, what is computing?
LEVI
Getting my math on.
LOSINSKI
That’s right, Levi. Last, good word problem people check their work. Means checking to make sure That they’ve the right calculations, they have set up their problem right. Sometimes they use reverse operations. so like using subtraction to figure out an addition problem. Why do you check math word problems? To make sure you get it right.
(switch to: say, ask, check)
So, good problem solvers also do stuff in their head. First thing he does is ask himself what to do...
WILLOW
Why does it have to be a guy?
LOSINSKI
It doesn’t. Thank you for checking me on gender micro-aggressions, Willow. So, the first thing they do is SAY things to tell them what to do. Next, They ASK themselves questions. Finally, they CHECK their work. I put Say, ask, check On These charts. Show metacognitive strategy chart. I also have these cards that’ll help you study. This big chart so you can See what to do. And now I’m going to go through the whole process once.Then, we will read it as a group. Finally, I’ll call on each of you to read it. Perform the explanations as described by Losinski. | textbooks/socialsci/Education_and_Professional_Development/Special_Education_in_Secondary_Schools_(Losinksi)/08%3A_Strategies_for_Improving_Student_Outcomes_in_Math/8.03%3A_Section_3-.txt |
The IES practice guide Teaching Strategies for Improving Algebra Knowledge in Middle and High School Students (Star, et al., 2015) provides three broad recommendations for improving the algebra skills of students. The three strategies include using solved prolems to engage learners, teach students to use the structure of equations, and teach students to intentionally choose specific strategies to solve problems. The following is a brief outline of the concepts and ways to implement them in your classroom.
Recommendation 1: Use Solved Problems to Engage Learners
The IES practice guide (Star, et al., 2015) suggests teachers should encourage the use of solved problems to engage learners in understanding algebraic logic and approaches. The practice guide provides evidence from four studies with adequate methodological quality to base the recommendation on. The rating of minimal evidence is based on the inability to generalize the findings to larger populations due to small sample sizes, and one of the studies finding negative outcomes when compared to the strategy in recommendation 2. Essentially, that it is better than normal activities, but not as great as teaching students to utilize the structure of equations. Obviously, utilizing all three recommendations in conjunction would be preferable.
Within this recommendation, teachers should have students discuss solved problems and how those solved problems are structured in whole group, small group, and individually. To that end, teachers should choose solved problems that directly reflect the lesson for the day or unit. The selection of non-examples would also be beneficial to illustrate common mistakes made in solving the specific problem type. The following is a sample solved problem.
Solve for x:
5^(2x+3)=25
5^(2x+3)=5^2
2x+3=2
2x=-1
x=-1/2
Discuss Solved Problems and Their Structure
The following are questions to facilitate discussion of solved problems.
• What were the steps involved in solving the problem? Why do they work in this order? Would they work in a different order?
• Could the problem have been solved with fewer steps?
• Can anyone think of a different way to solve this problem?
• Will this strategy always work? Why?
• What are other problems for which this strategy will work?
• How can you change the given problem so that this strategy does not work?
• How can you modify the solution to make it clearer to others?
• What other mathematical ideas connect to this solution? (Star, et al., 2015, p. 5).
These questions will allow discussion of the structure of the problems:
• What quantities—including numbers and variables—are present in this problem?
• Are these quantities discrete or continuous?
• What operations and relationships among quantities does the problem involve? Are there multiplicative or additive relationships? Does the problem include equality or inequality?
• How are parentheses used in the problem to indicate the problem’s structure? (Star, et al., 2015, p. 6).
Pick Problems That Reflect the Lesson Goal
The IES practice guide suggests using
problems that mirror the goal of the current lesson.
• Select problems with varying levels of difficulty and arrange them from simplest to most complex applications of the same concept.
• Display the multiple examples simultaneously to encourage students to recognize problems.
• Alternatively, show the problems individually, one after the other, to facilitate more detailed discussion on each problem (Star, et al., 2015, p. 6).
The following is a description of introducing and discussing incorrect and correct problem solving:
Correct solved problem: x^2-4x-45 = (x-9)(x+5)
Incorrect #1: Student did not factor correctly: x^2-4x-45 = (x - 40)(x + 5)
Incorrect #2: Student did not factor correctly: x^2-4x-45 = (x + 9)(x - 5)
Questions to lead discussion
1. How can you show that the answers from students B and C are incorrect?
2. What advice would you give to students B and C to help them avoid factoring this type of problem incorrectly in the future?
3. How can you check that student A factored this expression correctly?
4. What strategy would you use to factor this expression and why did you choose that strategy? (Star, et al., 2015, p. 10).
Common issues and solutions
Issue 1. I already use solved problems, but students aren’t engaged.
Suggestion. Keep doing it! Modeling solving problems during whole-class instruction with think-alouds.
Issue 2. I don’t know how to find solved problems and am too lazy to make my own.
Suggestion. Curriculum materials and textbooks often have these. You could also use student work on homework.
Issue 3. Won’t incorrect problems confuse them?
Suggestion. No. Using correct and incorrect problems will help students understand the common errors made when solving problems.
Recommendation #2: Use the Structure of Equations
According to the WWC’s practice guide (Star, et al., 2015), the structure of the equations refers to the number, type, and position of quantities, including variables, operations, existence of equality or inequality, and simpler expressions nested inside more complex ones. For example, the structure of the following three equations is the same:
5x+19=59
5(x+1)+19=59
5(3x -22)+19=59
The underlying structure is 5 times an unknown number (x) or (x+1) or (3x-22), plus 19 equals 59. In their review of this process, the WWC reviewers once again found minimal evidence for the strategy, with four studies meeting standards without reservations and two met standards with reservations. Once again, though, the finding of minimal evidence should be viewed in light of the fact that this is not suggesting it does not work, only that there arent enough quality studies out there to allow us to generalize to a larger population.
One of the more common ineffective practices for teachers and parents alike is the use of imprecise language. Indeed, providing effective commands (defined as explicit and specific commands) is an evidence-based practice for improving student compliance (Losinski, Sanders, Katsiyannis, & Wiseman, in press). For example, Mr. Zeller saying, “everyone get your materials out”, is not considered an effective command. In this case, he should say, “students, please place your math textbook and a pencil on your desk”. The specificity of the command reduces any chance of miscommunication. The same is true for providing precise language in mathematics instruction. The following describes the use of precise language.
Imprecise vs. precise mathematical language
Imprecise language Precise mathematical language
Take out the x.
Factor x from the expression.
Divide both sides of the equation by x, with a caution about the possibility of dividing by 0.
Move the 5 over. Subtract 5 from both sides of the equation.
Use the rainbow method.
Use FOIL.
Use the distributive property.
Solve an expression.
Solve an equation.
Rewrite an expression.
A is apples.
Let a represent the number of apples.
Let a represent the cost of the apples in dollars.
Let a represent the weight of the apples in pounds.
Plug in the 2. Substitute 2 for x.
To simplify, flip it and multiply. To simplify, multiply both sides by the reciprocal.
To divide a fraction, invert and multiply. To divide fractions, multiply by the reciprocal.
Do the opposite to each side.
Use inverse operations.
Add the opposite to each side.
The numbers cancel out.
The numbers add to zero.
The numbers divide to one.
Plug it into the expression. Evaluate the expression.
Use reflexive questioning
One of the key suggestions the authors use is having students utilize reflexive questioning. This involves asking themselves questions that uncovers the structure of the problem: The following are examples of reflexive questions:
• What am I being asked to do in this problem?
• How would I describe this problem using precise mathematical language?
• Is this problem structured similarly to another problem I’ve seen before?
• How many variables are there?
• What am I trying to solve for?
• What are the relationships between the quantities in this expression or equation?
• How will the placement of the quantities and the operations impact what I do first? (Star, et al., 2015, p. 20)
Using diagrams to denote the underlying structure
The following is an example of using a diagram to identify the structure of a problem. Students are asked to compare each.
Question: Compare a diagram and an equation to represent Timmy’s total online gaming costs per month if Timmy has a fixed/starting cost (f) of \$50 plus a game cost (g) of \$4.50 for every game. Timmy used 5 games last month. What was his total gaming cost (T)?
Equation (where n = the number of games used).
T = f + ng
T = 50 + 5(4.50)
T = \$72.50
Common issues and solutions
Issue 1. Teachers enjoy simplifying language, and students like it.
Suggestion. Imprecise language may cloud student understanding during standardized assessments. Precise language should not be treated as more complicated, but more mathematically accurate. Precise language promotes the use of common language across contexts.
Issue 2. Students rush through problems.
Suggestion. This could be due to two problems: First, problems may be too easy, and students can motor through them without much thought. If this is the case, offer problems that are similar but look different. Second, students may be using strategies they know well, by may not be correct. Assign students reflexive questions to develop understanding and use of varied strategies.
Issue 3. Students don’t use the diagrams
Suggestion. Some students will get to the answer without them, however using diagrams can bring the underlying structure to light. Thus, teachers should encourage the use of diagrams to help students learn the structure.
Recommendation #3: Intentionally Choose Specific Strategies
The WWC practice guide (Star, et al., 2015) suggests teaching students a variety of strategies, though it doesn’t stress that students need to be fluent in all of them. Six studies met WWC group design standards without reservations. Four of the six showed positive effects of teaching alternative strategies and two found negative or mixed effects. This resulted in the classification of this strategy as one with moderate evidence. Within this domain, it is recommended that teachers instruct students to recognize and choose strategies to solve specific problems. According to the Star and colleagues,
Provide students with examples that illustrate the use of multiple algebraic strategies. Include standard strategies that students commonly use, as well as alternative strategies that may be less obvious. Students can observe that strategies vary in their effectiveness and efficiency for solving a problem (Star, et al., 2015, p. 27).
The following is an example of using different strategies to solve problems.
Conventional method Alternative method
Question 3a + 9b – 7a + 2b – 8a (if a = 6 and b = 8)
3a + 9b – 7a + 2b – 8a
3(6) + 9(8) – 7(6) + 2(8) – 8(6)
18 + 72 – 42 + 16 - 48
16
3a + 9b – 7a + 2b – 8a
–12a + 11b
–12(6) + 11(8)
-72 + 88
16
Levi’s restaurant bill, including tax, but before tip, was \$23.00. If he wanted to leave a 12.5% tip, how much money should he leave in total?
23.00 * 1.125 = x
x = \$25.86
10% of \$23.00 is \$2.30, and one quarter of \$2.30 is \$0.56, which totals \$2.86, so the total bill with tip would be \$23.00 + \$2.86 or \$25.86.
Solve for x: 5(x + 1) = 25
5x + 5 = 25
5x = 20
x = 4
X + 1 = 5
X = 4
Solve for x: 8(x – 5) = 2(x – 5) + 12
8x – 40 = 2x – 10 + 12
8x – 40 = 2x + 2
6x – 40 = 2
6x = 42
x = 7
8(x – 5) = 2(x - 5) + 12
6(x – 5) = 12
x - 5 = 2
x = 7
Solve for x: 3(x – 5) + 3x + 12 = 2(4x + 1) + 3x + 10
3x – 15 + 3x + 12 = 8x + 2 + 3x + 10
6x – 3 = 11x + 12
-5x = 15
x = -3
3(x – 5) + 3x + 12 = 2(4x + 1) + 3x + 10
3(x – 5) + 3x + 2 = 2(4x +1) + 3x
3x – 15 + 3x + 2 = 8x + 2 + 3x
6x – 13 = 11x + 2
-5x = 15
x = -3
Common issues and solutions
Issue 1. Whenever I teach multiple strategies, kids get confused.
Suggestion. You’re right, it gets confusing. Start with one until they have mastered it, then present a second to show a different way of solving the problem. Let them practice with it, then they will be able to choose the one they feel more comfortable with.
Issue 2. Our textbook only covers one strategy, what am I supposed to do?
Suggestion. Professional development? Google? What Works Clearinghouse? | textbooks/socialsci/Education_and_Professional_Development/Special_Education_in_Secondary_Schools_(Losinksi)/08%3A_Strategies_for_Improving_Student_Outcomes_in_Math/8.04%3A_Section_4-.txt |
ON THE NATURE AND VALUE OF SOCIAL SOFTWARE FOR LEARNING
In this chapter we define what social software is, and present a list of ways that it can be of use to learners, describing some of the potentially valuable functions and features that are available in these systems. The chapter is intended to establish a common understanding and vocabulary that provides a background to issues explored in greater depth throughout the rest of the book.
Why Learn Online with Other People?
The first reason to learn online with others is opportunity: what Stuart Kauffman (2000) calls the “adjacent possible.” New technologies offer such an opportunity. There are more networked devices than people in the world, with around one-third of the world’s population (2.26 billion people as of 2011) having access to the Internet, a figure projected to rise to around 40% by 2016 (Broadband Commission, 2012, p. 44). In Europe, over 60% of the population has regular access to the Internet, in North America, over 78% (Internet World Stats, 2012). In some countries, nearly the entire population has regular, personal Internet access. The digital traces this population leaves are vast. Google alone indexes over 30 trillion Web pages (Koetsier, 2013), which does not include countless others that are not indexed or contain dynamic, ever-changing content. The International Telecommunication Union (2012) reports that there were over 6 billion cellphone subscriptions worldwide by the end of 2011. Of those, over 30% (and rising) sold are smartphones, capable of connecting to the Internet. Nevertheless, there remain massive inequalities and barriers: only 24% of people in developing nations currently have Internet access and the number of countries that censor or prohibit the use of the Internet is rising. However, it is not unreasonable to suppose that, before very long, nearly every human on the planet may be able to connect with nearly every other in order to share information, knowledge, and ideas in a myriad of ways, virtually instantaneously. In our pockets we carry devices that can connect us not only to billions of living people but also with the digital traces they have left and the things they have shared, and with much of the accumulated knowledge of our forebears. Not only can we connect with people and their products but we can also connect with their aggregates—groups, organizations, companies, institutions, networks, communities, nations, and cultures. Social technologies for learning, from email to learning management systems, are ubiquitous in our schools and colleges.
The second reason for learning online with others is that, with every connection, direct and indirect, comes the opportunity to learn, and learning happens in many of these interactions. Almost every search on Google, visit to a page on Wikipedia or a how-to site is an act of intentional learning, one that is only possible because many people have, intentionally or otherwise, acted on our behalf as teachers. Meanwhile, a vast amount of intentional and unintentional learning is facilitated every day through posts on Twitter, Facebook, YouTube, LinkedIn, Pinterest, and countless other services. Smartphones and dumbphones (basic phones) are increasingly used more as information-finding devices than as simple communication tools. Large-scale courses and tutorials, often clumped together under the label of MOOCs (massive open online courses) are gathering millions of learners, eager and willing to learn.
Learning with Technologies in Crowds
In prehistoric times, knowledge spread through time and space by word of mouth and through example, stories and songs, apprenticeships, direct engagement, copying and observing others. The temporal and physical space between the original knowledge creator and knowledge constructor was sometimes very great, but the learner and teacher were physically and temporally adjacent. This is, of course, an oversimplification, even if we conveniently ignore things like cave paintings and other representations of knowledge such as sculpture and jewellery available to our ancestors. From the time we first started shaping tools, clothing, dwellings, and weapons, we have offloaded some of our cognitive processes into the spaces around us and shared in the intelligence of others as a result. In some cases, such as the carefully aligned stones of Stonehenge or cuneiform impressions in clay, the cognitive element of the artifacts we create is obvious: these are technologies at least partly intended to embody and enable thinking, though they may serve other functions as well. In the case of Stonehenge, the stones’ alignment enabled prediction and calculation of solstices and other significant temporal events. Cuneiform impressions served many purposes that extended our cognition, including as an adjunct to memory, a means to record and manipulate numbers, and as a way of sharing our knowledge with those not occupying the same time and space. However, even the haft of a spear or the pressed clay of a drinking bowl makes a tool that we think with, a shared object of cognition from which our learning and thinking cannot be glibly separated (Saloman, 1993). These are shared objects that are innately social: they do not just perform tasks for individuals, but carry shared meanings, communicable purposes, and the memories of those who created, refined, and developed them over time. As S. Johnson (2012) observes about the skill of the pilot in a modern airplane, the pilot's success is only possible through a “duet” with the thousands of people whose learning is embodied in the systems, devices, and methods used to both create and sustain the aircraft.
Historically, learning was nearly always with and from a crowd: methods, tools, customs, dances, music and stories, whether prototypical or fully formed, all played a role in establishing a collective, learned culture. While the transmission of knowledge could be, and perhaps often was a one-to-one exchange, the innate physics of dance, music, and speech made much cultural transmission a crowd phenomenon, a sharable and shared performance.
In the past, written words conveyed and shared our insights and ideas beyond co-located groups, separate in both space and time. Writing is a technology that allows one individual to directly address another, whether separated by thousands of years, thousands of miles, or both. Artifacts like paintings and sculptures provide further examples of this mode of engagement, communicating facts, beliefs, and emotions over time and space. Similarly, once the skills of creating and reading have been mastered, writing seemingly requires no further interpretation or context to complete the connection between learner and teacher, though our familiarity belies much of the vast complexity of mastering the tools and sharing meaning in the most intricate and subtle of technologies. Writing is, in a sense, a one-to-one technology that may be replicated many times, the same one communicating with many other individuals, one at a time. Rarely, save in some limited contexts such as inscriptions on statues, shop signs, scoreboards at football games, or sacred texts read aloud in public gatherings, is writing a one-to-many technology like speech. Writing is ostensibly direct, a communication channel between writer and reader that seems unmediated and undistorted by the intercession of others. It thus serves to contract time and space. Even today, when writing is a medium that may be shared with billions of others both now and in the indeterminate future, it shares this interesting characteristic: it is at once the epitome of social technology and the most private of engagements since the reader is potentially unknown to the writer, and his or her context may be entirely different from that of the writer’s.
The invention of printing changed the scale of this imbalance between the one and the many. Publication for the masses—without the need for an intermediary interpreter, or a creator of glosses—separated the writer (content creator) and the crowd almost entirely. This process continued in the nineteenth and twentieth centuries, which saw the emergence of mass, instantaneous, and global communications: sound and video recording, radio and television broadcasting, and a host of accompanying technologies and infrastructures combined with evermore powerful tools for printing, and the dissemination of printed materials made one-to-many communication the predominant form of knowledge distribution. Though social in some important ways, this development made possible mass educational processes that were in many other ways asocial. Alongside that, first the telegraph and fax and later the telephone and mobile phone made it simple to engage in near instantaneous one-to-one communication across vast distances almost as easily as local conversations. A many-to-many gap had been created.
The Rise of Cyberspace
In recent decades we have witnessed the increasing convergence of all forms of communication, publication, and information-sharing onto networked digital platforms—mainly the Internet but also cellular networks, digital TV, gaming networks, satellite communication systems, personal area networks, and other networked digital media. Collectively, to emphasize that we are not always simply talking about the Internet, we will refer to this connected set of tools and the interactions they enable as “cyberspace,” a term first coined by William Gibson (1984). Cyberspace may mimic other media, but it always carries with it far greater potential for two-or-more-way communication. In addition, its digital character makes the possibility of precise replication a simple task that, as often as not, needs little or no thought or effort to achieve. Even when there is no intention or facility for dialogue, the protocols and standards that underpin computer networking systems are seething with internal and hidden dialogues, exchanges, caches, and buffers that replicate and communicate between the devices we attach to our networks. Earlier forms of learning and teaching tools still exist but, increasingly, they are formatted first for cyberspace, and then placed in a secondary medium such as textbooks, classrooms, DVDs, or broadcast television.
This shift of both communication and content to cyberspace has profound implications for both lifelong learning and the formal education produced by our schools and universities. Clay Shirky (2008), in his insightful analysis of major communication innovations in history notes that cyberspace encompasses all previous innovations (print, video, radio, cinema, etc.) and supports one-to-one, one-to-many, and many-to-many communications at the same time, using the same low-cost tools. Beyond what is practical or possible in conventional human interaction, cyberspace supports dynamic collective knowledge generation. Our activities in cyberspace create traces and artifacts that, when aggregated, allow us to better understand the activities, ideas, and the nature of other individuals, along with the societies and communities they belong to; these activities can also provide novel insights into our own behaviors and interests.
All of these capabilities create new and very exciting opportunities for formal and informal learning. However, McCarthy, Miller, and Skidmore have argued that these “networks are the language of our times, but our institutions are not programmed to understand them” (2004, p. 11) . One major purpose of this book, therefore, is to explore these opportunities and provide both understanding and keys to action that can be used by educators and, as importantly, by learners.
As McLuhan (1994) and many others have observed, there is a rich interplay between the medium and the message it conveys. The media utilized by educators have very profound effects on the content taught, the organization of the learning process, and the range of available learning activities. The convergence of media in cyberspace has radically altered the conditions for teaching and learning, causing some to complain about the mismatch between the skills needed to operate effectively in a net-infused society, and the skills developed and information created in most of our industrial age schools and universities (Oliver, 2008). As W. Richardson notes,
in an environment where it’s easy to publish to the globe, it feels more and more hollow to ask students to “hand in” their homework to an audience of one . . . when many of our students are already building networks far beyond our classroom walls, forming communities around their passions and their talents, it’s not hard to understand why rows of desks and time-constrained schedules and standardized tests are feeling more and more limiting and ineffective. (2006, p. 36)
Defining Social Structure
The bulk of the applications introduced and discussed in this book can be classified as social learning technologies. The “social” attribute comes from the fact that they acquire their value when used by two or more people. Many of these tools are used to support sharing, annotating, discussing, editing, and cooperatively or collaboratively constructing knowledge among collections of learners and “teachers” (a loose term for anyone, or ones, along with machinery or systems that make learning more effective). Other social technologies connect people differently and less directly—for instance, by aggregating their behaviors in order to recommend books (e.g., Amazon), movies (e.g., Netflix) or websites (e.g., Google or Delicious). The size of the aggregations of people connected by social technologies can vary from two to many millions. The openness and potential for sharing makes social technologies particularly useful for education and learning applications, since in many ways the vast majority of learning is a social activity. As we shall see, many of our most powerful pedagogical theories and understandings of learning processes assume that knowledge is both created and validated in social contexts. Thus, developments in social technologies hold great promise to affect teaching and learning.
While social software has existed for many decades, the term social software is often attributed to Clay Shirky (2003), who defined it as “software that supports group interaction.” This definition is so broad that it includes everything from email to immersive, virtual worlds, so it has been qualified by a number of authors. Allen (2004) noted the historical evolution of social software tools as the Internet gained capacity to support human interaction, decision-making, planning, and other higher level activities across the boundaries of time and space, and less adeptly those of culture and language. Levin (2004) noted the affordance of the Web to support new patterns of interconnection that “facilitate new social patterns: multi-scale social spaces, conversation discovery and group forming, personal and social decoration and collaborative folk art.”
Coates (2002) describes the functional characteristics of social software to extend human communication capabilities. He notes the enhanced communication capacity provided by social software over time and distance, which are the traditional challenges of access addressed by distance education. He goes on to point out that social software adds tools to help us deal with the complexities and scale of online context such as collaborative filtering, spam control, recommendation, and authentication systems. He argues that social software supports the efficacy of social interaction by alleviating challenges of group functioning such as decision-making, maintaining group memory, versioning, and documenting processes.
A useful addendum to the various definitions of social software was added by Mejias, who defined social software as “software that allows people to interact and collaborate online or that aggregates the actions of networked users” (2005; emphasis added). The benefits that accrue to learners from this aggregation of the ideas, behaviors, and attitudes of others are defining features for many of the forms of collective social software defined in this text. We are pleased that, unlike many others, this definition includes systems that are only obliquely “social” in the traditional sense that emerges from face-to-face interaction, such as Google Search, whose PageRank algorithm uses implicit recommendations supplied by the crowd, and Amazon’s book recommendation feature, which employs similarities in user behavior to help guide future choices. Social technologies extend the possibilities for us to help one another to learn in ways that were difficult or impossible in the past, and that is the focus of this book.
To further clarify the term in an educational context, we have in the past defined educational social software as “networked tools that support and encourage individuals to learn together while retaining individual control over their time, space, presence, activity, identity and relationship” (T. Anderson, 2005, p. 4). This definition speaks to the right of learners and teachers to retain control over the educational context in which they are engaged. It obviously resonates with distance educators who define their particular form of education by the increase in access in many dimensions to the educational process. However, social software is also being used on campus where it affords and encourages communication, collaboration, and social support within and outside of normal classroom learning, maintaining and building new social ties.
Beyond formal settings, social software has become one of the most central means enabling lifelong learning: Google Search and Wikipedia, both social technologies that benefit from extremely large crowds, are the first port of call for many learners seeking knowledge. Whereas learning with others in the past often meant giving up certain freedoms, such as those of place, time, or direction, increasingly our social technologies support networked individualism (Rainie & Wellman, 2012), where we interact with others but remain at the centre of our social worlds.
We also focus on the increasing rights and freedoms provided to learners by the advent of networked learning. Students now have options to choose the mode, the pace, the presentation format, the credential, and the degree of cooperative versus individual learning they wish to engage in, both in formal and informal learning contexts.
By definition, learning is associated with change. We change our ideas, actions, capacities and skills in response to challenges and opportunities. For most types of learning, the necessary knowledge or skills needed to solve our problem already exists in the mind of another person or resource. Our job as learners and educators is to provide tools, paths, and techniques by which this knowledge can be accessed, appropriated, constructed, and re-constructed so as to meet our individual and collective needs. Social software is designed to help in two fundamental ways. First, it creates a transparency by which we can locate individuals or groups of humans with the tools and means to help us learn. Second, it serves to effectively leverage the tacit knowledge contained in the minds of others and the myriad learning objects in ways that can easily be adapted to individual and collective needs. Like other Internet resources, it does this with an economy of scale that allows global access at an almost negligible cost. For the purposes of this book, we use the terms “social media” and “social software” interchangeably although, technically speaking, social software is the tool that enables social media to be embodied or enacted.
Interactions Supported by Social Software
Media used socially supports three obvious kinds of interaction:
1. One-to-one: a single person engaging with one other person
2. One-to-many: a single person or entity broadcasting to many people
3. Many-to-many: multi-way interaction between many people
A less obvious kind of interaction that is of particular significance in social media is many-to-one, in which the actions, judgments, or behaviors of many people are aggregated, transformed, and re-presented to an individual. A classic example of this is Google Search. Google’s PageRank algorithm takes into account the number of links made to a page, and the number of links to the pages that link to the page, and so on, treating each as an implicit recommendation of the page that it links to. This is a form of latent human annotation (Kleinberg, 1998) where behaviors that may have occurred with other purposes in mind are mined and repurposed to serve the needs of individuals.
Social software tools may support synchronous interaction (real-time communication) and asynchronous interaction (communication that may be viewed, listened to, or read by the recipient at a different time than when it was posted), or both.
Social tools may afford direct or indirect forms of interaction: their purposes can vary from enabling communication to collaborative discovery, cooperative sharing, and more, often with layers of mediation that may either reveal or obscure the people who leave traces, intentional or otherwise, for others.
A vast number, perhaps the majority, of social software systems are aggregations of different forms, offering one-to-one, one-to-many, many-to-many, many-to-one, asynchronous, synchronous, direct and indirect interaction. Like all technologies, social technologies are assemblies and may be used with or as part of further assemblies (Arthur, 2009). In order to provide concrete and familiar examples, in table 1.1 we list a range of families of social software, broadly categorizing them by the predominant forms of social engagement that they involve.
Table 1.1 Examples of social software
We have broadly categorized a range of social tools to describe the predominant social features in terms of whether they are one-to-one, one-to-many, many-tomany, many-to-one, and direct or indirect, but many tools can be used for a range of purposes that could, at a stretch, allow them to fit into most categories. For example, in some cases email interactions might be almost as instantaneous as a text chat, yet we have characterized it as an asynchronous tool because that is its main use. A Skype system could be used to broadcast from one to many, but normally it is a two-way or multi-way conversation. It is also true that many tools are amalgams or mashups of different tools: YouTube, for example, not only includes options for discussing and rating videos but also allows social networking, social tagging, and more. Several tools fit into more than one category: for instance, immersive worlds usually incorporate text and video chat as well as other features.
The Value of Social Software
In the same way that the definitions of social software are numerous, so are its functions and forms, and most importantly, the ways in which these tools are used to enhance teaching and learning. In this section we provide an overview of some of the major pedagogical contributions of social software for both formal and informal learning.
Social Software Helps Build Communities
The influential work of Etienne Wenger (1998) focuses on the value that community brings to professional practice and informal learning. Educators have applied these sociological insights to communities created during formal study, and have argued that “community is the vehicle through which online courses are most effectively delivered regardless of content” (Palloff & Pratt, 2005, p. 1). The creation of community is both an educational product and a process. Educational communities can extend beyond the time and place of study to become the tool that forms and cements values, attitudes, connections, and friendships. They thus become the crucibles within which the hidden curriculum of higher education is formed. This hidden curriculum can be used to propagate social and class advantages (Margolis, 2001), but also teaches learners to act as experts and professionals, and to play the educational game effectively (T. Anderson, 2002).
Community also creates social obligations and entitlements. Members of learning communities are empowered to both give and receive help from fellow members. Learning in formal education contexts is rarely easy, and many times the aid, encouragement, or obligation to or from community members provides a necessary motivation to persevere.
Social Software Helps Create Knowledge
Knowledge is information that has been contextualized, made relevant, and owned. Understanding and attending to context becomes more critical as information moves throughout our global community. Context both allows and constrains us from making sense of information and constructing a coherent framework in which to situate it. Of course, context includes language and the more subtle forms of cultural marking, but it also extends to relevance, applicability, and understandability. If information is obscure or incomprehensible to an individual or group, it will be discarded and remains outside of the context of understanding that allows it to be internally recreated as wisdom. Knowledge is also relevant to a real concern. We are bombarded with information in many formats delivered through numerous forms of media. We cannot and should not attend to it all, yet information we do wish to own must prove relevant to a real interest. Finally, knowledge is information that is owned by individuals and aggregations of individuals. This ownership is expressed in its capacity for recollection and application. Owned knowledge is valued, but unlike physical objects, knowledge gains value when it is given away, shared, replicated, and reapplied. Unlike rival goods, where possession by one person excludes ownership or use by others, knowledge is a non-rival good, which loses none of its original value to its possessor when it is shared (Benkler, 2006). Indeed, the act of sharing can enhance the knowledge of its possessor, because having to communicate an idea or skill to another is often reinforcing or even transformative: there is no better way to learn than to teach. Furthermore, knowledge gains in its capacity to be transformed and transforming as it is applied in different contexts, enabling its possessors to do new things and use it in new ways that its originators may not have imagined.
Social Software Engages, Motivates, and is Enjoyable
When social software becomes a component of formal education, students and teachers interact with one another in more meaningful ways, creating a variety of positive results. Ted Panitz (1997) details over 67 benefits from engaging in collective learning, arguing that collaborating reduces anxiety, builds self-esteem, enhances student satisfaction, and fosters positive relationships between students and faculty. Blog authors report feeling motivated by the opportunity to share their knowledge and expertise, experience pleasurable reactions to comments and the recognition of others, and positive reassurance about their own thinking and writing (Pedersen & Macafee, 2007). Engagement in the learning process is reflected in time spent studying, the level of enjoyment, and the quality of work and learning outcomes (Chickering & Gamson, 1987; Herrington, Oliver, & Reeves, 2003; Kearsley & Schneiderman, 1998; Richardson & Newby, 2006). Engagement is so critical to learning that Kearsley and Schneiderman have developed a whole theory of learning based upon it, and Shulman argues that engagement is both a critical process to learning development and an outcome of education itself: “[an] educator’s responsibility is to make it possible for students to engage in experiences they would never otherwise have had” (2002, p.38).
Although it would be an exaggeration to suggest that all students enjoy working (and learning) with others, the opportunity to make new social contacts and build new networks of friends is an important reason why many engage in formal educational activities.
Social Software is Cost-effective
Unlike the development of computer-assisted instruction, tutorials, and other multimedia-enhanced forms of online learning, it is easy and very cost effective to include social networking in formal and informal learning. The content of educational social networking is, for the most part, created by the participants in the process of their learning. The most common networking activity is to make comments and engage in discussions relating to the subject of study. However, there are many other effective social learning activities, including the selection and annotation of learning resources (educational tagging), formal debates and guided discussion, collaboratively creating reports and presentations, individual and group reflections, and so on. All of these activities are created by participants in the process of learning. The archives of these activities become content for further study and reflection across course sections, years, and institutions.
The “conversant” forms of online learning have been criticized as not being scalable or cost effective—at least compared to more traditional, individual-based forms of distance education (Annand, 1999). Social software can, however, be used to enhance and focus on students responding to and helping one another as peers, thereby creating models of formal learning that may be more cost effective than those organized by teachers. While not denying the importance of “teacher presence” at some point in an educational transaction, there is a need for learning designs that are scalable and can meet the learning needs of the millions of learners who are currently unable to participate in more traditional forms of campus-based education (J. S. Daniel, 1996).
Social Software Encourages Active Learning
Active learning engages learners emotionally and cognitively in the education process. Although not without controversy in the educational world, active learning flows from constructivist ideals in which learners shape their own understandings, ideas, and mental models. Activities that induce active learning include debates, collaborative learning, problem-solving and, most recently, inquiry (Chang, Sung, & Lee, 2003). Active learning has been associated with ideas of discovery, as opposed to guided inquiry, but as Mayer (2004) notes, cognitive engagement is critical to all forms of learning. Social networking creates both motivations and obligations among learners to work together, or at least in harmony, through the learning process. Activities that draw out learners’ interests, expertise, and individual gifts benefit not only the recipient of this expertise but also gives learners the thrill and expanded knowledge associated with helping or teaching another (B. Daniel, Schwier, & McCalla, 2003).
Social Software is Accountable and Transparent
Unlike many forms of communication, most types of social software leave persistent trails documenting the activities and conversations of participants. Although anonymous and fantasy-based approaches can be supported in social software contexts, in both formal and informal learning these are not the norm, and in most cases deception and anonymity are not acceptable social behaviors. The transparency and persistence of learning activities give rise to conditions that are ideal for the development of social capital. Individuals who have contributed the most to the community see their contributions giving them authority and prestige within that community and across their networks.
Social Software Spans the Gap Between Formal and Informal Learning
Social software, especially social networking, blurs the distinction between formal and informal learning. Research on learning often bifurcates learning into two often mutually hostile camps: formal education, with its institutional champions of accreditation, and informal learning, championed by advocates of community, workplace, informal and incidental learning. For example, Marsick and Watkins (2001, p. 28) conclude that informal learning is characterized as being:
• Integrated with daily routines—in contrast to formal education, which takes place at times and places defined by the educational institution.
• Triggered by an internal or external jolt. In formal education, the “jolt” almost always originates with requirements set by the teacher. Not highly conscious. Although formal education has also been criticized for putting learners to sleep in lecture theaters, the intent of the education is always made explicit in terms of expected learning outcomes.
• Haphazard and influenced by chance. In formal settings, the course outline ensures that curriculum is followed and certainly not influenced by chance.
• An inductive process of reflection and action. Although not excluded, reflection and action where ideas are validated in real-life contexts are rare in formal education.
• Linked to the learning of others. Formal education is almost always a contest among registered students for marks awarded by teachers, making the establishment of collaborative and supportive learning challenging, though not impossible.
Using Marsick and Watkins’s criteria, we argue that social networking integrates formal and informal learning, since its tools and context are used to coordinate both formal learning, and workplace, family and community ideas, relations, and activities. Jolts or triggers arise both from formal learning interactions and occurrences in real life, and social networking provides a forum where these jolts can be discussed, assessed, and reflected upon. Reflection and the reactions of others in social networking contexts are most often stimulating and rewarding. Social networking spans across both formal education and learners’ private and public lives. Thus, it is influenced both by chance and the requirements of formal education. Finally, social networking is, by definition and intense practice, linked to the learning of others. This linking may take place through formal collaborative tasks assigned by teachers, through reactions, feedback, and response to blogged reflections, or through spontaneous conversations in real time online or in face-to-face encounters.
Social Software Addresses both Individual and Social Needs
It has always been challenging to differentiate between the benefits and costs of education and how they are apportioned between the wider social community and the individual. John Dewey (1897) argued that “the school is primarily a social institution” and that “all education proceeds by the participation of an individual in the social consciousness of the race” (p.77), celebrating the role in which education is used to pass on to learners the benefits of socially derived knowledge. But the debate over education’s cost also reveals that it benefits the individual, and this is readily verified by noting the earning gap between citizens with high and low education levels (although this is a circular argument—employers seek those with qualifications and, in the case of higher education, the weeding out of those with less innate capability by university admission procedures means that many of the differences may be put down to intelligence and aptitude, or in some cases, social class). But the benefits of schooling to either individuals or the state depend upon learners being able to work, collaborate, and engage in discussion and decision making with others. Social networking both encourages and affords opportunities to practice these social skills in contexts that range from small groups to large and widely distributed networks.
Social Software Builds Identity, Expertise, and Social Capital
Generally the possession of social capital, like other forms of capital, allows individuals and groups to accomplish their goals because they can draw on the resources, support, and encouragement of these resources—in this case, human beings. Sandefur and Laumann (1988) argue that social capital confers three major benefits upon its owners: information, influence and control, and social solidarity. Social networking creates and enhances relationships among learners. These relationships can then be used by individuals and groups to achieve goals that are frequently beyond their individual capacity to attain (S. E. Page, 2008).
Social Software is Easy to Use
Most social software applications have very little functionality until they attract a significant number of users. In addition, their value to individual users increases as a function of other users. To attract high numbers of users, social software architects spend considerable effort in making interfaces friendly, intuitive, and easy to navigate. Social software has been built in an era dominated by “Net generation” learners who have adapted and adopted computer tools, but who are equally known for low attention thresholds—especially for confusing or difficult-to-understand applications. To be more precise, retaining such users requires rapid learnability. It is not the be-all and end-all: even those social tools that usability studies reveal as being very difficult to use may succeed due to their perceived value to the community. However, when all else is equal, learnability can mean the difference between success and failure in a social software system.
Social Software is Accessible
Social software is accessible in two senses of the word. First, the contributions of others in social software systems and tools are often not hidden behind passwords or closed classroom doors, nor are they archived in inaccessible libraries. Rather, social software has a tendency to meet the needs of a growing number of users. Failure to evolve results in the wreckage of empty and unused sites—a common sight on the changing twenty-first-century Web.
In a second sense, most social software is accessible to all learners, including those with physical or mental constraints. For example, being digital, social software can be reformatted into large print or audio formats to meet the needs of visually impaired users, or presented in alternative forms to those with dyslexia. It also makes no difference to social software users if input came from a voice, a keyboard, a Bliss board, or a drawing tablet. Social software can also be retrieved on many types of devices, ranging from home theaters to cell phones. This accessibility enables social software to be used for high-quality learning by anyone, anywhere. However, we do recognize that this is far from universally true, and there is a counter-trend to release early and often to appeal to the widest audience, sometimes making accessibility a secondary consideration.
Social Software Protects and Advances Current Models of Ownership and Identity
Social relationships are built on reputation and responsibility. Social software seeks to return the ownership of comments to their creator. Thus the persistence of contribution across formal and informal communities and the technical capacity for all participants to link, search, and archive contributions across these communities is critical. But social software also allows for new types of ownership. In pre-digital times, possession implied exclusive use—if I lost my possession, I was no longer able to use my property. Digital property, like the flame of a candle, is not diminished when shared with others. Indeed, the sharing of both candles and digital artifacts creates more light for the benefit of all.
Social Software is Persistent and Findable
Being digital and thus searchable, social contributions (with permission of the participants) can be used, referenced, researched, extracted, reused, and recycled across time and space (Erickson & Kellogg, 2000). The use of syndication, automatic and cooperative tagging, indexing, and spider tools allows social software contributions and information about their authors to be searched, harvested, and extracted.
Social Software Supports Multiple Media Formats
Although a powerful and expressive communication genre, and the one upon which most academic knowledge is inscribed, text is but one format for social expression. Social software supports audio (music, voice conversation, and podcasts), video (videoconferencing, videocasts), and graphics (photos, drawings, and animation displays). These can be combined to create immersive worlds, waves, VoiceThreads, and many other engaging media combinations.
Social Software Encourages Debate, Cognitive Conflict, and Discussion
Knowledge is built from active engagement with conflicting and confounding ideas that challenge older, pre-existing knowledge (Piaget, 1952). Given the capacity of online social learning to span the distance of both space and time, it is not surprising that learners become aware of the ideas of others. Since these ideas originate in different contexts, it is likely that some will be as divergent as they are convergent. Through this divergence, learners are forced to make explicit much of their implicit and pre-existing knowledge so that it can be communicated effectively to others. At the same time, the dissonance that arises when learners are exposed to divergent ideas forces them to defend, strengthen, alter, or abandon their existing ideas.
Social Software Leads to Emergence
Typically, social software contains elements that algorithmically combine the ideas, actions, or decisions of many to produce an unplanned result. For example, tag clouds form from the tagging behavior of a system’s users, with more popular tags being emphasized, typically displayed with a larger font. No one has decided which tags should be emphasized or not: the pattern emerges from the combined behaviors of many people.
Similarly, the buying behavior of previous customers can be used to offer recommendations to future buyers who have exhibited similar purchase patterns, whether through explicit recommendation or simply by observing that people who bought a particular item also bought other items. As with tagging, no individual has decided that a particular book should be recommended: group behavior dictates recommendations. There are many examples of such emergent patterns in social software systems, and we will discuss the implications of these at length later in this book.
Social Software is Soft
All technologies are assemblies of other technologies. That is how they evolve, and how they are built, through combination and recombination (Arthur, 2009). Some of those technologies in an assembly will be harder and more deterministic, some softer and open to change by end users. Softer technologies are those that incorporate humans in their design and enactment, allowing tools to be used in many different ways. Social technologies are inherently soft. Social technology applications are inseparable from the processes, rules, norms and techniques that are assembled with them. The technologies provide opportunities, and the users as individuals, groups, and networks determine how to best exploit them. Together they proceed in a dance (T. Anderson, 2009), intricately interwoven, mutually affective, and inseparable.
Social Software Supports Creativity
Being soft, social software is rich with assembling potential for human activities, and may be deeply interwoven with social and organizational processes. Unlike more specialized tools that are designed for particular purposes and have little flexibility, if any at all, for alternative uses, social software enables creative uses and purposes that its designers probably never dreamed of. It is thus a vehicle for change and creativity in learning and teaching.
Social Software Expands the Adjacent Possible
Every new technology that adds to those that came before extends what Kauffman (2000) refers to as the “adjacent possible:” the powerful driving force behind evolution and change in many aspects of the natural and built environment. Each time a new capacity evolves, it opens up avenues that were not there before. For example, it was necessary for light-sensitive cells to develop in animals before the potential existed for them to evolve into eyes. When we build a new technology, it opens up new paths for change. It is not just that we gain new capabilities, but that more potential capabilities consequently emerge. It would have been inconceivable for humans to reach the moon without a succession of earlier technologies, each building on and often incorporating the last, from the humblest rivet or metallurgical technique to the most sophisticated computational and propulsion devices.
In every way, not only do we, as Newton suggested, stand on the shoulders of giants, but everything that matters to us, from our bodies’ cells to our television sets, emerges from the history of what came previously. Moreover, this expansion is increasing at an exponential rate (Kelly, 2010). The rapid proliferation of social software tools is opening up vast landscapes of possibility that were never there before and, because such technologies are soft and combinable, their affordances are far greater than more rigid or, as U. M. Franklin (1999) puts it, prescriptive technologies.
Users of Educational Social Software
It is no exaggeration to claim that the number of users and applications of social software exploded during the first decade of the twenty-first century. The site Go2web20 provides links to over 3,000 unique Web 2.0 applications, most of which could also be classified as social software, and very few of which existed a decade ago. These networked applications have user numbers that range in size from very small to large country- or even continent-sized populations. The successful mega social software sites including Facebook, Twitter, Google+, YouTube, Tumblr, Pinterest, MySpace, SecondLife, Blogger, and Flickr number their user accounts in the tens of millions, and tabulations of monthly unique visitors in the millions or even billions. As we write this in early 2014, Facebook has over 1.3 billion user accounts (Statisticbrain, 2014a), Twitter over 645 million (Statisticbrain, 2014a), LinkedIn over 227 million (Linkedin, 2014), and Google+ has over 1.15 billion accounts, though the way this is designed to integrate far beyond the simple site-based approach used by Facebook means that only around a third of those are actively using the system (Wearsocial, 2014). WhatsApp, a fast growing mobile messaging system recently acquired by Facebook, has 450 million monthly users, growing at a rate of a million a day (Wearsocial, 2014). An astonishing 2 billion videos are watched on YouTube every day (Bullas, 2012) but this pales in comparison to users sharing content and links with Google +1 or Facebook shares. Searchmetrics predicts that, by May 2016, there will be 1096 billion Google +1s every month, and a further 849 billion via Facebook. Simple interactions such as sharing show not just passive interest in content but active social engagement with others.
A 2007 Canadian survey of a single social software application, Facebook, revealed that some cities had over 40% of the population as registered users (Feeley & Brooks, 2007). In 2011, the proportion of Canadian users had reached over 50%, a little below the global average. In Indonesia and the Philippines, social network use is well over 70%, and it is 60% in Russia and India (Broadband Commission, 2012, p. 9) Among Generation Y, social software use encompassed over 96% of the sampled population as early as 2007 (Grunwald Associates LLC, 2007). By 2010, the rate of growth for most social sites was still rapidly increasing, with Facebook experiencing a 7% increase in users year over year, and Twitter 11% (comScore, 2011). Perhaps the most interesting growth is seen in mobile social software. Though social media technology fit well with conventional mobile phones, broadband makes their data-intensive operation possible. With over 2 billion mobile broadband subscriptions worldwide compared to a mere 696 million fixed-line broadband subscriptions (Broadband Commission, 2013, p.12), with broadband subscriptions in the third world now exceeding those in the developed world, and with anticipated growth to 7 billion mobile broadband subscriptions by 2017, it seems almost certain that mobile social media are bound to dominate (Broadband Commission, 2013, p.14).
The largest growth in social software use is in older users, with a 36% increase in use between 2009 and 2010 for 55–64 year-old users and 34% for those 65+, though the majority are still in the 25–44 age range (comScore, 2012), and 98% of Americans in the 18-24 age range use social media of some kind (Statisticbrain, 2014 b). The demographic spread across different social software systems varies widely and reflects a maturing and ever more diverse range of systems and tools. It should be noted that many surveys do not consider tools such as YouTube, Wikipedia, and Google Search to be social media, despite the fact that they are entirely powered by the crowd and exist only because of user-generated content.
Social software includes a variety of types of networked applications offering different forms of social activity and focusing on different target audiences and interests. Social software is used to connect and reconnect people to families, past and current schoolmates, coworkers, local neighbours, and others sharing the same physical spaces. But it also links those separated by vast differences of geography and as importantly, differences of culture, age, income, and race. Besides supporting and enhancing existing relationships, social software also facilitates the discovery and building of new relationships through profiles, recommendations, observations, and charting of users with similar interests or activity patterns.
Social Software in Formal Education
The use of social software for personal reasons challenges educators used to having control over the tools used in their programs. Social software, unlike institutionally-based learning management systems (LMS), is often either not owned by the educational institution or incorporates elements that come from beyond it, is focused on individuals and their relationships rather than courses, and is under the control of these users, not teachers. In most current instances, social software applications have not been designed specifically for students enrolled in formal education programs. Rather, students join social networks for personal reasons, motivated by a desire to expand and enrich their social lives. Thus, a central challenge of this book is to help educators both understand social software use and equip them with the knowledge and skill to use educational software in formal courses and as doors to lifelong learning opportunities for themselves and their students.
To date, much social software use has focused on building communities in parallel or outside of formal education. For example, sites such as Facebook support communities of students enrolled or at least interested in a particular university or school. These groups often contain thousands of members and are used for discussions and announcements about special activities, providing a way to connect users who share a common interest in that particular institution—or at least its social life. We believe that these tools are too important and powerful to be excluded from the formal curriculum, that they can be used to support and encourage learning in all subject domains. In addition, the use of social software applications in formal education encourages and supports learners with lifelong learning skills that they will be able to apply beyond their graduation from any formal education program. Finally, social software develops “the kinds of skills needed to meet the challenge of earning a living in the twenty-first century—flexibility, adaptability, collaborativeness and problem-solving prowess—bear a one-to-one congruence with the constellation of skills and outlooks needed to engage in every other key participation opportunity related to human capacity development” (Levinger, 1996, Chapter 2, para. 16).
MOOCs
Recent years have seen a massive growth in MOOCs (Massive Open Online Courses), with courses from organizations like edX, Coursera, Udacity, and others gaining tens of thousands of participants. Their forebears, starting with CCK08—a connectivist course with a few thousand users (Downes, 2008b)—remain intensive in their use of social software, and could not run without extensive networked technologies such as Twitter, blogs, and social aggregation platforms. While many popular MOOCs employ predominantly instructivist approaches to teaching, they also provide tools for social interaction—as a result, a large ecosystem of social groups and networks has sprung up around them, with learners helping one another, exchanging ideas, and learning together in more or less formal groupings (Severance, 2012).
Social Software in Informal Learning
Non-formal and informal intentional learning outstrips formal learning in both time spent on the activity and the number of people engaged in it many times over, and has always done so. Tough’s research (1979) in the 1970s suggested that adults typically spent around 200 hours every year on intentional learning activities. In 2000, Livingstone found that Canadian adults spent considerably more time on informal learning than formal, in the area of 15 hours per week. Were these research studies to be repeated today, this amount of time may be considerably higher. Google’s search engine is used by over 85% of Internet users (Pick, 2012) and whenever someone performs a search, it is usually in order to learn something or be reminded of something that they already know. Perhaps it would be more accurate to say that, in keeping with connectivist precepts, people know that the knowledge they seek resides in the network—even if they often do not need to retain it—but, in one way or another, they are seeking knowledge. In other words, Google Search is a learning technology and, by any measure, the most widely used distinct learning technology product in the world.
While language and books are undoubtedly more important learning technologies, there is no single book or language that reaches a wider audience than Google Search. Meanwhile, Wikipedia, its nearest competitor as a learning technology, receives close to 10 million visits an hour to its English-language site alone, with nearly 8 billion page views of over 4 million articles produced by tens of thousands of editors, over 33,000 articles described as “active”, which means having had five or more edits per year (Wikimedia, 2014). Wikipedia gets further millions of visitors to its simplified English-language and Chinese sites, with billions of visitors to other sites using less commonplace languages. But Google Search and Wikipedia are just the tip of a massive iceberg of informal and nonformal learning that is enabled by the social web. Sites such as StackOverload, Answers.com, Lifehacker, How Stuff Works, Instructables, as well as millions of YouTube videos and thousands of less well-known sites provide more or less formal instruction to millions of people every day. Twitter, Facebook, and Google+ are rich sources of knowledge and information, providing simple questions and answers for study groups, reading groups, and collaborators. Despite the pointless trivia that often passes through it, the social web can be appreciated as a web of learning.
The Many Purposes of Educational Social Software
Social software functions in many ways and is as divergent in forms, systems, and software packages as it is in the interest and skills of users. However, Mejias (2005) argues that social software serves two purposes. The first is to manage ever-larger sets of social relationships, such that meaningful and functional social relationships can be built and effective communications can be maintained despite the numbers, distances, or time barriers that separate them. Second, social software affords us opportunity to create and support more intimate and authentic relationships between our closest friends, families, and colleagues. It also helps us to build social confidence, and sometimes, new relationships. Ellison, Steinfield, and Lampe (2007) have found that Facebook usage is associated with increased formation of social capital, especially for those with low self-esteem and lower life satisfaction. They also found that both bonding social capital (strengthening relationships with those whom one already has a primary relationship) and bridging social capital (weaker, more extended relationships with others) were associated with increased use of Facebook.
These direct social uses are important, but they are by no means the only ways that social software can provide value to learners. The social net creates an ecology “involving not only technologies but also other people, values, norms and social contexts” (Petrič, 2006, p. 293). This enables a learner to construct knowledge by seeing his or her place in the world, and hence grasping connections not just with other people but also with the world itself.
An obvious benefit that is not addressed by Mejias’s classifications is that social software systems enable learners to create content, find answers to questions, make and receive challenges, and provide opportunities to see the world differently. A less obvious benefit is that social software can be used to aggregate the opinions, beliefs, and discoveries of many people in order to guide us through our learning journeys with little or no direct social interaction at all. Social software is not just social glue but an enabler of the creation, discovery, and presentation of new knowledge.
Other people have many roles to play in the learning process, not just in the construction of factual or procedural knowledge. From an educational perspective, social software can, for instance, enable users to:
• Provide helpful resources
• Help them move into the next zone of proximal development
• Solve problems
• Create more complex artifacts
• Present multiple perspectives and enrich connections
• Model different ways of thinking • Explore ethical problems
• Learn to work with others
• Connect ideas from different perspectives and fill in gaps to connect existing ideas.
Uses for Social Software in Learning
We have already seen that there are many different forms of social software, which are becoming ubiquitous. However, though any exchange of information may instigate or enable learning, not all social software is suited to every learning task. In table 1.2 we present a few of the more obvious ways that social software can benefit the learner. Some of these functions overlap, and many of the same tools can be used for different purposes. The intention here is to give a sense of the range of ways that social software can support or enable learning to occur.
Table 1.2 Functions of educational social software.
Anyone and Everyone Can Be A Developer
Building a social application is no longer the preserve of skilled experts. Anyone with a basic understanding of a web browser can now create a social application on Ning (ning.com) or set up a group on an Elgg system, Facebook, academia. edu, or LinkedIn. In the group-oriented institutional domain, many sites provide services that allow anyone to set up courses or even whole learning management systems. It takes little extra effort to use Microsoft’s discontinued Popfly, and not much more for Yahoo Pipes, Google Gears, or Intel’s Mash Maker. Users can make basic but highly useful mashups incorporating RSS feeds, interactive maps, discussions, podcasts, and more by using systems such as iGoogle, Netvibes, Sproutbuilder, or PageFlakes. For the more proficient computer user, a rapidly increasing assortment of tools is available to build applications for Facebook or OpenSocial that take advantage of the facilities, users, variables, and processes provided by such complex social software to extend or use their functionality in a new way. Mobile app builders are widely and, sometimes, freely available: ShoutEm, Mobile App Builder, MobinCube, and many more offer simple tools to create fairly sophisticated apps for iOS, Android, and other mobile platforms.
Given the ease with which new systems can be created and/or built on top of others, we are moving toward an era that is freer of the hegemony of technocrats and learning technologists, where any teacher or instructional designer can build, select, or aggregate the tools they need to create a new learning environment adapted to the needs of their learners. There are, of course, great risks in what are typically cloud-based tools: questions about the ownership of data, privacy and security concerns, and overall system reliability. Furthermore, such innovations exist within a structural and technological hierarchy that may hinder or restrict their development. The market for applications is a rapidly evolving and highly competitive space.
Perhaps more interestingly, the same tools can, in principle, be used by the learners themselves to take the pieces that they need in the form that they need them to create their own learning spaces. The notion of the Personal Learning Environment (PLE) has been gaining traction for some years: it is an aggregation of learning tools and environments that is built by and for the learner, often using some form of widget (Downes, 2007; S. Wilson et al., 2007). Specifications for widget standards are now reaching maturity through the efforts of the W3Consortium (W3C) and it is increasingly easy to combine these into a single, web-hosted space. Mature environments such as Elgg offer such capabilities out of the box, while other systems such as Wookie are built from the ground up to do nothing but serve widgets.
The Importance of Effective Design
Though such tools can be very powerful learning aids, the corollary is that they are also potentially very dangerous: the greater the capabilities and flexibility of a system, the more it becomes an essential feature of our learning; and thus when it goes wrong, the more disastrous the effects. We have suffered enough over the years from the weaknesses of professionally designed software for education to know that there are many pitfalls and errors that can be made. Decisions that seem reasonable in one context may be inappropriate in another: we may inadvertently lock ourselves into technologies or approaches, build unusable interfaces, limit functionalities due to lack of time or skill, and so on. Just as limited options can lead us to poor choices, limitless options can make it hard to choose right from wrong for the learning environment.
The greater our capabilities, the easier it is to do things badly. Now that such systems are entering the toolsets of amateurs, the risks of poor design and inappropriate use have been magnified. It is too easy to forget that we are doing more than simply creating content, but embodying processes and patterns of learning and teaching that may tie us to systems that imprison rather than liberate us. If we are to become the creators of tools and environments rather than developing simple learning content, we must learn to do it right. In each chapter relating to sets, nets, groups, and collectives, we provide a set of design principles and guidelines as well as a framework for understanding social systems for learning that will hopefully reduce the capacity for error. In our "Stories From the Field" chapter, we present some stories and lessons that suggest useful ways to approach social systems for learning, and highlight some of the mistakes we have made on our journey.
Conclusion
We have painted what is mostly a very rosy picture of the potential and, in most cases, realized benefits of social software for learning. We have yet to spend much time on the dangers and disadvantages because we wish to present a prima facie compelling case that social software is worthy of investigation. As we shall see, all software comes with biases, embedded belief systems, risks, and pragmatic, pedagogical, and ethical pitfalls that can trap even wary designers. If we are to realize the potential value of social software for learning, it is therefore vital to understand how it works, how it does its job. That is the purpose of this book.
So far, we have presented no strong theoretical framework to help explain and inform how social software fits into a learning journey, and we have not examined the different ways it can work. These topics will be covered in the next few chapters, where we examine in turn the pedagogies of social learning, the social forms that are found in social software systems for learning, and the power and risks of the collective. | textbooks/socialsci/Education_and_Professional_Development/Teaching_Crowds_-_Learning_and_Social_Media_(Dron_and_Anderson)/01%3A_On_the_Nature_and_Value_of_Social_Software_for_Learning.txt |
SOCIAL LEARNING THEORIES
He who loves practice without theory is like the sailor who boards ship without a rudder and compass and never knows where he may cast. Leonardo da Vinci
In this chapter we provide an overview of the major learning theories that influence the development of social learning activity, culture, and research. For each theory we focus on the environment or the context in which learning takes place, and the constraints and facilities provided through that context. When this context is changed by pedagogical intervention, technological affordances, social expectations, or a host of other variables, one can expect change in learning effectiveness or efficiency. Social learning—especially in its cyber-enhanced forms—has evolved in a context of rapid change, and many of its proponents are champions of this. However, the formal institutional structures where most of these changes take place are noted more for their resistance to change and defense of tradition, than for the capacity for rapid or emergent adaptation (Bates, 2005; Winner, 1997). Thus online learning has long been engulfed in controversy, and there has been considerable jockeying among those with a vested interest in either change or the status quo. While this tension will and probably should never be fully resolved, we believe that dedicated educators often share underlying assumptions about teaching and learning. This section is designed to explicate the rationale for social learning and expose both its promises and shortcomings.
Social Learning
The defining component of social learning is the presence and participation of other learners and, at least in formal education, a teacher. In this section we will outline the theoretical and empirical evidence indicating how and why the presence of others makes a difference to both teaching and learning.
ce of others makes a difference to both teaching and learning. Until recently, most literature on social learning assumed that the interaction between participants takes place face-to-face, and often in a classroom, laboratory, or other structured context. However, recent pedagogical literature, especially from distance education and e-learning perspectives often assumes an electronically mediated context for teaching and learning. It is natural to wish to compare the online and face-to-face alternatives. When considered overall, studies reveal no significant difference in learning outcomes between activities and courses that are taken at a distance and those in the classroom (Russell, 2010). This is not too surprising because it is possible to use any learning technology well or badly, regardless of the type. It makes no more sense to ask whether people learn better at a distance or face-to-face than to ask whether pictures drawn in pencil are better than ones painted with oils. They are different technologies that can produce both excellent and atrocious results. That aside, the reliability of most studies that show the benefits of technology to learning are dubious, conflating many different factors (Oblinger & Hawkins, 2006; Russell, 2010). However, it is likely that the constraints and affordances of communication and information technologies, especially factors related to the limits of the media, scale, distance, and time, do effect how we learn from and with each other.
Different constraints and affordances will lead to different ways of doing things. Some methods will be difficult or impossible using certain media, but this is true in any setting. Just as it would not be wise to teach appreciation of music at a construction site or without the means to make music, it would not be sensible to teach programming without a computer. But the devil is, as always, in the details. Measuring the effects of teaching interventions and factoring in other contextual variables such as the nature and effectiveness of the technology, the users’ experience and efficacy, their motivation and the nature of the subject is difficult when they combine to create very complex and multifaceted learning environments.
Generations of Distance Learning
There have been many attempts to examine the history of distance learning in terms of dominant technologies (e.g., Bates, 2005; Gunawardena & McIsaac, 2004). We have taken a slightly different tack, looking instead at the evolution of pedagogies in distance learning (T. Anderson & Dron, 2011). These perspectives are not totally at odds because there is a strong case to be made for treating pedagogies themselves as technologies that only bring about improvements in learning when used in combination with other technologies (Dron, 2012).
At the very least, pedagogies and technologies are intertwined in a dance, where the moves of one determine the moves of the other (T. Anderson, 2009). In our three-generation model, we have divided the generations of developments in distance learning into three distinct pedagogical eras; at the time of writing, the third generation is still emerging. We consider each generation to be partly determined by the communication and processing tools available, and partly by the popular pedagogies of the period, noting that changes in each one alters the adjacent possibilities and thus both the affordances and uses of the other. This codependency between tools and pedagogies is inevitable: until there are the means for cheap, rapid forms of many-to-many dialogue, for example, it is very hard to design distance learning experiences that require peer debate. Distance education was not a viable option at all until the advent of reliable and affordable technologies of production like the printing press, and communication systems such as a postal service.
Although we describe each generation as an historical sequence, this does not mean that previous generations have faded away or vanished. As Kelly (2010) has observed, technologies seldom, if ever, die. As new pedagogical models emerge, they do not replace what came before, though they may become more dominant than those they supersede. Not only is it possible to find large numbers of fairly pure examples of older approaches being used today, the newer generations incorporate the older ones in their assemblies so previous generations of pedagogy have become, if anything, more popular than they were when first adopted.
These are the three generations that we have identified as emerging so far:
1. Behaviorist/cognitivist: pedagogies of instruction
2. Social constructivist: pedagogies of construction
3. Connectivist: pedagogies of connection We treat each of these in turn in the following sections.
The Instructivist-Era: Cognitivist/ Behaviorist Approaches
Until fairly recently, there were very few alternatives to broadcast or distribute fixed media for distance learning. Mail, print, radio, TV, video or audio recordings made up the vast majority of media available to distance educators and students. Telephone, the postal service and, in some cases, two-way radio were about as good as it got if two-way communication was needed, which meant that communication was nearly always one-to-many or one-to-one. Before the advent of the postal service, distance education as we know it today was virtually impossible, so it is no coincidence that the first examples of the form date from the late eighteenth century when such systems became ubiquitous and reliable (Gunawardena & McIsaac, 2004).
It is almost inevitable, without much capacity to communicate, that an instructivist approach will become the dominant form of teaching. The notion that there is a body of knowledge that can be represented in written, spoken, or enacted form and communicated from the learned to the unlearned is a powerful one at the best of times, but when it is combined with a communication channel that limits dialogue in both quantity and pace, an instructivist approach is overwhelmingly likely to occur. There are exceptions: Piaget’s constructivist pedagogies (1970), for example, focus on the construction of knowledge by an individual rather than simple conveyance of knowledge.
Instructivist teaching has, however, not historically been the dominant form of pedagogy, at least in Western culture. The Socratic form of pedagogic dialogue, for example, is inherently social. Apprenticeship models, while explicitly acknowledging that there are masters from whom to learn, are essentially conversational. Learning outside schoolrooms has almost always been a two-way flow of information. The “teacher” (whether a parent, peer, or formal pedagogue) imparts knowledge through telling and showing, but equally must pay attention to how and whether a learner is learning. With this in mind, and given that the focus of this book is on social learning, we briefly overview some of the main features of the cognitivist/behaviorist model of learning.
Cognitivist/ Behaviorist Pedagogies
Cognitivist/behaviorist pedagogies center on the individual as an autonomous entity to which certain stimuli can be applied in order to achieve a certain measurable output. Behaviorist pedagogies deliberately go no further than these observable inputs and outputs (Skinner, 1974), whereas cognitivist approaches take into account the mental models and internal processes, building on a richer psychological understanding of learning and how it occurs (e.g., Bruner, 1966; Gagne, 1985; Gardner, 1993). In each case, however, the viewpoint is that of an individual, and the individual processes that are involved in learning. The cognitivist/behaviourist tradition is also predominantly instructivist, inasmuch as it is assumed there is a body of material or specified measurable skill to be learned that may be transmitted to the learner. This mold begins to be broken in the Piagetian branch of cognitivism: constructivism (Piaget, 1970).
For Piaget and his followers, knowledge occurs as a result of connecting and constructing ideas, feelings, and structures. In cognitivist-constructivist approaches, learning is seen as a process of construction, building models, and connecting old knowledge with new. Every individual constructs a view of the world for him- or herself. This epistemologically different understanding of learning leads naturally to pedagogies such as problem-based, enquiry-based, and constructionist (learning by creating) methods of learning, which assume that, though there may be measurable outcomes reached by all, every individual constructs knowledge differently: starting somewhere different, learning differently, with different meanings attached to what they learn.
However, though epistemologically more advanced, the emphasis of such approaches is very much on the learner as an autonomous agent, learning alone. Although the learner may learn from others, learning itself is seen as something internal to the individual. This perspective is important: it is vital to understanding how individuals learn as much as how they learn with others. Much modern research in the area draws on our increasing knowledge of the brain and how we process and store information, leading to a field of study under the name of “brainbased learning” (Jensen, 2008; Weiss, 2000). Cognitive behavioral pedagogical models dominate training programs and much computer-based training, and have shown consistently improving results when teaching individuals to accomplish pre-determined behavioral objectives (see, for example, Fletcher, 2009).
Learning an an Inherently Social Process
Processes of meaning-making, integrating new information, and creating knowledge are not only enhanced and stimulated through reaction, discussion, and argument with others but also much knowledge confirmation, interpretation, contextualization, and validation happens only through interaction with others.
In an interesting study, Okita, Bailenson, and Schwartz (2007) tested learning and the degree of arousal (associated with engagement) for learners who believed they were interacting with an avatar controlled by a human being, versus those who believed they were interacting with an animated but machine-controlled agent. They found that the belief that one was interacting with a human resulted in both better learning outcomes and more engagement with the learning task.
Further confirmation that we think and behave differently when we believe we are interacting with humans comes from a fascinating study by Krach et al., (2009), in which all subjects engaged in the same task (interacting with a computer to play the Prisoner’s Dilemma), but showed significant differences in functional MRI scans depending on whether they believed they were interacting with a machine or a human. This does not mean that learning cannot or does not happen when an individual is studying on his or her own or interacting with simulations, tutorial systems, or other learning modalities, but it does highlight the increased attention of learners when they are, or believe they are, interacting with real human beings.
Humans have evolved for millions of years in contexts where shared support and cooperative activity has increased survival probabilities (E. O. Wilson, 2012). Thus we have evolutionary propensities for positively opening our social and learning selves to others who serve as models and sources of information, and who provide direct assistance in solving many types of problems. In our primordial past, and perhaps to a greater degree in our networked future, human beings will continue to exploit and benefit from the support and assistance of others. In the past these potential assistants shared common time and space—now they are available anytime and anyplace.
Social Learning Theories
The poet John Donne’s proclamation that no man is an island suggests our deep interdependence with others. It is an interesting but perhaps irresolvable debate as to which came first—whether it was the emergence of self from the family or tribal origins, or whether society emerged from the aggregation of many selves. Even when we are working alone, our language, metaphors, thoughts, and feelings are guided and created through the use of signs, symbols, and expressions that we have acquired from others. John Dewey’s colleague and fellow pragmatist George Herbert Mead is most remembered for his notions of how a sense of self can only arise through discourse with others. He notes how “we are in possession of selves just in so far as we can and do take the attitude of others towards ourselves and respond to those attitudes” (qtd. in Pfuetze, 1954, p.78). But Mead goes even further, arguing that in interaction and cooperative work with others, the giving and taking of directions and advice allows us to develop critical forms of empathy to create appropriate and viable images of ourselves. He argues that “in giving directions to others, he gives them to himself, and thus arouses a similar response in himself which is understood by himself” (Pfuetze, 1954, p.79). This lays the groundwork for responsibility and self-control.
Lave and Wenger argue, “activities, tasks, functions, and understandings do not exist in isolation; they are part of broader systems of relations in which they have meaning. These systems of relations arise out of and are reproduced and developed within social communities, which are in part systems of relations among persons” (1991, p. 53). For most early psychologists, this social development and growth of the self took place in wide varieties of face-to-face interaction and dialogue that has characterized human evolution from the earliest times. Now, however, face-to-face interaction is but one of many modes through which we see ourselves reflected in the response of others. Whether mediated interaction inevitably suffers due to social cues being filtered out or the media allows forms of hyper mediation (Walther, 1996) that affords more effective means of social interaction, is at present an unresolved issue. However, there can be no doubt that mediated interaction has come to form a major role in supporting cooperative work, collaborative understanding, discourse, and individual growth, as media use consumes an ever-greater proportion of our daily lives.
Much social learning theory developed in reaction to the behaviorist notions that learning resulted only from direct exposure to reinforcements and punishments, and further from cognitive notions of individual knowledge acquisition. Albert Bandura and others argued that people learn a great deal without experiencing rewards or punishments directly but through vicariously observing the effect of these on others. Bandura (1977) wrote, “learning would be exceedingly laborious, not to mention hazardous, if people had to rely solely on the effects of their own actions to inform them what to do. Fortunately, most human behavior is learned observationally through modeling: from observing others one forms an idea of how new behaviors are performed, and on later occasions this coded information serves as a guide for action” (p. 27). Bandura further noted the necessity of opportunities for practice. This practice is best done in social contexts so that it can be refined through reaction and feedback from others.
Humans learn socially in many ways, and one of the oldest of these is imitation (Warnick, 2008). Aristotle argued, “To imitate is, even from childhood, part of man's nature (and man is different from the other animals in that he is extremely imitative and makes his first steps in learning through imitation)” (1997, p. 57). Imitative learning has most often been studied among infants, but models of technical and cognitive apprenticeship also celebrate the effectiveness and efficiency of learning by imitation. However, learning by imitation has historically been limited by both time and space. Geographic separation can be overcome to a limited degree by video and immersion, but time restrictions also occur in place-bound and traditional forms of imitation. Asynchronous imitation occurs when one models the behavior, consciously or unconsciously assesses the means of expression, the rationale, or the arguments of others as displayed in their asynchronous uttering. This modeling often occurs when responding to discussion or problem sets, to which the answers of others already serve as visible models.
Social learning looks to the authentic clues that arise from interaction with others in a specific context. In the everyday interactions of individuals, problems arise and through negotiation, acquisition of information, and reflection, these problems are resolved (Dewey, 1916). Learning is not only the accumulation of facts and the understanding of concepts but also is both induced and confirmed through interaction and discussion with others. Even when one is alone, the shared use of language, cultural concepts, signs, and symbols both afford and constrain our understandings and creation of knowledge (Brown, Collins, & Duguid, 1989).
Social scientists have long struggled to match the predictability of their laws of human behavior with those developed in the natural sciences. Cognitive and behavioral learning models have strong roots in empirical science, in which the discovery of generalized laws of learning that can be applied across contexts is a major goal. One of the popular attempts used in economics and game theory is to develop models where rational decision-making on the part of the individual is assumed. However, Buchanan (1985) notes that rational theories break down because people talk to one another, change their minds, and utilize both overt and covert efforts to change others. Thus the capacity to communicate with one another is an essential skill and, as we have discussed, has long been an important tool for learning. However, communication and learning (whether face-to-face or at a distance) are very complicated—influenced by a host of variables including context, skill, attitudes, and the form of mediation used to convey that communication. In later chapters we focus on ways that our conceptual model of social organization may reduce this confusion. We next turn to pedagogies that were specifically developed to benefit from our propensity and capacity to learn socially.
Social Constructivism
Constructivism of the non-social variety has deep philosophical and pedagogical roots, and has been associated in a learning context with the works of John Dewey, George Herbert Mead, and Jean Piaget. Like many popular theories, it has been defined and characterized by many, often with little consistency among authors. However, all forms of constructivism share a belief that individuals construct knowledge dependent upon their individual and collective understandings, backgrounds, and proclivities. Debate arises, however, over the degree to which individuals hold common understandings, and whether these are rooted in any single form of externally defined and objective reality (Kanuka & Anderson, 1999). Since much of constructivism is touted as driving the current educational discussion, it should be noted that it is a philosophy of learning and not one of teaching. Despite this incongruence, many authors have extracted tenets of constructivist learning, and from them developed principles or guidelines for the design of learning contexts and activities.
Drawing mainly from the work of Vygotsky and Dewey, social constructivist models of learning emerged in the early part of the twentieth century, though they were only adopted on a widespread basis by the academic community from the 1970s onward, after Vygotsky’s work was discovered in the West, and Dewey’s half-forgotten writings began to be reinterpreted in the light of a Vygotskian understanding (e.g. Popkewitz, 1998). From a social-constructivist perspective, knowledge and knowledge creation is a fundamentally social phenomenon. Not only are meanings negotiated and formed in a social context, the process of education is one where learners move from one zone of proximal development to the next, mediated by others who have already reached beyond where the learner wishes to go. In distance learning, social constructivist approaches were prohibitively expensive until the advent of affordable communications technologies. While there are many variants on the theme, social constructivist models share a number of common features that we outline in the following subsections.
Multiple Perspectives and Engagement that Includes Dialogue
Since knowledge is both individually and socially constructed, it follows that there must be opportunity, reason, and capacity for individuals to share, debate, and discuss their understandings. Individually, discussion is used to validate knowledge construction and to test its veracity against the understandings of others. Socially, groups of learners use one another to both amplify and dampen their understandings so as to construct understandings that are congruent—at least to the extent where cooperative action can be undertaken.
Learning in Authentic Contexts
If learning is to be meaningfully constructed, it must have worth for the individual learner. This value arises most easily if learning takes place in authentic contexts with genuine personal value that is perceived by the learner as both interesting and useful. Unfortunately, there are domains of knowledge that, in themselves, have little intrinsic meaning (at least for the majority of learners), but they are considered prerequisites for acquisition of more relevant knowledge to be studied at a later time. This focus on the prerequisite, regardless of its own authenticity or relevance, is typically over-valued by discipline-centered teachers, resulting in learners often being forced to ingest large quantities of information with little apparent value. Constructivist practitioners of authentic learning design activities that are wide-ranging enough so that their connection to the relevant “big picture” is apparent even at early stages of inquiry.
Inquiry and Problem-Solving
The inquiry and problem-solving features of constructivist learning emerge from the need for authentic contexts. Problems not only situate the learning in an authentic task-driven challenge but also provide motivation and focus to the learning process (Jonassen, 2002). This is especially important in collaborative learning where the diversity of interests, expertise, and aptitude may cause groups to move away from constructive problem-solving toward following the interests of dominating or particularly interesting diversions.
Learning is Open Ended and Ill-Structured
Most learning does not take place in classrooms, but in the real-life context of authentic problems situated in ill-structured environments (Spiro, Coulson, Feltovich, & Anderson, 1988). Thus constructivists prefer to situate learning problems in messier domains where there is no single comprehensive and correct answer. The ill-structured domain of the problem also stimulates discussion among learners as they attempt to construct a useful understanding of the domain and develop solutions to problems.
Cooperative and Collaborative Learning
Despite that fact most formal education takes place in group settings, very little of what goes on in traditional classrooms or online can be described as cooperative or collaborative. Rather, both teachers and learners usually conceive of learning as an internal cognitive process. Indeed, in many learning designs, students are set as competitors against one another, each striving for a limited number of high grades that will be allocated by the teacher.
Despite this individualistic orientation in current practice, there is a growing body of research demonstrating that cooperative and collaborative education not only results in greater learning but also is perceived by students as generally being more satisfying, and is associated with lower dropout rates. In a large meta-analysis of studies that included over 4,000 students comparing cooperative and collaborative learning to traditional individualized study, Springer, Stanne, and Donovan (1999) concluded that “students who learn in small groups generally demonstrate greater academic achievement, express more favorable attitudes toward learning and persist through science, mathematics, engineering and technology courses to a greater extent than their more traditionally taught counterparts” (p. 22).
There is an ongoing and generally inconclusive debate in the literature differentiating collaborative from cooperative learning. Generally, collaborative learning is considered to be less teacher-driven and more ill-defined than cooperative learning. Learners working collaboratively deliberately support one another's learning, negotiate the division of tasks, and help one another to learn by using and/ or developing group processes in more or less formal ways to produce some common or individual outputs. Cooperative learning tends to be based on more structural sharing. For example, students may research topics independently, or focus on parts of a broad topic and share them with others in the class. Although many writers and teachers use the terms interchangeably, we will be fairly specific in defining collaborative learning as a process where learners deliberately work together to achieve outcomes of mutual benefit, and cooperative learning as a process where independent learners do work that benefits themselves and other students. Despite sometimes contested differences, there is a great deal of common theory and practice in both collaborative and cooperative learning. These similarities include:
• A teacher who is usually more a facilitator or guide than a "sage on the stage"
• Teaching and learning as shared experiences
• Students participating in small group activities
• Students taking responsibility for their own learning and that of their group
• Students stimulated to reflect on their own assumptions and though processes; and
• Social and team skills developed through the give-and-take of consensus-building (adapted from Kreijns, Kirschner, & Jochems, 2003, p. 337).
In Springer et al.’s meta-analysis (1999), attempts to describe learning designs they investigated as either cooperative or collaborative and then comparing results revealed no significant differences in outcomes. However, the collaboration or cooperation reviewed in these studies took place in face-to-face interactions. In a smaller study comparing the two in online interactions, Rose (2004) found that groups characterized as cooperative achieved higher degrees of in-depth processing in a shorter period of time than those working collaboratively. This finding is consistent with our own experiences of online learning in which coordination, task clarification, assignment, and negotiation seems to take longer in online and especially asynchronous online contexts. Of course, such skills are themselves valuable, need to be learned, and may contribute to outcomes that are not intentionally measured.
Given the theoretical and empirical evidence supporting the use of collaborative and cooperative learning designs, one might reasonably ask why this model is not employed more often in formal education. The answers may lie in the social norms that privilege independence and individualism in many Western countries. However, there are also pedagogical, organizational, and technical problems that challenge collaborative design implementations. From a pedagogical perspective, many educators conceive of learning as an individual process, and assess it as such accordingly. The central role of assessment in institutional learning thus drives it toward patterns that emphasize the individual at the expense of the group.
Communities of Inquiry
Our final model of conventional social learning, Communities of Inquiry, is partly a systems theory and partly a model for analyzing learning transactions that both predicts and describes behaviors. It concerns the elements that are essential to the social educational experience. Explicitly concerned with group learning (see figure 2.1), it identifies three kinds of presence within a social learning transaction:
• Cognitive presence. The extent to which participants can construct meaning through reflection and discourse,
• Social presence. The extent of identification with a community and trusting inter-personal engagement, and
• Teaching presence. The design, facilitation, and direction of social and cognitive processes (Garrison, Anderson, & Archer, 2000, 2001).
We will return to the community of inquiry model in some detail later but, for now, note that it provides a way of understanding how learning occurs within a group setting, where a group of intentional learners and one or more teachers build knowledge together.
Figure 2.1 Community of Inquiry model (Garrison & Anderson 2003, p. 88).
The Connectivist Era
We argue throughout this book that the affordances of cyberspace offer new ways to approach all forms of human interaction and communication, including education. It is thus not surprising that new pedagogies and theories of learning have arisen that attempt to both explain and provide guidance to educators when teaching in net-infused contexts. There are many related theories that help to explain and recommend approaches to learning in networked contexts, outside the classroom. Each addresses a set of related concepts:
• learning is and should be unfettered by formal boundaries and delimited groups;
• learning is not just a feature of individuals, but of communities;
• learning is distributed not just in the heads of humans but in the tools, conceptual and physical, that they use, the artifacts they create, and the environments they build and inhabit;
• knowledge exists in a social and physical context as well as a personal one;
• structure and meaning can be an emergent feature of the dynamic learning system in which many individuals, loosely joined, can play a role in creating;
• diversity has value to the whole learning community, and individual differences should be valorized.
Since the late twentieth century, these themes have emerged from multiple disciplinary areas and, in sum, add up to a new and different way of thinking about learning. In making this assertion, we distinguish Connectivism (a theory created by George Siemens (2005)) from connectivism with a small “c,” which we use as a generic term for a family of network learning theories. Just as there are many different variations on social constructivism that share the unifying characteristics, so there are variations of connectivism that share the common properties of knowledge emerging from and within a network.
Foundational Theories For Connectivist-Era Models
In the following subsections we explore some of the theories and models that have informed the connectivist era. While incomplete as theories of learning or teaching in themselves, they are woven into a fabric of ideas that informs the two most distinctive connectivist learning theories, communities of practice and Connectivism itself.
Heutagogy
The principles (and naming) of heutagogy were first articulated by Australian educators Stewart Hase and Chris Kenyon (2000). Heutagogy (derived from the Greek word for “self”) is a direct result of self-determined learning theories and practice. Heutagogy brings these theories into a networked context by noting the ways in which the tools and resources for effective self-determined learning have been expanded exponentially through cyberspace. However, access to tools does not ensure that learners are capable of using them effectively. Thus, Hase and Kenyon also note the importance of capability in heutagogically based education. They write, “capability is a holistic attribute and concerns the capacity to use one’s competence in novel situations rather than just the familiar, a justified level of self-efficacy for dealing with novel problems, having appropriate values, being able to work in teams, and knowing how to learn” (Hase & Kenyon, 2007, p. 113).
Heutagogy also stresses the need for learners to understand their own learning processes. This reflective capacity allows learners to direct their own learning when needed—even in the absence of a formal education structure. Interesting as well is Hase and Kenyon’s (2007) distinction between competencies (the darling of many, especially vocational educators) and capability. Competencies are tested in known contexts and usually are focused backward on instruction already provided. Capability, however, looks to the future and celebrates the capacity to learn as contextually demanded. Increasingly, both workplaces and schools are changing rapidly, and thus the competencies acquired last year or last month may not provide the capacity to learn and apply that knowledge going forward in those environments.
Hase and Kenyon end their 2007 paper with a list of ways in which Heutagogical pedagogies are used to design learning processes applicable inside or outside of formal education. These capacities are magnified by the net-infused context in which collaboration, student input into content selection from vast open educational resources, self-reflection through tools like blogs, and greatly enhanced flexibility in where and when to learn are all afforded.
Distributed Cognition
The field of distributed cognition, originally developed by Edwin Hutchins (1995), is concerned with ways that the tools, methods, and objects we interact with may be seen as part of our thinking processes and extensions of our minds into the world. Rather than thinking of cognition as an internal process of thought, proponents of this perspective observe that memories, facts, and knowledge may be reified and embodied in objects and other people we interact with. In many cases, the environment places constraints on our thinking and behaviour, or influences us to think and behave in certain ways and, in many cases, is an integral part of thinking. Objects and spaces are participants in the cognitive process, not simply neutral things that we use, but an inextricable part of how we think and learn, both as individuals and as connected groups (Salmon & Perkins, 1998). S. Johnson provides a nice illustration of this: he talks of the successful landing of a plane damaged by geese as “a kind of duet between a single human being at the helm of the aircraft and the embedded knowledge of the thousands of human beings that had collaborated over the years to build the Airbus A320’s fly-by-wire technology” (2012, Introduction, Section 2, para. 10). Knowledge is not just held within the artificial intelligence that guides the aircraft—although the subtle interactions with the autopilot do play a role—but in the design of controls, seats, and other artifacts through which pilots, co-pilots, and others interact with one another and the vehicle (Hutchins & Lintern, 1995; Norman, 1993). Similarly, we as individuals offload some of our cognition onto the objects around us—the organization of books on a bookshelf, the things we lay out on our desks, the pictures on our walls, and the cutlery in our kitchen drawers, all act as extensions of our minds that both reflect thinking and engender it. As Churchill (1943) said, “We shape our buildings and afterwards our buildings shape us.”
Distribution not only applies to unthinking objects but also to us and the people around us: cognition is a social process where different people play different roles, leading to the distribution of knowledge within a group or network of people (Salmon & Perkins, 1998). A simple demonstration of this is the loss of cognitive capacity that occurs when couples split up or one partner dies. The remaining individual will have come to rely on their partner to remember things, perform activities from washing dishes to doing accounts, and vice versa, a process sometimes described as “socially distributed remembering” (Sutton, Harris, Keil, & Barnier, 2010). Whether in intentional organizations or looser networks, this socially distributed remembering allows us to do more and think further (S. E. Page, 2011).
Activity Theory
Most commonly associated with social constructivism but equally central to understanding connectivist models, activity theory emerged from the work of Soviet psychologists in the early-to-mid twentieth century such as Leontev and Vygotsky, who were attempting to find ways to explain how individuals and objects worked together as dynamic systems. The binding concept of an activity from which the name is derived is concerned with subjects doing things, typically together, engaging in activities through mediating objects or tools—be they physical or mental objects. It was elaborated on and brought to the West primarily by Engeström (1987) who added “community” to Leontev’s individual and object as a fundamental unit of interaction.
One of Activity Theory’s most distinctive features is its insistence that, in understanding the mental capabilities and learning of an individual, it makes no sense to treat an isolated person as a unit of analysis: the physical, cultural, and technical world that he or she inhabits is as much a player in any activity as the mental processes of the individual who engages in it. Activity Theory describes actions in a socio-technical system by considering six interdependent and related dimensions:
The object—the purpose of the activity
The subject—the individual actor
The community—the combination of all actors in the system
The tools—the artifacts used by actors
The division of labour—how work is divided and tools mediate the activity
The rules—things that regulate and guide the system
These interdependent parts are usually represented as a pyramid that illustrates their interactions (see figure 2.2).
Figure 2.2 Activity theory view of a human activity system (Engeström, 1987, p. 78).
Activity Theory is not predictive, but provides a framework for understanding the complex ways that humans interact with the world and one another through mediating artifacts. The main lesson to take from its sometimes arcane perspective on the world is that, if we are to understand the ways individuals behave in a social context, it is important to consider not just their mental processes, but their interactions with the entire activity system including, importantly, the physical and mental tools and processes that they use. Combined, they provide a way of understanding consciousness as a social phenomenon that extends into and is inextricable from the world, the tools, and the signs (notably language) that people employ. In a very real sense, tools mediate between people and the world, not as simple channels, nor as a means of achieving ends, but actively affecting how the world is experienced and perceived. This makes it highly relevant to the context of networked learning, in which interactions are mediated and objects play not just a supportive role, but an architectural one in learning.
Actor Network Theory
Like Activity Theory, Actor Network Theory (ANT) is concerned with systemic interactions of people and the objects that they use in their interactions. While sharing some of the terminology and related conceptual models of activity theory, Actor Network Theory emerged from a very different tradition and has a complementary agenda. Conceived by Latour (1987, 2005) and elaborated by Law (1992), it was created in the context of social practices in science and technology, mainly in reaction to sociological and technologically determinist views of the role of technology in society. Latour, in particular, sought a “scientific” way of describing the behaviors of people that avoided self-referential explanations of the “social,” therefore avoiding latent assumptions. His objective was to rebuild sociology from the bottom up without reference to what he saw as fuzzy or ill-defined terms that had bogged down the discipline, most notably eschewing use of the term “society” itself as a simple given.
Actors in an actor network may be human or non-human, with no special priority given to either. Instead, actors are constituted in heterogeneous relationships with one another: they form networks of related pieces that have no distinct edges. Given that such networks are continuous and unbounded, ANT helps educators to understand how some collections of actors may be thought of and considered as individual actors in their own right—for example, we can say things like “Athabasca University tops the league table of open universities,” or “the US invaded Afghanistan.” In the language of ANT, some networks may be black-boxed. In other words, we may choose to treat a complex network as a single entity, and to consider it in its relationships with others as a single actor.
Complexity Theory and Complex Adaptive Systems
Another notable feature of theories from the connectivist era is that they describe emergence and draw on the dynamics of complex systems. Complex systems are those where new and often unpredictable behavior emerges out of multiple interactions of entities, where the interactions are known and follow fixed rules: the weather, for instance, is a good example of this, as are rainforests and eddies in flowing water. Complex adaptive systems (CASs) consist of interacting entities that adapt in response to changes often brought about by other entities: evolution, ecosystems, cities, economies, stock markets, and termite mounds are good examples of these (Kauffman, 1995).
Educational systems may be thought of as CASs: while they are typically constrained by top-down governance and rules that determine the range of behaviors that can occur, there are many parts which are complex and adaptive, including learning itself, where patterns and behaviors emerge as a consequence of individual actors and their interactions. Once we enter the world of informal learning, especially in the context of networks and sets, patterns that emerge are almost always complex and adaptive but, even in the most controlled institutional learning contexts, educational systems are open, unbounded, and connected to human and natural systems. This is the problem that confronts any educational researcher who attempts to analyze the effectiveness of a given intervention: it is never, in principle, possible to control all the variables that may affect any learning transaction.
Emergent behaviors arise when autonomous yet interdependent agents interact with one another within a context that partly determines the possibilities of interaction, and that is itself warped by the interactions of agents within it. This means that one of the most important defining characteristics of all complex systems is that they are, at least at some scales, unpredictable. While we can recognize patterns and broad tendencies, it is theoretically impossible to predict any particular event. The famous “butterfly effect,” whereby the flap of a butterfly’s wing in one part of the world might cause a storm in another, was a term originally coined by Edward Lorenz to describe his work (Lorenz, 1963) on what would later come to be known as chaotic systems. Lorenz (1963) showed conclusively that, though an entirely deterministic system, the weather at any given time is impossible to reliably and accurately predict from a previous known state. That a butterfly’s or (in its original formulation) a seagull’s wing flap can affect weather systems on the other side of the planet is a captivating, mathematically provable if empirically untestable image. Such sensitivity to initial conditions is observable in far more mundane and commonplace events that we can more easily observe, such as the movement of individuals in a crowd, the patterns of drips from a tap or the cascades of sand on a dune. But hand in hand with unpredictability come large-scale emergent patterns, in which higher levels of order emerge from small-scale interactions, such as can be seen in everything from ripples on a pond to life itself (Kauffman, 1995). In an educational context, theorists look for and attempt to predict “transformations or phase transitions that provide the markers for growth, change, or learning” (Horn, 2008, p. 133).
If systems are complex and unpredictable, they are not easily explained by positivist researchers and educators who attempt to eliminate or control all the variables that affect a learning transaction. Rather, those with a perspective based on recognizing complexity seek social structures that allow effective behavior to emerge and evolve and ineffective ideas to be extinguished. Researchers in CASs seek to understand features of the environment, and especially social or structural norms or organizations that resist either overt or covert attempts at self organization. Such attempts to stifle emergence may be impossible and involve a large expenditure of effort. Horn argues that “the management of social organizations of all types has been maintained by control measures that work to block the capacity of systems to operate autonomously” (2008, p. 133). These blocking mechanisms were designed for educational systems so that learners can operate in close proximity with one another without becoming mutually destructive or descending to chaos. But these same control mechanisms can thwart the emergence of adaptive behaviors and phase shifts that provide potential for rapid and profound learning.
Implications of complexity theory for learning and education operate on at least two levels. At the level of the individual learner, complexity theory, like constructivist theory, supports learners’ acquisition of skills and power such that they can articulate and achieve personal learning goals. By noting the presence of agents and structures that both support and impede emergence of effective adaptive behavior, individual learners are better able to influence and indeed survive in often threatening and always complex learning environments.
At the organization level of either formal or informal learning, complexity theory points to the social structures that we create to manage that learning. There is usually some level of self-organization going on in all complex systems, brought about by a combination of diverse learners with diverse backgrounds, needs, interests, and a wide range of ways to interact with one another, their surroundings, teachers, and learning resources. Any schoolteacher who has experienced a wasp or a thunderstorm in a classroom of children will be familiar with the way that small perturbations can have large effects on the learning behaviors and activities that are occurring, no matter how well planned they might have been in the first place. Even so, most of us can recall occasions when poor and stultifying approaches to teaching still resulted in good learning, often because of interactions with other learners or the chance discovery of interesting learning materials.
Good teachers adapt and change behaviors as the environment, context, and interactions between learners change. However, the self-organizing facets of a learning system can work against this, making it an uphill struggle. Complexity theorists (e.g., Kauffman, 1995, p. 233) talk of different levels of orderliness in self-organizing systems: the “Red Queen” and “Stalinist” regimes. When there is too much chaos and unpredictability, systems are always running to stay in the same place, like the Red Queen from Alice Through the Looking Glass. Conversely, if there is too little dynamism and change, then things settle down to a fixed and unchanging point or set of points—the Stalinist regime. Neither is helpful in learning. From the point at which these management functions begin to inhibit the emergence of positive adaptive behavior or facilitate and sustain behaviors that are not conducive to deep learning, we can expect negative results. The emergence of complex self-organized behavior occurs between the realms of chaos and order, for which Doyne Farmer coined the term “the edge of chaos” (Langton, 1990). Organizational structures should help us to surf the edge of chaos, not eliminate or constrain the creative potential of learners and teachers. Further, this understanding can guide us to create and manage these complex environments, not with a goal of controlling or even completely understanding learning, but instead with a goal of creating systems in which learning emerges rapidly and profoundly.
Complexity theory also encourages us to think of learning contexts—classrooms, online learning cohorts, and so on—as entities in themselves. These entities can be healthy or sick; emerging, growing, or dying. By thinking at the systems level, reformers search for interventions that promote healthy adaptation and the emergence of cultures, tools, and languages that produce healthy human beings.
Learning designers following complexity models eschew the linear processes associated with much instructional design theory. Rather, they situate learning in contexts that are characterized by fluidity and turbulence, located near the edge of chaos, with rich possibilities for diverse actions and reactions, in complex contexts, and the presence of strange attractors, where order emerges from chaos. Most importantly for our study of networked learning, high-quality learning contexts are marked by “interconnectedness of and intercommunications among all parts of the system” (Laroche, Nicol, & Mayer-Smith, 2007, p.72). Thus, individual learning is enmeshed in the complex social experience and context of group, network, and collective social activity and culture.
Complexity theorists have drawn examples from many contexts to show the power and usefulness of emergent organizations and their capacity to thrive without total understanding, much less control, of the context in which they exist. Connectivist-era models of learning embrace this uncertainty and seek ways to utilize complexity without the potential drift to chaos that a lack of top-down organization might entail.
Connectivist Learning Theories
Two connectivist theories have emerged as central and archetypal. The first, with the longest history, is that of communities of practice and its successor, networks of practice. The second is Connectivism, as propounded by its creator Siemens, with contributions from his collaborator Stephen Downes.
Communities of Practice
The theory of communities of practice was established in the work of Lave and Wenger (1991) and fully expounded in Wenger’s seminal book, Communities of Practice (1998). Lave and Wenger sought to explain and improve upon learning that occurs outside of formal group-based courses, typically in the workplace or among co-located learners in communities. The theory describes primarily informal processes of community formation and growth, though much of Wenger’s more recent work has focused on approaches to deliberate fostering of such learning communities. The concept, drawn from anthropological studies, relates to how newcomers to a collection of people, such as a department in a firm, a university, or a group of charity workers, learn the group’s practices and become participants in the community. At first, Lave and Wenger used an all-encompassing notion of “legitimate peripheral participation” to describe the process of becoming a full member of the learning community, but Wenger’s later work unpacked this in terms of
• mutual engagement—the group-like formation of shared norms and methods of collaboration,
• joint enterprise—a shared set of goals and purposes, also known as the community’s domain, and
• shared repertoire—a set of resources, both physical and conceptual, that the community shares (Wenger, 1998, p. 73).
The concept of shared repertoire, in particular, echoes the notion of distributed cognition and sharply distinguishes this as a networked learning theory, in which both human and non-human actors in a network are mutually constitutive and joined together. Part of the value of the concept of a community of practice is that it treats learning as dynamic and situated, and describes ways that tacit knowledge spreads through a network, as opposed to the more formal methods of deliberate learning that may convey explicit and implicit knowledge, but do not (and, according to Polanyi (1966) cannot) succumb to explanation and formalization.
A particularly powerful aspect of the theory is its description and explanation of boundaries. In a conventional intentionally formed group, boundaries are defined easily: one either is or is not a member of the group, and there is usually a process involved in joining or leaving it. In the fuzzier realm of communities of practice, boundaries are typically emergent phenomena that arise out of shared practice, a bottom-up process resulting from the joint enterprise that naturally channels the community and separates it from others. Central to this idea is the importance of those who exist at or near the boundaries, and who cross them between communities of practice. Boundaries are spaces where learning is particularly likely to happen, because that is where different conceptual models are likely to clash or merge, where “competence and experience tend to diverge: a boundary interaction is usually an experience of being exposed to a foreign competence” (Wenger 1998, p. 233).
The divergence can be both creatively inspirational and a cause of conflict. Wenger’s boundary-crossers may be networked individuals who move beyond and between closed communities, cross-fertilizing each community with ideas and practices of others. There may be more or less concrete boundary objects, including symbols and metaphors that are technological connectors like social software platforms and the processes enabled through them, which act as a means to bridge different communities. Communities thus become networked by boundary-crossing in order to play the role of one another’s teachers, spread knowledge within the community, and also engender changes in knowledge in other communities.
Models and interventions based on communities of practice have been widely adopted in many sectors. The concept is not, however, without its problems. First, the term carries multiple terminology and disciplinary understandings associated with the word “community.” Second, different researchers often understand the degree of formality of the “practice” differently. The “community of practice” label has been applied to emergent, informal, and spontaneous organizations of face-to-face professionals, but it has also been used to describe managed professional development activities which almost preclude only voluntary participation. Schlager & Fusco (2004) use the term extensively to define, and Wenger’s theory to describe, online educators’ forums (such as TappedIn); yet after years of studying this rather large community of practice, “the question of whether the users of the TappedIn environment collectively constitute a community or practice remains unresolved” (Schlager & Fusco, 2004, p. 121). In many ways, the blurring of the term has led to it being hijacked by those who are more fixed in a social-constructivist model of the world, so although communities of practice are, in the way Wenger first described them, in the vanguard of the connectivist era of learning theories, they still have one foot firmly planted in older models of learning.
Networks of Practice
Perhaps because of the fuzzy borders between networked and grouped ways of thinking of communities of practice, Wenger, Trayner, and Laat (2011) have extended the notion of communities of practice for the networked age, taking advantage of more recent work that treats networks and groups as distinct and separable social forms (e.g., Downes, 2007; Rainie & Wellman, 2012; Siemens, 2005). Although Wenger’s earlier work did describe ways that knowledge spreads through a network, he did not explicitly distinguish between intentional groups and the broader, looser spread of network connections. In this more recent work, Wenger et al. make the distinction between communities (what we call “groups”) and networks. Because networks do not have a specific domain or shared enterprise, they differ from communities of practice in some important ways:
The learning value of a network derives from access to a rich web of information sources offering multiple perspectives and dialogues, responses to queries, and help from others—whether this access is initiated by the learner or by others. On the one hand, because of personal connections, networking enables access to learning resources to be very targeted—whether one sends an email query to a friend or decides to follow someone’s Twitter feed. On the other hand, because information flows can be picked up, interpreted, and propagated in unexpected ways, they traverse networks with a high level of spontaneity and unpredictability. This potential for spontaneous connections and serendipity—and the resulting potential for collective exploration without collective intention or design—is a key aspect of the value of networks for learning. (Wenger et al., 2011, p.12)
While communities/groups are concerned with building a shared identity and fostering trust and commitment, networks, if they can be said to be concerned with anything at all, are about fostering and optimizing connectivity. Because networks are emergent features of connections with others, this concept is far more blurred and hard to grasp than it is in the context of groups, especially as those who are part of a network may not even be able to see the network, let alone view or affect aspects of its structure. Nonetheless, Wenger et al. identify a wide range of indicators to identify value within networks and make tentative steps toward identifying how such value may be reified through structured storytelling. This approach carries with it an underlying assumption that the networked learner is concerned with meaning-making in a constantly shifting, dynamic context. It is a process in which the creation of value is linked to the creation of content; the process of navigating a network and interacting with others in it is a process of learning in and of itself.
Connectivism
George Siemens coined the term Connectivism. In his 2006 book, Knowing Knowledge, he described it as “the integration of principles explored by chaos, network, and complexity and self-organization theories” (Siemens, 2006, p.30) Like Heutagogy, and drawing on the conceptual underpinnings of distributed cognition, actor-network theory, and communities of practice, connectivism assumes a context connected through pervasive networks that link not only individuals but also machines and resources as well. Siemens (2005) articulated eight oft-quoted principles of connectivism:
• Learning and knowledge rests in a diversity of opinions.
• Learning is a process of connecting specialized nodes or information sources.
• Learning may reside in non-human appliances.
• Capacity to know more is more critical than what is currently known.
• Nurturing and maintaining connections is needed to facilitate continual learning.
• The ability to see connections between fields, ideas, and concepts is a core skill.
• Currency (accurate, up-to-date knowledge) is the intent of all connectivist learning activities.
• Decision-making is itself a learning process. Choosing what to learn and the meaning of incoming information is seen through the lens of a shifting reality.
Connectivism shares many of the attributes of constructivism, notably in its valorization of diversity and a philosophical basis that knowledge is constructed in a social context. Like Heutagogy, Connectivism values capacity over what is currently known and proposes students learn how and what to learn and have input into this process.
Connectivism draws heavily from distributed cognition and actor-network theory in its view of learning in non-human appliances. This is about the traces that we leave in our networked lives, the artifacts through which we build and share knowledge and create new ideas, the tools and objects we offload cognitive functions to and think with. From the first time humans scrawled signs and images on cave walls or in the dirt, they were offloading part of their intellect into external space. Like those who rail against Wikipedianism and the Googlization of society today, Socrates saw this as problematic, as Plato relates in Phaedrus on the subject of the invention of writing:
The specific which you have discovered is an aid not to memory, but to reminiscence, and you give your disciples not truth, but only the semblance of truth; they will be hearers of many things and will have learned nothing; they will appear to be omniscient and will generally know nothing; they will be tiresome company, having the show of wisdom without the reality. (Plato, trans. 1993, pp. 87-88)
Notwithstanding these dangers, this offloading enables us not only to stand more easily on the shoulders of giants but also on the shoulders of our peers, and to enable them to stand on ours.
Connectivism also acknowledges the speed with which knowledge expands and changes in net-infused societies. By being connected to both other humans and knowledge resources, we retain currency and benefit from the diversity of ideas and cultures that abound. Through our awareness and maintenance of these connections, we become able to create new connections, resolve problems for ourselves and others, and thus become truly networked lifelong learners.
There are some aspects of Connectivism—the theory itself, rather than the family of theories—that we remain unconvinced by. Siemens and particularly Downes have taken it to be a complete theory of learning, following from connectionist views of psychological reality, in which networks like the Internet and our social networks of knowledge are directly analogous to connections that we make in our brains and, ultimately, the synapses of which they are comprised (Downes, 2008a). While there are some strong topological similarities between these networks, there are also strong topological similarities between them and the patterns of flu virus epidemics and song charts (Watts, 2003), but this does not make them qualitatively similar. Connectivism presents one of the most compelling theories of the networked era of education, but it is, as its authors are happy to admit, a work in progress that provides a blueprint for others to follow, rather than a bible that must be adhered to in every respect.
The Holist Era?
No single generation of learning has ever superseded the last. Like all technologies, learning technologies evolve by assembly (Arthur, 2009) and incorporate and extend what came before. One does not need to look far to discover plentiful examples of each generation, often coexisting in the same course or set of learning transactions.
Connectivism as a theory in itself, as opposed to a collection of related theories, has been criticized on many fronts. Some suggest that it is not a theory at all (Ireland, 2007) but the more substantive critiques mostly relate to its notable inefficiencies (Kop, 2011; Kop & Hill, 2008; Mackness, Mak, & Wiliams, 2010). The vast majority of people who start out taking explicitly Connectivist courses, typically run as MOOCs, fail to finish them. However, the concept of “finishing” is itself not entirely relevant to connectivist learning. Its explicit emphasis on emergence rather than planned learning means that it is hard to measure whether targets have been reached at all, much less with efficacy, and perhaps more disconcertingly, it is far from clear whether the resulting learning might have been more effectively or efficiently achieved in some other way. In response to these and other criticisms as well as opportunities afforded by new technologies, there has been an evolution toward a more holistic model that incorporates all earlier models of learning, including connectivist models. We have christened this the ‘holistic generation,’ in recognition of the fact that it encompasses all earlier models.
Holistic approaches to learning are agnostic as to method. Drawing from connectivist and older models, they valorize diversity and the socially distributed cognition afforded by the read-write Web and other publishing models, accepting that every learning experience is unique, and every learner’s needs are different. Connectivist approaches, for all their extensive reliance on networks of people engaging socially, are at heart focused on the individual—specifically, the individual’s learning. Holistic models embrace the fact that it is sometimes more important that a group learns, rather than an individual, especially in collectivist cultures (Potgieter et al., 2006). Holistic models recognize that, sometimes, guidance is what is most needed, that people can learn without direct engagement with others and, even that transmittive instructionist models of teaching have a place.
The current generation of large-scale MOOCs provide a good example of this. Courses from the likes of Coursera and Udacity tend to follow a highly instructivist model but, because of their size, spawn networks and study groups of learners who meet face-to-face, and through various social media such as Facebook, to enhance and support one another using quite different and more connectivist approaches. To support diversity and maintain the right amount of coherence for any given learner, holistic approaches are, like connectivist methods, heavily reliant on technologies. In particular, they make use of tools that can aggregate the actions and behaviors of many people in order to help make sense of a topic for those that follow. Social and learning analytics, collaborative filters, recommender systems, reputation management tools, and social adaptation systems are used to counter the torrential flow of information and plethora of connections that characterize the connectivist process. We will discuss most of these in greater detail later in this book, but for now, note that one of the main features of such systems is that they use, directly or indirectly, the diverse knowledge and actions of a crowd.
Theory of Transactional Distance
Beyond broad families of learning theories, the theory of transactional distance has been highly influential in distance learning teaching and research. It is a theory of instruction rather than learning, and it was developed within the specific context of distance education programming. Like activity theory, ANT, and complexity theory, it is a systems theory that looks at the interactions of agents and the effects that those interactions have on the behavior of the system. As noted previously, social learning takes place in both formal and informal settings and in distance, classroom, and blended contexts. Nonetheless, it is perhaps most powerfully apparent when it operates beyond the limitations of time and space as a means of supporting distance education and distributed learning.
Moore (1993) attempted to develop a theoretical model that addresses both structured instructivist and dialogic social-constructivist models of distance education, and provides guidelines for creating mixtures of the two. Moore argues that the “distance” in “distance education” should be considered not in either geographic or temporal terms, but as a psychological and communications gulf between learner and teacher, measured on a continuum of structure and dialogue. The basic tenet of the theory is that a negative, “transactional distance” separates learners and instructors from one another and learners from the content they wish to master. This is not to suggest that high transactional distance necessarily leads to poor learning outcomes, but merely that there is greater transactional separation between learner and teacher.
Moore (1993) postulated that there are three dimensions of transactional distance—structure, dialogue, and autonomy. Structure refers to the degree of activity, learning outcome, media, and content selection that is prescribed by the instructor or delivery institution. Dialogue is the interaction between and among students and teachers, determined by factors such as the number of students in a given class, the degree of familiarity and cultural understanding among participants, the nature of learning activities engaged in, the immediacy of the technologies employed, and the sense of integration and identification with the educational institution, content, and other participants (Tinto, 1975). Autonomy is “the extent to which, in the teaching/learning relationship, it is the learner rather than the teacher who determines the goals, the learning experiences, and the evaluation decisions of the learning programme” (Moore, 1993, p. 28). Autonomy is dependent upon the self-discipline, existing knowledge, and self-motivation needed by learners to thrive in contexts that are not completely prescribed by external agents (teachers and rigid curriculum). As Candy (1991) observes, self-direction is a variable quantity that shifts in different contexts and is influenced heavily by external stimuli.
The educational designer has an opportunity to manipulate the structure and amount of dialogue in the learning sequence. High and low levels of each variable present educational opportunities in four quadrants, measured according to the degree of structure and dialogue found within them (Kawachi, 2009).
Figure 2.3 Transactional distance quadrants (adapted from Kawachi, 2009).
As illustrated in figure 2.3, there are many potential classic forms of formal and informal study that are associated with each of the quadrants. However, each learning context results in more or fewer restrictions on student freedoms, and each is associated with different degrees of scalability, speed of production, direct and indirect costs, and other variables. Rather than dispute the value of intense interaction as advocated by proponents of collaborative and cohort models of distance education (Garrison, 2000) or celebrating the autonomy offered by individual study (Holmberg, 1986), Moore’s transactional distance theory (1993) helps us create models that trade off the advantages of both. Anderson has argued for an equivalency theory that postulates “deep and meaningful formal learning is supported as long as one of the three forms of interaction—student–teacher; student–student; student–content—is at a high level. The other two may be offered at minimal levels, or even eliminated, without degrading the educational experience” (2003, p.4). Thus, tension exists between developing formal learning programs that decrease transactional distance by increasing interaction and decreasing prescriptive activity, and providing access to educational experience that is of both high quality and affordable cost.
This accords with Moore’s own view (1993) that effective learning may occur whether transactional distance is high or low: structure or dialogue may be used effectively to improve learning. However, Saba and Shearer (1994) have demonstrated a system dependency that implies the more there is of one, the less there is of the other. As structure increases, it reduces the opportunities for dialogue, and as dialogue increases, it breaks up any intended structure. For example, a broadcast video lecture, one of the most highly structured forms of teaching, offers no opportunities at all for dialogue, at least while the lecture is playing. Conversely, a web meeting equivalent of the same lecture, if chat or audio are enabled for participants, allows participants to interrupt, ask questions, seek clarification, and change the pace or direction of the speaker. As a consequence, the event becomes less structured. At its most extreme, a dialogue between multiple participants may exhibit nothing but emergent structure.
Transactional Distance in Crowds
While Moore’s theory (1993) applies well within a traditional formal distance learning setting and has been verified and applied many times (e.g., Chen & Willits, 1998; Lowe, 2000; Stein, Wanstreet, Calvin, Overtoom, & Wheaton, 2005; Zhang, 2003), its applicability outside this setting, especially when social forms beyond the traditional dyadic or group modes of engagement are in play, is less clear. Transactional distance in social spaces that are not tightly controlled by a teacher is a complex phenomenon, whereby the teaching role may be distributed, anonymous, or emergent as a consequence of behaviors in a crowd, where the learner may be a contributor and active shaper of activities and content. We will be arguing that the concept applies differently under such circumstances and that, though the dynamic between an individual learner and teacher strictly obeys the inverse relationship between structure and dialogue, there are ways to bypass the problem when the teaching role is embodied in a crowd.
Moore (1993) does not distinguish between the communications gulf and psychological distance in their roles as definers of transactional distance, but as we have explored the range of ways that transactional distance operates within our typology of social forms, we have come to realize that the two aspects, communication and psychological gulf, are entirely separable. In some forms and tools used within new social media, it is possible to be in close and constant two-way communication without significant psychological attachment, without closeness to another human being. Less commonly, there may be a sense of closeness without significant two-way communication. Both psychological connection and communication are important aspects of what creates distance. For minimal transactional distance, negotiable control, rich communication, and a feeling of closeness (in a psychological sense) are all important. Reducing any one of these increases transactional distance, and each variable is potentially independent of the others.
Transactional Control
Like author Anderson, author Dron (2007b) has also examined Moore’s theory of transactional distance (1993) and found equivalencies: most notably, that the “distance” of which Moore speaks is actually composed of two distinct and largely independent variables. On the one hand, transactional distance is a mental phenomenon, a measure of the psychological and communications gulf between learner and teacher. On the other, it is a systems phenomenon that may be more precisely defined as an issue of control, which explains much of the negative correlation between dialogue and structure observed by Saba and Shearer. Both psychological/communication and control aspects are important, but they operate independently from each other.
From a systems perspective, when transactional distance is high, learner control over the learning transaction is lower and teacher control is higher, the teacher or teaching presence largely determining the learning trajectory. Through dialogue it is possible to negotiate control, and thus lower the transactional distance. The more dialogue there is, the more control is distributed among the participants. For example, learners can ask questions, seek clarification, express confusion, boredom, or interest, thus changing the path of the learning trajectory. The third dimension of Moore’s model (1993), autonomy, equates to the learner having control over his or her learning trajectory, requiring neither structure nor dialogue. Dron’s theory thus unifies the three variables identified by Moore in transactional distance theory by treating all as part of a continuum of control, from autonomous learner control through negotiated control via dialogue, to teacher control through structure (Figure 2.4).
Figure 2.4 The relationship between transactional control and transactional distance (adapted from Dron, 2007, p.32).
Most real-life learning transactions occur at some point along the continuum of complete learner control through to complete teacher control, and they seldom if ever occur at the extremes. Even in the most regulated transactions learners may choose to tune out, switch off, and will always reinterpret or construct their own understandings; conversely, even the most autonomous of learners will usually allow some of their control to be taken away by narratives provided by the author of a book, director of a video, or creator of a website.
Cooperative Freedoms
It is valuable to unpack the notion of control a little further, as it is of some significance in all forms of learning, especially in a social context. Garrison and Baynton (1987) provide the important insight that control is not simply a question of choice. In order to make effective learning choices, the learner needs independence—which, as Candy (1991) shows, is a highly situated and context-sensitive variable—power (the capacity to exercise that independence), and support (the tools, people, and processes needed to implement that power). However, for the idea to have any meaning at all, it is necessary to know some of the constraints and factors over which learners may exercise control. If control is an important aspect of an educational transaction, we need to understand the nature of what can be controlled in a learning process. Morten Paulsen’s theory of cooperative freedom (2003) describes a range of possible freedoms that might be available to a learner in a formal learning setting. His hexagon of cooperative freedoms (see figure 2.5) describes six dimensions:
• Place: freedom to choose where one learns
• Time: freedom to choose when one learns
• Pace: freedom to choose how fast or slow one learns
• Medium: freedom to choose the media used for learning
• Access: freedom to learn regardless of qualifications or extrinsic obstacles
• Content: freedom to choose what one learns
Figure 2.5 Paulsen’s model of cooperative freedoms (adapted from Paulsen, 2003).
Paulsen’s cooperative freedoms provide a fairly complete picture of freedoms in a formal, institutional learning context. However, there are gaps, and it does not describe well the different models of formal learning, such as those in connectivist transactions, or less formal learning environments. To Paulsen’s list of six dimensions, T. Anderson (2005) added the freedom of relationship, that describes the ability to choose with whom and how one engages with others, an essential freedom if we are concerned with social learning. Related to freedom of relationship but distinct from it is the freedom of disclosure: deciding to whom one discloses one’s communications. This is concerned with privacy and is of some significance to learners who may be fearful of displaying their ignorance. Disclosure is a more pronounced problem when entering the public realm rather than a closed group, although the commonplace requirement for students to engage with others in a group can also greatly limit this freedom.
In addition to these freedoms, Dron (2007a) has observed that there is also a meta-freedom to choosing whether or not and when to choose: the freedom of delegation. To be in control of one’s learning, it is essential to be able to submit to the control of others when we do not ourselves have sufficient knowledge, experience, or time to decide what and how to learn next. Another freedom that is not quite addressed by Paulsen’s “medium” is the technology used to present content. There is a world of difference between text presented on a mobile phone and text presented on a tablet or large screen, even though the medium may be considered the same. It is useful and, from a learner’s perspective, valuable to distinguish between media and the technologies used to deliver them. We therefore add “technology” to the list of freedoms. We might use the word “tool” instead, but the popular term “technology” makes it more easily understood in this context.
While our newly added freedom of technology might be comfortably stretched to cover the pedagogies and processes of learning, it may also be valuable to consider the freedom of method as a separate category. This requires a little justification. There are many ways that method is inseparable from technology. Indeed, a full definition of any technology must include both the methods and any tools it may employ, and in some cases, the method is the technology. There are strong arguments suggesting that pedagogies, for example, should be treated as technologies (Dron, 2012). However, especially in a learning context, it remains valuable to think of methods separately, especially when we are talking about pedagogies, particularly as populist definitions of technologies tend to focus on the physical tools such as whiteboards, desks, cellphones, and computers, rather than what makes them into technologies. So, although we believe that any full definition of a technology must include both the tools of which it is comprised and the ways they are used, the two are often separable in popular understanding. One can, for instance, use the same tool in many different ways, employing different methods.
Of all Paulsen’s freedoms, “access” stands out as being beyond the potential control of an individual or teacher. Access may be denied, for example, due to a lack of qualifications, but can be equally due to limited experience and prior knowledge. This is not a matter of personal choice, or if it is, it is at an entirely different scale from the other freedoms, sometimes relating back to choices made years or even decades ago. Access is not only about prior learning, it also relates to the availability of technology and the ability to use it. However, these are both covered by other freedoms—the freedom to choose an appropriate technology or medium, and the choice of method. Thus, while access is a very important issue, especially in formal learning, it is of a different kind and/or scale to the other freedoms and is not easily controlled by the learner; we therefore exclude it from the list.
This leads us to our own decagon of cooperative freedoms, extending and adapting those identified by Paulsen, illustrated in figure 2.6:
• Place: freedom to choose where one learns
• Time: freedom to choose when one learns
• Pace: freedom to choose how fast or slow one learns
• Medium: freedom to choose the media used for learning
• Content: freedom to choose what one learns, from what source
• Technology: freedom to choose the tools with which one learns
• Method: freedom to choose the approach and pattern of learning
• Relationship: freedom to choose with whom one learns and how to engage with them
• Delegation: freedom to choose whether and when to choose
• Disclosure: the freedom to decide what and to whom it is revealed
Figure 2.6 Decagon of cooperative freedoms (adapted from Paulsen, 2003).
Mirroring Moore’s theory of transaction distance, cooperative freedoms are, in many cases, inversely related to one another, though due to the number of freedoms under consideration and the ways they can interrelate, the relationships are more complex. Of particular note, many forms of social learning and freedom of relationship affect and are deeply affected by pace. If we are learning in direct dialogue with others, then the pace of interaction is strongly related to the pace of learning: we have to wait for responses, to work in synchronization with others. Similarly, social interaction may place limits on the potential times and places that learning transactions can occur, as well as the medium, technology, and method used. Likewise, if constraints are placed on relationships, then this may affect freedom of disclosure: for example, if engagement is required as a classroom activity. There is a constant and ever-shifting interplay between constraints and affordances in any sequence of learning transactions, in which technologies, pedagogies, physical and temporal constraints, financial imperatives, prior learning, future needs, methods, and media all help to determine the actual learning path that will be most useful or practical.
Conclusion
Social learning mediated and enhanced on digital networks has much in common with other models of learning, teaching, and associated instructional designs and pedagogies. Ideas and learning activities can be extracted from these other contexts and applied effectively in networked contexts; thus, there is value in extracting ideas and testing their efficacy in them.
In this chapter, however, we have focused on the main families of learning and educational theories that we believe are most directly relevant to the emergent context of networked learning. None of these are exclusive: the most rigidly behaviourist methods of learning have a social context and application, and may be found within learning trajectories that use social constructivist and connectivist models without negating the benefits of either. Connectivist learning often blurs into social constructivist modes as part of the emergent whole, and transactional distance provides a useful way to measure the varying quantities of control and social engagement at any point along the journey. | textbooks/socialsci/Education_and_Professional_Development/Teaching_Crowds_-_Learning_and_Social_Media_(Dron_and_Anderson)/02%3A_Social_Learning_Theories.txt |
A TYPOLOGY OF SOCIAL FORMS FOR LEARNING
The beginning of wisdom is to call things by their right names. Chinese proverb
The Internet era forces each of us to deal with an often bewildering and continuous set of technology-induced changes. When an infrastructure of powerful computational and communications tools is matched with a ubiquitous communication network, the stage is set for rapid innovation. Some of these innovations are sustaining and help us to communicate, play, and learn more effectively using familiar ideas and behaviours. Other innovations are disruptive—forcing users to go outside the economic and social boundaries set by previous technologies and pedagogies to use them effectively (Christensen, 2008; C. Christensen, Horn, & Johnson, 2008). Learning, however, is universal, and thus humans invent means and applications to use both disruptive and sustaining technologies to enhance their lives and those of others on the planet. In this chapter we introduce an organizational scheme, or heuristic, designed to create a conceptual home for both sustaining and disruptive networked technologies—and those with elements of both when applied in particular contexts.
We developed this guiding heuristic for learning and education in 2007 (Dron & Anderson, 2007) and it has been used in our work, and by others, to help make sense of the changing social patterns in learning that cyberspace has engendered (e.g., Buus, Georgsen, Ryberg, Glud, & Davidsen, 2010; Conole, 2010; Dalsgaard & Paulsen, 2009; Gray, Annabell, & Kennedy, 2010; Kop, 2011; Ryberg, Dirckinck-Holmfeld, & Jones, 2010; Thompson, 2011).
Though it proved to be of some value in its original form, we have since modified and refined our model for clarity and explanatory power. In brief, the evolved form illustrates three kinds of aggregation of learners in either formal or informal learning: groups, networks, and sets. We originally conflated sets with a further emergent entity that is not a social form as such, which we have referred to as the collective. The collective is an embodiment of collective intelligence, and it plays a binding and, in many cases, extremely active role in enabling social software systems to do things that were difficult or impossible in the past. Collectives are not a social form, but an emergent actor that arises from actions taken by people in a crowd.
To distinguish these forms, it may help to think of an example drawn from everyday life. Imagine that you are sitting in a café in the square of a busy city. Around you is a teeming multitude of people—the set of people in this part of the city. You do not know who they are, and they are not part of your social network though you may be learning things from them, such as whether it is raining or not: you might, for example, note how many are carrying unfurled umbrellas. As you look around, you see subsets of this set: men, women, children, people dressed in red coats, people running, people going to work. Some of these people come in groups—families, friends, classes of schoolchildren—that share a purpose and are, in some way, coordinated in their movements and activities. They may be there for the purpose of learning together: children on field trips, surveyors mapping out the land, or tourists being shown the sights of the city. Every now and then you see people running into friends, colleagues, and people they know. Strung between the people in the crowd are networks, exchanging information and co-constructing knowledge. Then you notice a cluster of people forming, gathering around a street entertainer performing in the middle of the square. No one has organized the gathering—a small crowd seems to attract more members, as though there were an invisible force pulling them together, a leaderless form of coordination, an emergent order: a collective. The crowd is acting as a signal for others to join it, playing a role not unlike that of a teacher telling a class to pay attention to some reading or performance.
Figure 3.1 illustrates the three social forms for learning, representing the fact that there is a continuum between the forms, each blurring into the next.
All of these social forms are bound by common attributes of sharing and communication that can contribute to the learning of others. Collectives, a particular form of collective intelligence, can emerge from any or all of these social forms and are characterized by algorithmic aggregation, filtering, data mining, clustering, and pattern-matching. These algorithmic processes may be internal to crowd members (e.g., responding to others in a crowd) and/or externally imposed, typically by computers (e.g., recommender systems) but sometimes by individuals (e.g., people who count votes in an election).
Figure 3.1 Social forms for learning: Sets, nets, and groups.
Our model is derived from our observations about collections of learners and how they benefit from one another’s knowledge and actions. While these social forms can and do exist in contexts other than learning, it is not our intention to provide a complete model of human society, or to suggest that the model would be useful in all other contexts. This model is useful because, as we will demonstrate in the ensuing chapters, it helps to make sense of not only how social learning occurs in traditional educational settings but also how the different ways that we can connect using cyberspace technologies may contribute to our learning trajectories in informal and personal settings. These social forms can and do exist in many circumstances beyond learning, and we will from time to time provide examples of their use in other contexts in order to help illustrate what we mean, but it is not our intention to tread outside the boundaries of a learning context in applying this model.
Individuals
Before we move into the realm of truly social forms that involve multiple participants, it is important to observe that much learning involves only the most tenuous links between people. When we as individuals read a book, paper, web page, or news feed, transactional distance is extremely high. However, even for the most solitary of learners, other people are necessarily involved in the learning transaction as authors and creators of content. In many cases, this involves a form of guided didactic conversation (Holmberg, 1986) in which the learner engages in internalized dialogue with the very distant tutor. Even where this is not the case, the author’s voice may be apparent and there is a strong sense that almost every learning process involves, at one or more steps removed, another human being. At a small scale, all textual communication and many that use voice, video, or avatars include a process of turn-taking in which we read/absorb and, potentially, respond. The difference for the individual learner is that the possibility of an ongoing exchange is not available.
Dyads
In 1984, B. S. Bloom famously posed the 2-sigma problem, referring to the finding that an average student tutored one-to-one performed two standard deviations better than an average student tutored using conventional one-to-many instructional methods. We are a little skeptical about the validity of the assessment used to take this measurement, since such objective-driven testing does not reveal all of the learning that may have occurred in a transaction, and does not look at creative gains or serendipitous discoveries that may have been made in larger groups or with different methods of learning and teaching.
However, the general point is hard to ignore: when compared to traditional institutional educational forms, where the goal is to transfer replicable knowledge, one-to-one tutoring works extremely well. Since Bloom’s original challenge, one-to-one tutoring (assuming appropriate methods are applied) has remained the gold standard for effective instruction, and no other teaching model has consistently reached or bettered the same 2-sigma improvement that results from it. Unfortunately, one-to-one tutoring is very expensive and, in formal learning, only common in a limited range of situations such as Ph.D. mentoring, project work, and personal tutoring. More than that, there are gains to be had from a diversity of perspectives, heuristics, interpretations, and predictive models that may be found in a large number of people (S. E. Page, 2008).
Though a pair of people communicating may be seen, in some ways, as a very small network or group, one-to-one conversation is different from other forms of learning conducted with more than one person. Rainie and Wellman (2012) observe that as soon as a third person is introduced, the potential for coalitions arises, and the persistence of the group no longer stands or falls on the actions of a single individual: if one leaves, interaction does not necessarily cease. Greater numbers have many other benefits that differ from dyadic communication in scale, if not in kind. Diversity increases with more people, allowing greater types and levels of interaction to occur, providing multiple perspectives, different interpretations, heuristics, and predictive models (S. E. Page, 2008), all of which can contribute to learning: more possibilities mean greater breadth and depth of discourse, more creative opportunities, and better problem-solving capacity.
For all the benefits of many individuals learning together, from a learning perspective dyadic communication typically affords the greatest possible level of freedom of delegation for the learner: the tutor can respond directly to questions, adapt teaching to the learner’s stated or implied reactions, and the learner can choose whether to intervene in the course of his or her own tuition without contest with others (Dron, 2007a). Although it may occur in the context of a large group, a great deal of dyadic communication underpins most forms of social learning, from email exchanges to telephone conversations, face-to-face mentoring to instant messages. While the title of this book makes it clear that we are mostly concerned with learning in larger groups, one-to-one dialogue represents an “ideal” form of guided learning, at least where there is a teacher who knows more than the learner and is able to apply methods and techniques to help that learner to learn. It continues to play an important role in network forms of sociality because of the essentially oneto-one edges between nodes that lead to what Rainie and Wellman (2012) refer to as “networked individualism”—a focus on an individual and their many one-toone connections with others. It is also an important form in sets, where we may interact with an unknown other in the same direct way.
Groups
The most familiar social form in an educational context is the group. In a formal educational context, these are just a few of the common forms that groups may take:
Classes Schools Administrative departments
Tutorial groups Colleges Panels
Seminar groups Committees Special Interest Groups
Cohorts Working groups Study groups
Divisions Workshops Sports teams
Centres Conferences Playground gangs
Faculties Project teams Houses
Universities Academic departments Year groups
Learning technology groups Research groups
Boards of governors Senior management teams
Each of these groups may be more or less formally constituted, and each can play a role in the learning experience for anyone affected by them. Groups are cohesive: they are identifiable as distinct entities with existences of their own that are, in principle, independent of their members. However, one of their defining characteristics is that their members are, in principle and often in practice, listable. Groups often have formal lines of authority and roles, such as a designated chairperson, team leader or teacher, enrolled student, and so on, with implicit and/or explicit rules that govern behaviour and structure. They are structured around particular tasks or activities that may be term-based or ongoing, and institute various levels of access control to restrict participation, review of group artifacts, or transcripts to members, providing a less public domain. Groups often have schedules: members frequently use and create opportunities to meet faceto-face or online through synchronous activities, and their modes of interaction are typically many-to-many or one-to-many.
Nets
Our second major social form is the network. The distinction between groups and networks that we employ is a common one, used by many researchers in the field as well as in fields like community studies, sociology, and community informatics (e.g., Downes, 2007; Rainie & Wellman, 2012; Sloep et al., 2007; Wenger et al., 2011). Networks consist of nodes—such as people, objects, or ideas—and edges, the connections between them. In the social form of a network, networks connect distributed individuals and groups of individuals, one node and edge at a time. They are not designed from the top down, though we may create channels that make their emergence more likely. Instead, they evolve through our many and varied interactions with others. Entry and exit to networks is usually simple— we connect in some way with another person, or we don’t: although we might occasionally cut our ties with other individuals, for the most part it is enough to simply not engage with someone for them to drift out of our network. Every individual’s network is different from those of others because it is defined by social connections and therefore it matters whose perspective and connections are being observed. People may drift in and out of network activity and participation based on relevance, time availability, context, needs, and other personal constraints.
Networks have always been channels of knowledge diffusion and discovery: we learn from and with the people we know, whether connected via networked technologies or in person. Online, net forms are typically enabled by technologies incorporating social networking systems. Learners can be connected to other learners either directly or indirectly, and may not even be aware of all those who form part of the wider network to which they belong.
Many social networking sites such as Facebook, LinkedIn, and MySpace provide network support and facilitation tools, yet the form has been used by distance learners for much longer: earlier email lists and threaded discussions also support networked learning and physical social networks, and have long been important channels of knowledge diffusion.
It is important to distinguish some shifting notions in the concept of a network: the Internet, for example, is as much a physical network of machines and connections between them, as it is a network of people. Indeed, that physical network is the means through which people can come together. It is also important to recognize that, quite apart from a means of transport, a network can include or be entirely composed of things (physical and conceptual), not just people. Indeed, it is possible to view the entire universe as a network. Our concern here is not with the abstract topological form of networks in general, but with the social form of the network. Physical networks may be fundamentally required to connect people in a group, for example, but the group social form is different from the net social form even though both are, in several meaningful ways, describable as networks. Net modes of interaction can be one-to-one, one-to-many, and many-to-many.
Sets
Our final social form is the set. Sets are made up of people who are bound together by commonalities or shared interests. People may be unaware that they are part of a set (e.g., people with a particular genetic marker), or they may identify with it (e.g., people who are fans of football or constructivist teaching methods). Sets involve interactions with others, but typically these are impersonal or even anonymous. When an author publishes a textbook, he or she is writing for a set—an unknown number of people with a particular shared interest. Library books are categorized with metadata that puts them into sets, allowing individuals to seek items of interest.
In the past, the social interaction in most sets tended to be one-way, with a few exceptions such as a speaker’s engagement with crowds in lecture theatres, for example. Online, the set form has become more significant. A blog post or public tweet (especially when tagged or given a subject line to indicate its content) is not usually aimed at an individual, a group, or a network of friends (though they may be included), but at others who share that interest. While learners seeking information about a topic may well take individuals and networks into account when choosing a blog post to read or article in an online journal, it is more often than not the topic that attracts them, not the network. Much of the time there will be no expectation of engagement, no new network formed, no group joined. When individuals browse YouTube videos, networks may well play a role but, for the most part, discovery is based on content similarity and shared keywords. When we pick curated items or those that have been highly rated, the network is simply the underlying infrastructure: what matters are the metadata that classify and organize social content. This does not make the social ties of sets unimportant: sets can be central to our identity and we may feel closeness with and trust others simply because they share attributes with us: people with the same religious beliefs, who like the same kind of music, or who support the same football team, for instance. Set modes of interaction are typically one-to-many and many-to one, though they can enable many-to-many engagement.
Social Software Support for Social Forms
Different kinds of social software support various social forms in diverse ways. Group-oriented systems tend to provide features like variable roles, restricted membership, and role-based permissions. Network-oriented systems tend to provide features like friending, linking, and commenting. Set-oriented systems tend to provide tools like topic- or location-based selections, tags, and categories. Very few substantial systems are limited to any single mode, but most have varying strengths or emphases in different areas. The more complex or multi-featured the system, the more likely it will be to support different modes, and most can, with sufficient effort, be cajoled into performing different roles even though their intended purpose may be at odds with a particular use. Table 3.1 provides a few examples of popular social systems categorized according to what we perceive to be the predominant forms they support at the time of writing: but the reader must bear in mind that this is a shifting arena where changes and enhancements are constantly being introduced and that our perceptions may differ from those of others that use them in different ways. These are all soft technologies composed not just of tools but of the methods, processes, and intentions of their users. Almost any tool can be ben to support almost any social form, even if the fit is poor.
Table 3.1 Support for social forms in some common social software.
Many, if not most, social sites and software systems incorporate facilities to support and/or gain benefit from each social form. For instance, Facebook is primarily a social networking platform, yet it supports the formation of closed groups, individual-to-individual communication, and a host of collective aggregations such as voting systems, data mining to identify people you may know but have not connected to already, and add-in applications such as music/movie/ book recommenders. An archetypal group such as a face-to-face class may contain many networks of friends that extend beyond and within the group, its members may be categorized in sets relating to, say, ability, interests, or opinions, and collectives may occur in many ways, such as a teacher counting a show of hands or collating the results of clicker presses.
Intersections
There are many hybrid types of each of the main social structures we have identified that are as significant as the pure forms themselves. The “pure” forms of sets, nets, and groups may be mixed in different proportions to combine their features, producing some of the social organizational forms we are familiar with.
Group-Net: The Community of Practice
The classic intersection of a group and network is a community of practice (CoP). CoPs emerge, typically in workplace contexts, as networks of people who are within a group or groups. The notion of legitimate peripheral participation attests to the network-like features of a CoP, and yet there are many ways that members might regard them as cohesive units. It is helpful to think of these as clusters: a number of people in a network who share a purpose, practice, and often location, but without the explicit hierarchies, exclusions, and roles of a more defined group.
Group-Set: The Tribe/Community of Interest
Shifting from the pure group toward the set, communities of interest gather due to shared interests, and typically engage in more or less formal ways. They are often bound by interest in a topic more than by the group itself, though this may change over time. Some communities of interest occur at boundaries between sets and nets as well, if there are no formal kinds of engagement. When there is a shift beyond communities of interest toward more set-like engagement, we define this blurred category between groups and sets at the “set” end of the continuum as “tribes,” a label that applies not just to actual tribes but also to a range of forms that share some characteristics of sets and some of groups: these include companies, universities, nations, and academic groupings.
Like groups, many tribes have hierarchies, social norms, explicit and implicit rules, and shared purposes. In a learning context, unlike groups, they are seldom time-limited, and few individuals know everyone in the tribe. They are bound by one distinct shared attribute, but this always comes with a range of other attributes, otherwise they would be pure sets. For example, those who share the same religion will also be bound by moral codes, belief systems, and expectations of behaviour, or other features that mark them as members of the tribe. As they become more set-like—for example, Goths, fans of a hockey team, learning technology researchers—the deliberate hierarchies disappear, becoming more diffuse and abstract, though the characteristics that make them a set may still be firmly associated with their sense of identity.
Set-Net: The Circle
It is commonplace to divide networks into more or less arbitrary categories that are often described as ‘circles,’ such as in ‘my circle of friends.’ We might, say, think of sets of people we know who live nearby and those who don’t, or those who are friends and those we work with. Technologies such as Google+ Circles, Facebook Lists, and Elgg Collections are explicitly designed to allow us to classify people in multiple ways, reflecting the differences in how we relate to them, what we reveal about ourselves to them, and what we hide. Communities of interest may also occupy this blurred line between nets and sets, where the shared interest is the set attribute but where there are no formal or informal norms, rules, exclusions or inclusions. For example, followers of a particular band may come to know one another and cluster together at band concerts, without any formal, group-like constitution.
Kinds of Collections of People
As E. O. Wilson observed, “every person is a compulsive group-seeker” (2012, Chapter 24, Para. 10), a statement that is embodied in the phenomenal range of words that we have in the English language to distinguish different aggregations of people. In analyzing existing social forms to test our model, we came up with over 120 different words commonly used to refer to a collection of people, from alliances to workforces, without taking into account any of the millions of distinct proper nouns used to refer to specific groupings like banks, cities, countries, or scout troops. In our analysis, we discovered a few interesting things of note about this very incomplete example list. In the first place, many formal words relate to distinct organizational forms, especially those that occur in military, religious, business, and scholarly contexts—squads, sororities, flocks, federations, and the like. Bearing in mind that language has evolved slowly, this speaks to an important feature of many human groupings: they are technologized.
Many social groupings come with associated processes, methods, rules, legislations, procedures, rites of passage, rituals of entry and leaving, and are such an embedded feature that they have acquired their own vocabularies. Others categorize people according to things they share in common or that others perceive them as sharing in common such as race, class, dwelling place and so on, sometimes with implications that relate to other characteristics. Words like “tribe,” “nation,” “race,” “working class,” and “neighbours,” for instance, indicate set-like characteristics that are used to fit people into slots.
Identifying Social Forms
In determining the dominant social forms, the distinctions we have made are:
• Sets are social forms where people may have no knowledge of others in the set but are clustered by commonalities between them. This may lead to strong identification and trust in some cases, but not typically.
• Groups are social forms where individuals deliberately join others with shared goals and identify with group norms and behaviours.
• Nets are social forms where the connections between individuals and sometimes clusters of individuals are what bind them together.
While sometimes it can be hard to identify whether one collection of people is a group, net, or set, there are rules of thumb to follow. In brief:
• If the social entity persists even if there are no participants, likely it is a group.
• If there is little consequence to knowing who is involved and the topic is the most significant aspect, it is likely to be a set.
• If identifiable people are recognized by one another, it is probably a net.
In many cases, it is possible for all three to be true. It is helpful to visualize the typology as a Venn diagram of overlapping sets, the overlap indicating not only that we choose to see a particular social form within a collection of people and this does not exclude us from having other perspectives—all groups are both sets and nets, for instance—but also that there are often overlaps and fuzzy borders between them. Figure 3.2 shows the typology with some examples of the kinds of social entities relevant to learning found within them. Alternatively, you could see it as a continuum (see figure 3.3).
Figure 3.2 Venn diagram view of the typology
Figure 3.3 View of the typology as a continuum.
Each social form blends into the next. For example, many tribal forms such as affinity groups like hockey fans, Goths, or actor network theorists, are closer to sets than groups; others, like universities, nations, and international conferences are more group-like. Communities of practice exist somewhere on the continuum between groups and nets, often with limited or non-existent power structures but showing greater intentional cohesiveness than a simple network. The notion of blending is useful as it suggests an analogy to colours: an infinite variety of different shades and hues can be created by combining the three primary colours.
Collectives
Having defined the three social forms, we now turn our attention to collectives, which are perhaps the most intriguing of entities enabled by social software. Collectives, as we use the term, make the crowd behave as a single actor. They are not social forms like groups, nets, and sets, but are the machine- and/or human-aggregated results of the activities of a collection of individuals. Collectives achieve value by extracting information from the individual, group, set, and network activities of people, and then using that information to perform some action. Typically in cyberspace, these activities are aggregated by software and the results presented through computer interfaces, but humans can intentionally perform the aggregation role too. However, there need be no external agent involved for a collective to form: the individuals who form the crowd may themselves perform the aggregation, leading to emergent behaviours of the crowd.
Prior to the advent of the Internet, intentional collectives were used in, for instance, voting in elections or shows of hands in a classroom, but unintentional collectives occur in a more widespread manner, such as the formation of distinct footpaths in forests, the gathering of crowds around a street entertainer, and the movements of the stock market.
On the Internet, there are perhaps millions of applications that create value through aggregation, analysis, processing, and re-presentation of crowd activities, collecting user actions such as links placed on web pages (e.g., Google PageRank), photo and video tags, annotations and downloads (e.g., Flickr, YouTube, Instagram), article or solution evaluations (e.g., Digg, Mixx, Slashdot, StackOverflow), recommendations (e.g., Amazon, ratemyteacher.ca), and those that employ individuals’ reputations for some other purpose (e.g., eBay). Crowd behaviour can be mined from implicit choices or contributions made at the individual, group, or network levels, from explicit behaviours such as rating or tagging, or by combinations of each approach. Collectives generally improve in value as the size of the group’s/ network’s/ set’s sampled actions grows. When large numbers of resources are sorted, annotated, and rated by many, for example, the resultant resource listing can gain considerable collective value compared to a list rated by a single unknown individual.
Collectives behave as active agents within a system in ways that are analogous to the agency of human beings: in fairly predictable ways they make choices, value statements, expressions of belief, and act to bring about changes in the behaviour of others. This is of great importance in the context of learning in networks and sets because, in the absence of a formal teaching or cognitive presence, collectives often play that role. Collectives may sometimes act as mirrors of the group mind, or aspects of network consciousness that system designers or members of the crowd have chosen as significant. Because they represent chosen aspects of group, set, or network activity, the reflection of the collective mind is always shown through a distorting mirror that may be aggregating, refining, concentrating, selecting, filtering, averaging or otherwise processing aspects of crowd behaviour.
Typically, but not exclusively, collectives affect their own members in an iterative and self-organizing cycle. For instance, in social navigation, cues are often emphasized or de-emphasized as a result of individuals within a group or network moving around a system, which in turn affects the later navigation of that same group or network. However, this does not have to be the case. For example, the results of voting for a candidate by one group may influence the voting behaviour of another, or the tagging of photos within a system such as Flickr may influence the behaviour of outsiders and visitors to that system’s resources.
Size of Groups, Networks, and Sets
E.O. Wilson notes that “to form groups, drawing visceral comfort and pride from familiar fellowship, and to defend the group enthusiastically against rival group— these are among the absolute universals of human nature and hence of culture” (2012, Chapter 7, Para. 1). Groups in early human societies reached practical limits that were related to their function as humans evolved. The limits were constrained by available food sources to support communities, difficulties of coordination and allocation of work, and the laws of physics. Family-sized groups and workgroups are not viable persistent units in evolutionary terms because there are insufficient gains to be had from the division of labour and spread of innovation (Ridley, 2010). However, to extend beyond a certain size in the past required complex structures that evolved quite late in our species development, such as macrodemes and trade.
Moreover, with limited means of communicating over long distances, interactions were, of necessity, local: physics places limits on how far a voice can carry or the distance at which a person can be seen. While large herds are possible in many species, they emerge through individuals’ coordination with others in the vicinity (Miller, 2010). For coordination of the kind seen in human communities, large sizes posed distinct limits.
British psychologist Robin Dunbar (1993) examined the size of groups among many primate species. He noted that the size of the group is related to the amount of social grooming engaged in by that species. Humans, however, have much larger brains than most primates, and limiting our interactions to those with whom we could be mutually engaged in social hair grooming would be both costly in time and likely very boring. Dunbar used statistical mapping techniques to suggest that our brains allow us to expand the size of groups with which we can interact and “can have a genuinely social relationship, the kind of relationship that goes without knowing who they are and how they relate to us” (1996, p. 77). Based on the size of our brains and validated by observations of both primitive and modern communities, online groups, army units, businesses, and other groups, Dunbar estimated this size is 150 persons, often referred to as Dunbar’s Number. Interestingly, this coincides broadly with what Caporael (1997) distinguished as “macrodemes”: originally seasonal gatherings of bands (demes of around 30 individuals that could sustainably hunt together) and later instantiated as the typical size of villages for around 15,000 years.
In reality, we operate in groups of significantly greater size than Dunbar’s number suggests, though we may not, and in many cases cannot have a personal relationship with all the people in them. Companies, towns, universities, countries, religions, and many other group forms have developed primarily through the use of hierarchies and processes, methods and technologies that facilitate the exchange of knowledge between them. As Dunbar (1993) himself notes, language makes it possible for us to form groups with hierarchies and divisions of labour, so the actual size of human groups is considerably larger than what our brain capacity alone would suggest is possible (p. 689).
But what of broader networks in a technologically mediated age? Dunbar’s notion of relationships in virtual spaces in the mid-1990s was decidedly jaded. He felt deception and fraud by “shadowy ciphers” would result in such an excess of deceit that face-to-face interaction would be necessary to restore trust, resulting in the number of trustworthy acquaintances conforming to earlier norms of around 150. However, technology changes that, and he was probably wrong in the first place. Apart from anything else, the definition of a “genuinely social relationship” that he uses is neither clear nor precise. Moreover, far from reducing genuine human interaction, it appears that the connections formed online strengthen and increase those that are face-to-face. As a probable result of improved Internet and mobile contact, the average number of friends whom American adults see in person grew 20% in the five years between 2002 and 2007 (Rainie & Wellman, 2012). More recent research suggests that the number of networked ties maintained by individuals in present-day developed societies tends to be closer to 600 (DiPrete, Gelman, McCormick, Teitler, & Zheng, 2011; T. H. McCormick, Salganick, & Zheng, 2010) and Dunbar himself explains close ties as only one of a series of layers of embedded relationships (Rainie & Wellman, 2012).
Donath (2007) brought the arguments on group size in virtual space to bear on popular social networks such as MySpace and LinkedIn. Using signalling theory, she notes the means by which individuals signal to each other using fashion, linguistic shortcuts, and public displays of “friendships” to build and maintain social networks and trust. Her speculations appear to explain the ways that sets can transition into networks and groups. Sets are, however, unbound by intrinsic size restrictions. They can be as small as an individual or as large as the population of the universe: we are all in the set of physical things, for example. All that is required for a set of unlimited size is the capacity to identify and present it. Modern search engines, classification schemes, aggregation tools, and filters make it possible to engage with enormous sets of people.
There is a loose correlation between size and the levels of our social typology. Most groups are smaller than most networks; many networks are smaller than many sets. However, technological mediation can make groups, nets, and sets of any size a possibility.
Aggregation and the “wisdom of crowds” arise at many levels, but the results generally become more useful as numbers increase and the benefits of large aggregation among otherwise non-related choices become apparent. This is the power of the long tail (C. Anderson, 2004), whereby even very small tendencies and interests arise in significant enough numbers to be of value. More is nearly always better. A classic example of a collective is the fairground game of guessing the number of candies in a jar. In this collective, a number of independent decisions which are, when considered individually likely to be wrong, are usually, when averaged together, very close to correct (Surowiecki, 2004). However, when there are only two people guessing, it is far less likely to be accurate than when there are a hundred, and the accuracy rises when there are a thousand. In the online world, Amazon’s success at predicting books you will like is, in large measure, due to the number of people’s independent choices that are available. If there were fewer people than books, to take an extreme example, it is quite unlikely that the results would be valuable.
Summary of the Values of Different Social Forms and Collectives
When designing a social system to support learning, it is important to bear in mind what kinds of activities and what goals are intended, and to choose approaches and social forms that best serve the needs identified. To summarize the main strengths and weaknesses of each form:
• Groups offer the greatest value when the object of knowing is known and the process of knowing is complex. They are especially helpful when a sustained effort is needed. Groups are powerful motivators, exploiting our innate need for belonging and the ways that we have grown up and/ or evolved to live in hierarchies. However, groups require commitment and come with a large overhead of design and management; they are also expensive. Tools built to support groups should normally provide support for roles, processes, and procedures.
• Networks are embedded in practice, extend beyond the specifiable, and allow us to benefit from diversity and knowledge that transcends boundaries and easily specified objectives. Networks are great for topical, just-in-time learning, and expose us to serendipity and change. Networks, like groups, exploit social capital for both contribution and motivation. However, networks take effort to be exploited for learning. Without structure and guidance, we have to make decisions for ourselves. Generally speaking, network tools should help manage and sustain relationships, make and break connections, and deal with the organization of subsets of the network, with discretionary access and privacy controls.
• Sets are most useful when the knowledge we seek cannot be easily found in our groups and networks, when we need to know something but do not know who to ask. They are also a valuable means of gaining diverse insights and knowledge about a subject. However, like networks, they demand effort from us to decide what to learn in the first place and then to make decisions about reliability, relevance, and truthfulness. Sets need tools for organization and, on the whole, benefit most from the availability of collectives to support them.
• Collectives provide the means for us to make sense of, in particular, sets, to a lesser extent nets, and occasionally, groups. Like teachers, collectives tell us what to do, who to trust, what is interesting, and how to approach a subject. However, collectives are only as smart as the crowd, the means by which the crowd is selected, defined by the algorithms and presentations that perform the work. The learning needs, rather than simply the preferences, of their users should be supported.
The form or forms that an individual learner may make use of in his or her learning journey will always depend upon context and needs, but these will be codetermined by external structures like the need for assessment and accreditation, the formal and informal rules of behaviour in a given context, as well as other financial, personal, ethical, and social constraints.
Table 3.2 summarizes a range of attributes and their typical values of groups, nets, sets, and collectives so that the reader may match them with the needs of their own communities with which they are concerned.
Table 3.2 Groups, nets, sets, and collectives compared.
In many cases, the lines between the different social forms may be blurred or shifting. It is common, for example, to encourage communities of practice that share emergent properties with networks and, at least in their early stages of formation, have weak structures and limited hierarchies. Similarly, a tribal group may often be more set-like than group-like in terms of the interactions between people. For example, we may know no one in a large organization beyond our own groups, and so interactions beyond the group share many commonalities with interactions between strangers in a set. It is also, as we have observed, common for there to be blends of forms in any given community. There can be people that we know within an anonymous set, for instance, and we may have many crosscutting networks within and beyond the groups we are members of.
Conclusion
We have presented a typology of the kinds of aggregation that social software can support and of collectives that can emerge from them. It is not the only possible means of categorizing such things, but it makes sense of the different ways that social software systems can support a social learning process, and helps us to unpack the sometimes subtle differences between ways of teaching and learning on the Net. We hope to show, as the book progresses, that the differences (though sometimes blurred or mixed) are profound, and failure to recognize the kind of entity with which we are dealing can, at best, lead to lost opportunities and, at worst, can undermine the educational endeavour.
Choosing names is an important task, and getting the right name matters. As the British philosopher J.L. Austin put it, “Words are our tools, and, as a minimum, we should use clean tools: we should know what we mean and what we do not, and we must forearm ourselves against the traps that language sets us” (1979, p. 182). The names we have chosen were the result of much debate and cogitation, but they may not fit with your own understanding of the words. If that is so, then we ask that you suspend your existing preconceptions for a while and, if you wish, substitute words that you find more appropriate. It is not the words we use that are important here, but what they signify. | textbooks/socialsci/Education_and_Professional_Development/Teaching_Crowds_-_Learning_and_Social_Media_(Dron_and_Anderson)/03%3A_A_Typology_of_Social_Forms_for_Learning.txt |
LEARNING IN GROUPS
An impressive collection of studies has shown that participation in well-functioning cooperative groups leads students to feel more positive about themselves, about each other, and about the subject they’re studying. Students also learn more effectively on a variety of measures when they can learn with each other instead of against each other or apart from each other. Alfie Kohn, Punished by Rewards
In this chapter, we delve into the most commonly used form of social aggregation in campus-based, workplace, and distance-based forms of education. The group has a history that began with our primal ancestors as the most practical aggregation of individuals for survival and necessary social cooperation (Caporael, 1997; Ridley, 2010; E. O. Wilson, 2012). It has survived and flourishes today as, among many other things, the standard social form used in face-to-face classes, as the cohort and hierarchical organizational form that commonly characterizes education. The vast majority of research into social learning in formal education has focused on the group form because that has, until recently, been the only social option available to most face-to-face and distance institutional learners. In this chapter we examine the strengths and weaknesses of groups, and the typical evolution of educational groups as they form, perform, and dissolve. We also look at research on the development and support of social, teaching, and cognitive presence that defines quality online learning groups.
Defining the Group
Webster’s online dictionary defines a group as “(a) a number of individuals assembled together or having some unifying relationship; (b) an assemblage of objects regarded as a unit” (“Group,” n.d.). These definitions alert us to the most important characteristic of groups, whether online or face-to-face. First, groups are gathered together and exist for some purpose. Second, group members regard themselves and are regarded by others as having some unifying purpose. However, the dictionary definition allows for a wide variety of interpretations and connotations, and does not capture its distinctiveness in formal learning. We need something more precise. With that in mind, we note the following characteristics of groups used in formal and non-formal learning.
Hierarchical Structure and Leadership in Groups
In order to define the purpose and activities that are central to the definition and function of a group, members develop organization and leadership roles. In education, this function is normally assigned to the teacher, who often articulates the structure of the group’s activities in the ubiquitous course syllabus. Many courses also create smaller group activities—one of the challenges of this is that individuals must determine their own sense of structure and leadership—though often teachers fill this void as well by pre-determining group membership and even leadership roles. The same applies as we work our way up the organizational hierarchy: teachers report to department heads, principals, deans, vice-chancellors, presidents, and so on up the chain, often ending at regional or national government levels.
Groups Have Rules
The fact that teachers assign and structure groups reveals perhaps their most significant feature: they are designed. Groups exist largely as a set of implicit and/ or explicit rules that govern their constitution, their activities, and expected behaviours of their members. These may be strongly stated as laws, regulations, or procedures, or be vaguer or less tangible expectations, norms and patterns associated with group membership. The rules can shift between formal and non-formal manifestations as the group persists through time. This further implies that many of the characteristics of groups are designed to foster or enhance a sense of identity, and this is often created at the cost of individual freedom.
Groups are Purposeful
Ridgeway (1983) argues that groups are formed for two possible reasons: support or task accomplishment. Primary groups are formed to provide support for their members, while task groups are formed to reach some goal or to accomplish a task. In the process of working together to meet either or both of these needs, the group creates a set of norms or an evolving culture that strengthens the sense of group commitment.
Groups are Technologically
Driven Groups are more than labels applied to a particular collection of individuals. In many cases, groups are invented devices designed to orchestrate phenomena to a purpose: they are thus technologies (Arthur, 2009). They have forms, processes, and functions that are distinct and not emergent from the members and their interactions. Groups are deliberately bound together as an assembly of processes and structural forms to achieve some purpose or set of purposes. They utilize a range of processes that relate to group function and construction. Frequently, these processes are made explicit: technologies such as scheduling, formalized processes such as lectures, seminars, or guided discussions, regulations for behavior, and so on are the engine of many groups in an academic setting. Implicit group norms, tacit process structures, and hierarchical process management also contribute the technological assembly that enables and channels group behaviors and activities. In the language of actor network theory, they are blackboxed (Latour, 2005), and translated into punctualized actors (Law, 1992). The technologizing of the group form is perhaps its most distinctive feature when compared to network and set social forms, neither of which incorporates such formal structures and processes.
Groups Exist Independently of Members
Groups celebrate the stability and comprehensibility of form and function. This is not to suggest that groups do not change as they develop over time—a field of study often referred to as “group development”—but that the process of development is constrained within the structures and norms established by the group’s founders and/or owners. In other words, groups exist as something distinct from their members. It is notable that some groups—companies, organizations, clubs, and societies, for example—have persisted for hundreds or even thousands of years with recognizable identities despite constantly shifting membership. While we might identify distinct cohorts and classes of students, the course they are enrolled in and its surrounding relationships with other technologies and structures remains a unitary object. The teacher, the location, the students, even the topics taught and means by which they are assessed may change over time, but a course can seemingly persist through all of this, like the Ship of Theseus, or a river that remains the same, though everything in it constantly changes.
Members Aware of Membership
Members of a group invariably know that they are members. There may appear to be some very rare exceptions, such as a native tribe not knowing that its members are part of a country, or non-Mormons not realizing they have been included as honorary Mormons in genealogical records but, in all cases, such membership is, from the point of view of the member, that of the set (we will have more to say about this later). Most of the time we join groups intentionally, though in some cases other actions, such as being born in a particular country, the merger of two companies or departments, living in a particular city, or being enrolled in a course because we are working in a program, can make us members without our assent. Once we are members, we become obligated to behave as the group’s regulations require, or risk exclusion and possibly expulsion.
Groups are Exclusionary
Wilson, Ludwig-Hardman, Thornam, and Dunlap (2004) refer to groups that are formed in formal education contexts as “bounded communities.” They erect barriers that separate members from non-members. Shirky (2008) observes that groups are as dependent for their existence on who they exclude as much as who they include. Most groups involve rites of admission such as filling in forms, pledges, initiations, formal introductions, rituals, admission to buildings, et cetera. They typically place restrictions on who can and who cannot join. Interestingly, restrictions are commonly defined by set-based characteristics—race, creed, gender, academic qualifications, job, location, marital status, family, et cetera— sometimes supplemented with network characteristics: whether they are known to or recommended by an existing member, for instance. There are often rules that determine how, whether, and when people might leave a group. Many groups set time limits, especially in an educational setting, have rituals for exit such as award ceremonies, retirement events, or farewell parties, and may include processes for deliberate expulsion.
Distinctive Educational Group Features
While there are many common features for all groups, whether intended as vehicles for learning or not, some features are distinctive in a teaching setting.
Participation Often Required to Obtain a Desired End
Group membership in an educational context carries with it a commitment to share time and knowledge with group members. How to assess this participation remains a contentious issue. Some teachers track attendance—reminiscent of the all-too-familiar daily ritual of elementary schools. Others use tools and rubrics to assess the quantity and quality of students’ contribution to online discussion forums. More innovative assessments include those where students produce learning artifacts, and assess themselves and their peers for attendance and participation.
Group Members do not Select Classmates or Instructor
Although larger institutions can offer greater choice for students, and students can and do enroll in courses with close friends, admission to a program and the assignment of teachers is a task jealously guarded by administrators. Despite the exclusion of student control at this level, students as stakeholders are being increasingly welcomed onto advisory and even governance committees in many institutions.
Group Members must Commit to a Fixed Length of Time
Course organization in batches, where cohorts proceed through a course of studies together, defines the vast majority of higher education learning systems. The groups that form using this organizational model provide a ready group of collaborators for social and cooperative forms of learning.
Group Members must make an Explicit Effort to Connect with Others
By coming together online or face-to-face, synchronously or asynchronously, group members enact the technology of the group. Groups do not meet unintentionally.
Groups Restrict Pace
If students are learning together as a group, there is nearly always some constraint on the speed at which they learn. Typically, they must attend the same lectures, or engage within a fixed period in a discussion forum, or submit assignments at the same time.
Why Groups are Worthwhile
As a result of all these constraints, one might assume that groups are an unattractive form of learning organization, but this could hardly be further from the truth. The vast majority of formal education takes place in group contexts. The group is a familiar and comfortable aggregation for both learners and teachers. The agricultural-based notions of pacing study to allow students freedom to work on the farm in the summer, and the flow of cohorts into evenly spaced and paced fall and spring terms has become synonymous with institutional learning, and is matched with promotions, catalogs, and advertising for even informal and non-credit forms of education.
The rationale for organizing formal learning in bounded communities is often defended, as the resulting security allows for the creation of a safe and supportive environment. Within this protected harbor, learners and teachers are free to explore ideas, make new friends, challenge one another’s interpretations, and place obligations of cooperation and support upon one another. From the earliest days of formal education, security for scholars and scholarship to evolve outside of the constraints of ideological or theological hegemony has been a dominant component of academic freedom, necessary for the development of innovative solutions to solve the complex problems facing society. Thus, there remains a strong case for the provision of group-based learning.
Cooperative Freedoms in Groups
In an educational context, grouped modes of learning share a number of distinctive characteristics, some are simply a result of physics, and others are the product of the nature of group social forms. While there are uses for groups in self-paced models of learning that we will refer to later, by far the most common model used in institutional and organizational learning is that of the paced group, which we will focus on here. We present a spider chart indicating the typical notional freedoms available to learners working at a distance in paced groups in Figure 4.1, noting that such groups in face-to-face learning are significantly more constrained.
Place
Although home situations or the need to visit cafés or libraries for Internet access may occasionally impose some limits on the freedom of where learning occurs, as in all distance learning, there is in principle virtually no limit on freedom of place in a group-based distance learning context.
Figure 4.1 Notional levels of control in a typical paced course.
Content
Choosing or creating content has long been a defining role for teachers in group based learning. Despite the large and growing quantities of learning resources available in cyberspace, many of which are freely accessible, students expect teachers to filter and annotate the content, so as to create a structured path through learning activities and content. It is interesting to note the widening gap between the learning that occurs in formal courses—where students are expected to consume content selected by teachers—and common behavior in informal learning, where students turn to search engines, trusted friends, answer systems, or libraries when they want to learn something.
Pace
The fact that groups tend to work in lockstep makes control over pace relatively low in a group-based setting. Like time, it is a question of scale. In asynchronous mode, though a student may have to perform activities within a time period, he or she may vary the pace within those constraints. This is especially valuable when it comes to the much-lauded benefit of asynchronous discussion, because technologies provide students with time to reflect on contributions before posting them, with pedagogically beneficial results. Even when the primary mode of teaching is synchronous, the primary mode of learning may not be. It is, for example, common for readings or activities to be set so that the learner can choose to address at any time between synchronous sessions. This illustrates the important point that, though a method can be described as a social constructivist mode of learning, it will nearly always include some elements that are behaviorist/cognitivist in nature.
At the smallest scale, the way that messages are phrased in a social-constructivist dialogue will usually take into account some model of learning, implicit or explicit. We may, for instance, phrase something as simply as possible, make connections, or draw analogies, all of which assume some model of how individual people think and learn.
Method
While a teacher may determine the general pedagogies used in a group-based learning environment, there are some opportunities for learners to negotiate the method. For example, if a student in a group has difficulties with a particular issue, the teacher or other learners can reformulate a discussion, provide a different presentation, or an alternative perspective that is pedagogically different from what was originally planned. As with other freedoms in group contexts, however, the freedom of an individual may be constrained by the needs of others in the group.
Relationship
If the teacher has decided that a particular form of interaction is required, there may be relatively little control afforded to the learners in a group as to how and with whom engagement occurs. Indeed, it is commonplace in formal learning for engagement to be assessed, whether directly or indirectly, placing strong constraints on how and whether learners engage with one another or their tutors.
Technology
Most Internet-based solutions allow some control over the devices and software used to access them. This can, however, lead to problems such as inequalities between learners, and support for a preferred technology may be limited or nonexistent. Particularly commonplace examples of this include when a textbook is only provided in either paper or electronic form, or a particular web browser must be used, or mobile devices are not supported.
Medium
Group-based approaches seldom offer much choice over the media used for learning. Normally the institution or the individual teacher makes a decision about the type of media used both for disseminating content and supporting interaction within the group. This decision has become much more challenging for both teachers and students with the development of very low cost so-called Web 2.0 applications, providing hundreds of additional choices beyond the textbook and face-to-face interaction that have defined classroom groups or the Learning Management Systems (LMSs) supporting the vast majority of online learning groups. Technical and end-user based support for large numbers of web based programs present a large and growing challenge for learning organizations that, while attempting to provide up-to-date alternatives, are constrained by the need to protect group confidentiality and security, and ensure performance.
Time
Choice of time for learning engagement depends on whether communication is synchronous or asynchronous. In most group-based classes, it is common for asynchronous tools like email and discussion forums to be used for interactions. These provide a certain amount of freedom of time for engagement, albeit usually with constraints. It is typical to require responses within a period of days, or sometimes, hours. Synchronous tools, of course, provide no freedom of time at all.
Delegation
The ability to ask for clarification, change the direction of discussion, seek help and so on, makes freedom of delegation in a group-based learning context quite high. Though the hierarchical nature of group-based approaches to learning means that teachers may play a very large role in determining how and when interactions and learning transactions occur, there are often plentiful opportunities for learners to ask for more guidance. There are some dependencies, however, on other learners. While a single individual may seek further guidance or a change in direction, the needs of one typically need to be balanced with the needs of the many. If people are learning together, then outliers who wish to take a different direction may not always be heard.
Disclosure
There is seldom a great deal of control over what and how things are disclosed in a traditional institutional group setting. It is nearly always determined by the teacher, and represents one of the more technological aspects of groups: disclosure is designed into group interactions. A teacher may, for example, decide that sharing is bad for final assignments, but necessary for collaborative work. Commonly, the teacher may require students to engage in discussion forums or, less obviously controlling but equally coercive, may provide a discussion forum where every message is seen by all members of the group that is the only formal means of engagement for a course.
Transactional Distance and Control in Group Learning
Moore formulated his theory of transactional distance (1993) in an era when it was assumed that the teaching presence might be mediated through structured resources or more immediate communication between a student and his or her teacher via phone or letter. However, it provides a useful lens for exploring dynamics within groups. In a group, leaners and peers may also participate as teaching presences, leading to a more complex dynamic of distance. It is certainly true in most learning based on social constructivist models that the communication distance between teacher and learner is much lower than it is in an instructivist setting. This puts the learner in a more powerful position when negotiating control, where he or she is able to challenge and change the path of learning.
However, this occurs in a group setting in the company of other learners, each likewise engaged in negotiation for control, and each who may become the teaching presence in a learning transaction. The communication and psychological distance is thus very low, thanks to the effects of distribution within the group. However, transactional control is affected by competition. For example, if a learner seeks clarification from a teacher, though this increases control for him or her, from the point of view of others in the group their control is diminished, at least until they contribute and take back the reins themselves.
Group Size
Different patterns and methods work differently in various sizes of groups. In most cases, this is not due to the nature of groups as a social form so much as it is to the constraints of physics. For example, a teaching method that involves each member of the group sharing what they have learned with the rest may be effective among five to ten learners, but would require more hours than there are in the day with a group of 200, and would lead to massive decreases in attention and engagement after the first few students had shared their findings.
The technological nature of groups means that pedagogies for them must be engineered with due consideration for the exigencies and constraints of the group context, including its size. In the example above, one might use a different pedagogy altogether, or if one were set on the pedagogy, one could split the larger group into smaller ones, pick some students to present to the rest, or use a pyramiding process so that small groups selected the best and presented these to larger groups. While most size limitations are amenable to common sense, there are some differences in various kinds of groups that are worth mentioning.
Dyads
The basic dyad consisting of two individuals is common in, for example, supervisor-supervisee relationships, such as Socratic dialogue, master-apprentice models of learning, and personal tutelage. This is, as we observed in Chapter 2, a highly effective but generally too costly method of learning. While a group of two may be the smallest social group form from a logical perspective, there is normally little to distinguish a group of two from a set or net of two: individuals will establish roles and rules according to their needs. An exception exists in the supervisory relationship, where there may be rules and procedures that govern the nature of the interaction.
Work/Family Groups
It is not uncommon for study groups, tutorial groups, and small breakout groups to contain around five members, corresponding to the archetypal work/family group identified by Caporael (1997). Such small groups make the coordination and allocation of tasks simple to perform, even in the absence of particularly strong roles. In an online setting, a small group often communicates with nothing more than email or teleconferencing, modes of communication that, in larger groups, become very unwieldy.
Demes
The typical class in a school, and in many adult learning classes, is the rough size of what Caporael (1997) called a deme (from the Greek dēmos, or “people”), like the hunter/gatherer bands of our distant ancestors, consisting of around 30 members. It is at least a plausible hypothesis that we have evolved through group-level selection such that the deme is a manageable size of group that can work face-to-face in a coordinated way, assuming some leadership role to organize its actions.
Tribes
Identified by Caporael (1997) as the “macrodeme,” some group forms drift toward the set in their constitution, typically when they approach or exceed around 150 members. As we have previously noted this is significant in an educational context because tribal groups such as universities, schools, and colleges have the features of closed membership, rules, roles, and hierarchies that are common to all groups but typically lack the close connections, time, and pace restrictions of things like classes, tutorial groups, and workgroups. In these cases, as well as in more time constrained settings such as lectures given to large groups of students who do not know one another, it may be more useful to think of the group as being a set. Unlike a true set, a tribal group’s hierarchies and rules mean the form of learning that occurs is typically very much dominated by the teacher or other group leader. This is not the self-directed, topic-driven process that characterizes set-based learning: the teacher not only determines content and activities but also can act as arbiter and judge of what the set shares. This latter feature of tribal learning is particularly valuable, as the teacher can guide the learner down the desired learning path. Also, as suggested by our example, the teacher is able to manage the group processes so that larger tribes can be split into smaller groups, with all the benefits they bring.
Learning in Groups
Since group learning has been such a dominant form in institutional and organizational education, there is plenty of literature on how groups work in that context. Groups are as much machines for social action as they are social binders, and they are replete with repeatable processes that enable their construction and maintenance. In the following sections we explore some of the features of this semi-mechanical nature.
Online Group Formation
As groups in education are temporally bound, with pacing and scheduling limited by constraints on time for their formation and dissolution, it is important to pay attention to the way they evolve over time. A large number of researchers have studied the way groups form and develop. Here we present some of the more well-founded models.
Dimensions of Change
Many kinds of group development show great similarity among cyclical, linear, and recurring models. J.D. Smith (2001) argue that groups develop in three dimensions. The first is the social dimension, and occurs most often at the early stages of group formation when members come to know one another and the roles they are playing in the task. The second dimension relates to task development, in which the task that the group sets for itself evolves over time as component parts are completed and new assignments are accepted. The third, as Smith notes, is the dimension of group culture that develops with norms, values, and standards of behavior. Even when assessment is criteria-based, student perceptions can lead to a competitive rather than cooperative environment. This interplay between dimensions provides a useful way to understand the growth of groups.
Forming, Storming, Norming, Performing, and Adjourning
Perhaps the most commonly known and easily remembered model of group development is Tuckman and Jensen’s five-stage model of forming, storming, norming, performing, and adjourning (1977). This model adapts well to online learning groups.
Forming. The formation stage is often set by the educational institution and is quite normalized by the familiar roles that teachers (assertive and taking charge) and students (passive) easily fall into. Once a course has begun subgroups may form, but they are typically guided in their inception by the teacher.
Storming. The storming phase is also often constrained in formal education by the expectations and compliance of group members. Although aggressive and flaming behaviors in online groups have been widely studied (N. McCormick & McCormick, 1992; Schrage, 2003) formal education groups note the almost complete absence of such behavior, and even an excess of what our colleague Walter Archer, cited in Garrison and Anderson (2003), refers to as “pathological politeness.” Fabro and Garrison (1998) reported that the cohort they studied was “generally conditioned in many ways to be polite” and disagreement was taken “as either a personal affront or they were open and a very few people were open” (p. 48). This group appeared to be “quite timid” and “polite” and “began to just agree with each other rather than challenge each other’s ideas” (Fabro & Garrison, 1998, p. 48). It should be noted however that these observations were made on Canadian students, who may have distinct national problems with pathological politeness! Thus, for groups to form effectively in formal education, teachers might be advised to stimulate rather than repress “storming” behavior; this might explain the popularity of online debates (Fox & MacKeough, 2003; Jeong, 2003).
Norming. Norming refers to the comfort level that members of groups develop with one another as they come to have both their social and task expectations confirmed in their interactions with others in group meetings. The group stage is now set for the production and accomplishment of tasks. In some cases, the norming stage may be formalized into rules, procedures, and perhaps even a social contract that specifies expectations (Kort, Reilly, & Williams, 2002).
Performing. Once the previous stages of group development have been accomplished, the group can get on with doing what it is supposed to do.
Adjournment. Finally the group prepares for adjournment, with such rituals as the end of class party, completion of course evaluation forms, and fretting and extensive questions related to final examinations and term paper requirements.
Despite the linear nature of Tuckman and Jensen’s model (1977), many researchers have noted that group development also proceeds cyclically, revisiting earlier stages, or even progresses swinging like a pendulum, with “storming, norming, and performing” being visited in succession as the group develops over time.
Salmon’s Five-stage Model
Most of the interest in and study of groups occurred during the last half of the twentieth century before online groups were common. Perhaps the most influential model of group development for online groups—and especially those within educational context—was developed by Gilly Salmon (2000). Her five-stage model has been particularly popular and successful in recent years as a means of developing learning communities. Emerging from her research into online communities, the model is both descriptive of successful learning communities and prescriptive as to how they evolve, particularly with regard to the role of the moderator in facilitating their development. The model works in Maslowian hierarchical style. The five stages are:
Access and motivation. At this stage, the moderator’s role is to ensure that learners are able to use the relevant technologies, are enrolled as group members, and feel welcomed on arrival.
Online socialization. Learners engage in non-threatening message sending, typically greeting others, saying something about themselves, and getting to know people in the group. Salmon suggests that the moderator should help students become familiar with the norms and behaviors expected, offering bridges between this and prior experience in online and offline communities.
Information exchange. Learners begin to share ideas and knowledge with one another. The moderator now acts as a facilitator, establishing tasks and sharing learning materials and processes.
Knowledge construction. Learners begin to engage in meaningful dialogue, exploring and challenging ideas. The moderator facilitates this process with probing questions, challenging ideas, summarizing, channeling, and modeling good practices.
Development. At this stage, not reached by all groups, learners take responsibility for their own learning, challenging not just ideas but the process itself, taking the learning beyond the moderator’s prescribed limits. When this occurs, the moderator becomes an almost equal participant, supporting the independence of learners and dealing with problems as they arise. The model seems to fit well with our experience of online groups up to this point. However, it is not entirely clear what is being developed at this stage. We would have expected to see “learning application” or at least “integration” with relevant and authentic aspects of the real world contained within this phase.
Salmon’s model has proved useful in many online learning communities, and appears to describe what tends to happen in a well-moderated learning community, offering good advice for those hoping to facilitate such a process. There are complexities, however. In many cases, a cohort of learners will have gone through this process before, and may not need to do so again. Author Dron instituted Salmon’s model across a distance-taught program, applying the pattern mindfully in every course, and found that the first two or three stages were of little or no further value once they had been addressed in the first course taken by a given cohort (Dron, Seidel, & Litten, 2004). Students in a cohort were already familiar with the tools and one another, so they were able to start a new course at stage 3 or even 4 of the model. The intentionally scaffolded process thus got in the way of efficient learner-centred learning. As with any framework, the context of application needs to be taken into account and the framework modified to suit the needs, subject area, and learning history of the group concerned.
Power and Trust Relationships in Groups
Roberts (2006) notes the problems with power in groups that are referred to as “oppressed group behaviour.” Power relationships that define the organization often infuse thinking and constrain creativity within the group. The accountable nature of group interactions means that members act under the power constraints that define their lives, and these often exist outside the relationships within the group. This is especially relevant in the rigid hierarchy that differentiates teacher from student identity, power, and specific contributions in group contexts.
Trust is also problematic in groups. While group members need trust in order to freely elicit honest contributions from everyone, the unbalanced power dynamics noted above and the competition among students both limit its development. Formal education is marked by the assessment of student accomplishment. This has many downsides, not least of which is the enormously demotivating effects it has for both high and low achievers (Kohn, 1999), but is particularly pronounced when assessment is norm- rather than criteria-based, such that one excels based on their accomplishment and learning compared to other students, not from absolute knowledge of content or individual learning accomplishment. This was most dramatically evident during author Anderson’s first-year calculus class at a university where rather inept teaching, coupled with low motivation and a very large class resulted in a pass mark being calculated at 19%! This curve-graded score allowed all (teacher included) to feel good about their learning and themselves, even though most were failing to achieve the objectives of the class. It relates back to the problem of power relationships: competitive grading is less a way of enabling students to learn, and more a way of emphasizing and enacting the power of the teacher to control the process (Kohn, 1999). It is difficult to develop trust in competitive environments, thus explaining in part the distrust many teachers and students have for collaborative and cooperative learning models, despite the proven efficacy of these approaches (D. Johnson & Johnson, 1994).
Understanding Groups as Communities of Inquiry
In 1999 author Anderson with colleagues Randy Garrison and Walter Archer at the University of Alberta devised a conceptual model for online education, which they named the Community of Inquiry model. They developed it to provide both practical guidelines for teachers and designers, and as a research model for what was then asynchronous, text-based models of online education that were the norm for online education. During the last decade many other researchers have employed this model, and it is likely the most frequently cited tool used to evaluate formal distance education. Google Scholar (2013) lists over 1,000 citations for each of the four major papers and the book written by the original COI authors. The seminal articles associated with this model, as well as links to the work of numerous researchers referencing and extending it are available at (www.communitiesofinquiry.com).
Foundations
The COI model has its roots in Dewey’s (1933) pragmatic model of practical inquiry, in which ideas must be tested in the crucible of real application to establish and hone their accuracy. Lipman’s (1991) community of inquiry provided the model with both its name and the notions of reflective learning in a formal education, which he characterizes as follows:
• Education is the outcome of participation in a teacher-guided community of inquiry;
• Teachers stir students to think about the world when they reveal knowledge to be ambiguous, equivocal, and mysterious;
• Knowledge disciplines are overlapping and are therefore problematic;
• Teachers are ready to concede fallibility;
• Students are expected to be reflective and increasingly reasonable and judicious;
• The educational process is not information acquisition, but a grasp of relationships among disciplines (Lipman, 1991, pp. 18–19).
Note especially the essential role of the teacher in Lipman’s description, which fuelled the desire of Anderson, Rourke, Garrison, & Archer (2001) to explicate the role of the teacher and teaching presence created in formal education transactions. Lipman (1991) notes that within the community of inquiry members question one another, demand reasons for beliefs, and point out the consequences of one another’s ideas, thus creating a self-guiding and emergent community when adequate levels of social, cognitive, and teacher presence are present. To round the process off, Garrison’s (1991) model of critical thinking was used to develop stages and processes of reflection and decision-making that define critical thinking.
These theoretical works were used to provide conceptual order and a practical heuristic model to assess the teaching and learning context in the online community of inquiry. The model consists of three elements deemed essential to successful educational transactions: cognitive presence, teaching presence, and social presence. Garrison, Anderson, and Archer developed tools and techniques to reliably measure each of these three presences in text-based, asynchronous computer conferencing transcripts. In this section we expand and apply the ideas from the COI model to online group-based learning in both synchronous and asynchronous modes.
Community of Inquiry and Cognitive Presence
Cognitive presence differentiates social interaction in a group-based community of inquiry from casual interaction in the pub or on the street. Some have argued critical thinking most clearly defines quality in higher education contexts (Candy, 2000). We thus built on models and ideals of critical thinking to create our notion of cognitive presence.
Despite almost universal adoption of the notions of the importance of critical thinking in higher education, it is quite difficult to gain a consensus from the literature or practice on what it actually means. The confusion is related to the fact that critical thinking is both a process and a product (Garrison, Anderson, & Archer, 2000). Teachers in group contexts are expected to develop learning activities, model the process of critical thinking, and assess the outcomes of cognitive presence in the products of study—projects, papers, and test results—designed to provide evidence of the successful completion of critical thinking. In the Community of Inquiry model, we focused on gathering evidence of the process of critical thinking, and postulated it could be found in the activities of teachers and learners, as demonstrated by their contributions to the threaded discussions that serve as the main communication tool for much online group-based learning.
The first of four phases of cognitive presence is some sort of triggering event. This is often provided as an opening, question, or invitation for comment by the teacher’s post to the group. But additional triggers arise when participants reflect upon or challenge one another. To be effective, triggering messages must be meaningful, must spring from the experience of the group, and must be accessible and within conceptual understanding of the group’s members. Poscente and Fahy (2003) empirically defined triggering statements by the numbers of responding posts learners generated and, as expected, found that teacher triggers were most heavily responded to. However, student triggering statements were also observed on a regular basis in threaded online discussion.
The second phase of cognitive presence is “exploration,” within which group members iterate between individual reflection and group questioning, probing, and extension of their ideas and solutions to the triggering idea. This exploration is a divergent phase characterized by brainstorming, questioning, clarifications, and exchange of information.
During the third “integration phase” of the group-based development of cognition presence, focus shifts from exploring meaning to constructing it, and the integration of ideas into robust conceptual models. The leadership of the group is important at this stage, as group members often feel more comfortable “exploring” a problem until interest wanes without making the serious effort needed to arrive at a conceptually whole and integrated solution.
In the final “resolution phase,” the group focuses on ways to apply the knowledge generated in the three previous phases. This resolution may take the form of application and testing in a real-life context. However, often in educational applications, the resolution is a well-argued and detailed answer to a triggering problem.
Cognitive presence has been measured through surveys of participants’ qualitative interviews, automated neural network analysis of key words, and the transcript analysis method developed by the original COI team. In nearly all studies, evidence of the fourth and final resolution phase has been minimal, indicating that perhaps true resolution and critical thinking rarely occurs in the closed and often artificial groups or classes that define most forms of higher education.
Community of Inquiry and Social Presence
The second critical component of the Community of Inquiry is social presence, defined as “the ability of participants in a community of inquiry to project themselves socially and emotionally, as ‘real’ people (i.e., their full personality), through the medium of communication being used” (Garrison et al., 2000, p. 94). This definition was later expanded to include a sense of other group members as well as self and common commitment to a task. We identified three broad categories of social presence indicators: affective, open communication, and cohesive communicative responses. Thus development of a group and individual sense of social comfort is evidenced by use of affective interactions such as humor, self-disclosure, and changes in media use such as employing bold text, or the use of emoticons in group discussion. Open communication is shown by timely responses to member posts, quoting and referring to others, asking questions and complimenting or thanking other group members for their contributions. Finally, cohesive comments such as addressing group members by name, using inclusive pronouns to describe the group, and informal salutations indicate a sense of group cohesion and commitment that we defined as a component of social presence. Once again, through transcript analysis we were able to quantify the extent of social presence evidenced in the group, and this was correlated with satisfaction and perception of learning in a number of later studies.
Community of Inquiry and Teaching Presence
The final component of an effective group-based Community of Inquiry in formal education is Teaching Presence. Teaching presence begins with the instructional design and organization of tasks that are necessary to construct a context in which social and especially cognitive presence arises. In group activities within formal education contexts, both students and teachers have accumulated expectations about these organizational issues that often lead students to a role of passive reaction to the learning agenda specified by the teacher. The second component of teaching presence is the active facilitation of group discussion or other learning activities. Good teachers find opportunities to question, drill down and challenge learners to thoroughly explore, integrate, and apply the knowledge generated by the group. They also nurture the development of social presence by insuring appropriate levels of contribution by group members, and help establish a climate of trust and acceptance within the group. Finally, teaching presence includes direct instruction where the teacher or other group participants contribute their specialized knowledge to the group, diagnose misunderstandings, and otherwise provide leadership in the attainment of deep and meaningful learning experiences.
Applying the Lessons of the Community of Inquiry Model
The COI model has been widely used by both researchers and instructional designers. The designers validated and compared it to contexts beyond asynchronous online learning to show its relevance in comparison to face-to-face learning (Heckman & Annabi, 2005). Methodologically, the COI model was validated through student survey responses (Rourke & Anderson, 2002) and factor analysis of survey results (Arbaugh, 2007). Work has continued to develop a standardized instrument for measuring the extent of community of inquiry formation through student survey assessment (Swan et al., 2008).
We conclude this overview of COI’s contribution to the design and function of group-based learning with the series of recommendations that Randy Garrison made for designers and teachers. He advises them to
• Establish a climate that will create a community of inquiry;
• Establish critical reflection and discourse that will support systematic inquiry;
• Sustain community through the expression of group cohesion;
• Encourage and support the progression of inquiry through to resolution;
• Foster the evolution of collaborative relationships where students are
• supported in assuming increasing responsibility for their learning;
• Ensure that there is resolution and metacognitive development.
As these recommendations demonstrate, the community of inquiry model has strong implications for process, and emphasizes the deeply technological nature of traditional groups in formal learning: this is about repeatable methods and techniques that carry with them assumptions of structure and architecture that are designed and enacted.
The Critical Role of Tasks on Groups
Collaborative behavior is not a function of the group, but of the learning activities assigned or undertaken by that group. The task sets the context, the goals, and in most cases the appropriate organizational structure for the group. Townsend, DeMarie, and Hendrickson define virtual teams as “groups of geographically and/ or organizationally dispersed coworkers that are assembled using a combination of telecommunications and information technologies to accomplish an organizational task” (1998, p. 18). The role of the task is highlighted as having major significance in the function, organization, and success of virtual and face-bound groups. Bell and Kozolowski (2002) observe that task complexity is an especially salient factor. However it is not only the task but also its treatment by the group that affects its complexity. Tasks used by educators with learners vary widely in a number of ways.
Van de Ven, Delbecq, & Koenig (1976) described four types of organizational structure of increasing complexity that a group may develop to accomplish a task. The first was termed polled or additive: group members simply combined their work to accomplish the task. The second requires group members to work on some part of the task before moving the incomplete work to another (often differentially specialized group member) for additional work. The third follows a less structured back-and-forth movement of task artifacts, with group members adding value at various times as the product moves through production stages to completion. The final and most complex structure was termed “intensive,” and is characterized by continuous discussion, debate, evaluation, and contribution among team members at all stages of task function.
Virtual groups, because of the reduction in proximal clues, tend to need greater and more explicit amounts of external direction (teaching presence), and more structured forms of organization. They also tend to both rely upon and nurture more self-direction among learners than teacher-dominated groups characteristic of campus education. Learners have many more responsibilities than merely arriving at the designated teaching location at the correct time each week. These include technical competencies so that they can effectively utilize the various communication and information technologies necessary to complete of group tasks. They also must be able to monitor and effectively manage their time—being focused and committed enough to attend to assigned group tasks, while at the same time able to resist time-wasting activities such as unfocused web browsing.
Trust, Cohesion, and Groupthink
Groups or “teams” (as they are often referred to in business contexts), have long been the focus of study by business sociologists. Groups function as the primary means to increase trust, alignment, cohesion, and ultimately efficiency in the workplace (Burt, 2009). Group members, through exposure to one another and common social norms and behaviors, come to share common ideas, create localized jargon, and develop and share “similar views of proper opinion and practice and similar views of how to go forward into the future” (Burt, 2009, p. 4). This commonality leads to integration, the development of trust within the group, and the expectation of support and help when needed from individual group members. Further, increased communications within a tightly defined group creates efficiencies, and perhaps just as important, an inhibiting relational cost for bad behavior. All of this is positive and is used by effective group-based teachers and campus administrators in education to foster bonding and integration within classrooms, which in turn leads to increased engagement and academic success (Kuh, 2001).
However, cohesion in groups, like most social variables, has both positive and negative consequences. The American sociologist Irving Janis is credited with coining the term “groupthink,” which he defined as “a mode of thinking that people engage in when they are deeply involved in a cohesive in-group, when the members’ strivings for unanimity override their motivation to realistically appraise alternative courses of action” (1972, p. 9). Groupthink is a popular concept intuitively understood (at least in part) by academics from many disciplines and the general public. However, the antecedent conditions necessary for the emergence and symptoms of groupthink have not always been substantiated by rigorous experimental study (see, for example, Turner & Pratkanis, 1998). Nonetheless, some recent scholars have argued that the groupthink phenomenon is even more ubiquitous than Janis thought, and arises even in the absence of many of his critical antecedents.
Janis identified two groups of antecedent conditions leading to groupthink. The first are of a structural nature:
• Insulation of the group: Insulation is a cherished characteristic found behind the closed classroom door, gated campus, and password-protected discussions common in educational groups. Though originally designed as a way to protect dissenting scholarly views, the closed group now serves as much to isolate as it does to protect group members. As S. E. Page observes, this can lead to a lack of diversity, as well as reduced creativity and problem solving capacity (2008).
• Lack of a tradition of impartial leadership: Educational contexts have a strong tradition of leadership exerted by the teacher and school administrators. While we do not suggest that this leadership inevitably lacks impartiality, the leadership is often authoritarian, and at best carries a bias toward scholarship and at worst one that favors conformity.
• Lack of norms requiring methodological procedures: School groups seldom lack methodological procedures for getting things done, but again these procedures are rarely critically examined by either students or teachers.
• Homogeneity of members’ social background and ideology: Despite the desire of many advocates of liberal democracy for schools to serve as a great equalizer, there is considerable evidence that schools and the groups within them are one of the main conduits for the transmission of dominant social values with accompanying class divisions and capital moving only between generations of the privileged.
Janis’s second set of antecedents of negative groupthink is associated with emergent social conditions that are characterized by
• High stress from external threats: The life of a student is often a very stressful one. Examinations are frequent, and the recent trend to require more group and collaborative work adds additional stress to many students forced to be dependent upon others and deal with exploitation by freeloaders and social loafers (Piezon & Ferree, 2008).
• Recent failures: The external threat imposed by numerous tests and examinations of course also gives rise (at least occasionally) to failures by both groups and individuals.
• Excessive difficulties on the decision-making task: When groups move online, there is evidence that group decision-making, though not impossible, is slower and usually less efficient (Walther, 1994); online groups “are more prone toward conflict, and, most importantly, have more difficulty achieving consensus” (Farnham, Chesley, McGhee, Kawal, & Landau, 2000, p. 299).
• Moral dilemmas: Formal education rarely struggles with ethical dilemmas, except through removed academic lenses. Nonetheless, educational groups have their own set of issues related to plagiarism, cheating, and other forms of ethical dilemmas (Demiray & Sharma, 2009).
From the above description of antecedents, one can see that there is high potential for groupthink and its associated negative outcomes in group-based models of formal education. Indeed, one could wonder—given the prevalence of these antecedents in formal education groups—if anything but impaired forms of groupthink ever arise. Confronting the lack of direct causal relationship between antecedents and groupthink outcomes, and the knowledge that groupthink impairments exist to some degree in almost all groups, Baron (2005) developed a ubiquity model of groupthink in which he identified three broader antecedents: shared social identity; salient norms; and low group self-efficacy.
Our own most vivid experience of groupthink in online groups was evident in the “pathological politeness” exhibited by many students in our online discussion groups (Garrison & Anderson, 2003). The literature from the earliest days of the Internet has documented examples of “flaming” and other disruptive behaviour (Lee, 2005; Sproull & Kiesler, 1986). However, in our classes and the transcripts of others we examined, we found just the opposite—many instances in which learners refused to engage in healthy debate or challenge one another’s ideas or assertions. This excessive politeness is likely an indicator that groupthink is lurking, ready to muzzle ideas that potentially strain group cohesion or challenge established authority and ideas—not an atmosphere we were hoping to develop in our graduate courses.
This brief overview of the extensive literature on groupthink underscores the potential negative consequences of facilitating education in group contexts. These are to some degree balanced by the pedagogical value associated with collaboration and productive learning in a community of inquiry. Nonetheless, groupthink lurks, ready to emerge in any group context, and both learners and teachers are advised to guard against the social forces that attract us to familiar solutions that produce less stress and conflict among group members.
Social Capital in Groups
These group connections often persist beyond the course of studies and are a prime mechanism by which the “hidden curriculum” is propagated. The hidden curriculum is often associated with classism and dissemination of dominant ideologies (Margolis, 2001). It is worth repeating that, in education contexts, especially those operating at a distance, cohesive groups also are the primary mechanism for more positive applications of the “hidden curriculum,” including help in “learning to play the game” and learn how to learn in often unfamiliar mediated contexts (T. Anderson, 2001).
The Tools of Groups
A variety of tools has been developed to support groups of learners, the most ubiquitous of which are learning management systems (LMSs), or as they are referred in the UK and some other places, Virtual or Managed Learning Environments (VLEs or MLEs).
Learning Management Systems
Learning management systems were developed to make online course creation and management possible for teachers with minimal Internet expertise. They offer a suite of tools matched to the needs and current classroom practice for average educators and trainers working with adults or high school-level students. Prior to the development of LMS, web course authorship was accessible only to those with considerable Internet and page creation skills, supplemented with unintegrated discussion tools such as newsgroups and email. Many early examples of web-based courses consisted of pages of text, with a few of the presentation, assessment, record-keeping or monitoring tools developed over the years for campus-based instruction. Thus, the arrival of effective and relatively easy-to-use LMSs proved instrumental for the rapid adoption of web technologies both in campus instruction as blended learning and for distance education applications.
A central binding feature of almost all LMSs and related systems is that of roles: there is nearly always at least a teacher role, with the power to control the environment to a far greater extent than a student role. In many systems, roles may be assigned for different features and aspects, and complex organizational forms may be embedded, with different roles for tutors, course coordinators, course designers, systems administrators, teaching assistants, evaluators, and of course, students. This deep structural embedding not only reflects the existing hierarchies but also reinforces them, preventing serendipitous ad hoc role reversals or shifts within hierarchies that might occur in a traditional classroom. The online teacher wishing to turn over control of a class to his or her students may face technical obstacles that make it difficult, awkward, or for some systems, impossible to achieve.
At the heart of the LMS is a system of security, authorization, and access control that allows learners only to enter into course spaces in which they are enrolled, and in many cases links to other components of an institution’s student information system. Most LMS systems create an opening page that links students directly to the courses they are registered in, as well as to a variety of other student services such as the registrar, libraries, student clubs, and so on. Thus, the LMS becomes a sort of personalized portal to the services provided by the institution.
In the early days of online learning, there was a proliferation of homemade and/or unintegrated systems, sometimes composed of repurposed groupware such as Lotus Notes. While several of these were well tailored to the needs of their communities, lack of integration across courses and programs, a disjointed user experience, and above all, the difficulties of maintaining, developing, and sustaining such systems led many to ossify or degenerate into disuse. Nowadays, many institutions support only a single, centrally managed LMS system, to minimize technical support issues, so that both learners and students can become familiar and competent users throughout their time of enrollment with that institution. Similarly, to enhance ease of use, most LMS systems use single login systems so that users need to remember only one username and password to access all of the institutions’ services.
LMS systems continue to increase the number and variety of modules available to instructors, in a “Swiss Army knife” approach that is designed to meet as many teaching needs as possible, while maintaining complexity and choice at manageable levels. Key components of modern LMS systems include organization and display tools with options for printing content on demand, calendars with important dates, quiz creation and administration, asynchronous text conferences, real-time text chats, group space for collaborative work, and drop boxes and grade books for assignment. All of these tools are integrated, and most are equipped with push capabilities such that new activity triggers notification by email or Rich Site Syndication (RSS). In the competitive drive to entice more customers, LMS developers are adding tools regularly, including ones more commonly associated with network learning such as blogs, wikis, and e-portfolios.
One particular developer, Blackboard, has captured a significant portion of the commercial LMS market, especially since acquiring competitors such as WebCT and ANGEL. There is intense competition from smaller companies and products such as Desire2Learn and GlobalScholar, but it is hard for them to make inroads where Blackboard is already incumbent. To some extent Blackboard’s commercial success is inevitable: once an institution chooses an LMS vendor it tends to lock into using it, since the costs of transition, training, and content migration inhibit subsequent movement to rival brands. This means that being first comes with a lot of “stickability,” and Blackboard has—understandably enough for a commercial company with a strong interest in keeping the cash flowing in—not gone far out of its way to enable migration and export.
The main competition for Blackboard comes from outside of the commercial sector. The open source movement has been very active in developing and delivering LMS products, and recent studies are showing that in the higher education applications, they may even be surpassing commercial LMS products in terms of the number of installations (see, for example, the market penetration statistics at Zacker.org [2014]). The growing number of users of open source LMS systems such as Moodle, Sakai, Canvas, and aTutor (to note just a few larger systems of hundreds available), bear evidence that some learning organizations are attracted to the lower initial cost, volunteer support community, and security of code ownership afforded by open source products.
Early fears that such systems would not be scalable have been put to rest by large-scale adoptions made by institutions like the Open University of the United Kingdom (which uses Moodle), who have also contributed generously to the system’s development, as Athabasca University in Canada has done, and many others. Similar to other successful open source software, a variety of companies are now offering training, support, and integration services for these products, in an attempt to meet the needs of institutions that do not wish to develop these services in-house. Interestingly, as Dawson’s (2012) article details, even Blackboard has absorbed companies providing Moodle hosting in a move that surprised many industry followers.
A quick look at the many orphaned applications distributed on the SourceForge repository of open source products reveals that it is much easier to create and release the first version of an open source software package than it is to gather and sustain a community of active developers. Nonetheless, examples such as Apache, Linux, and the LMS systems mentioned earlier prove that it is possible to develop and maintain very sophisticated products over extended periods of time using open source development tools and ideals. Many institutions either making the leap into learning management systems for the first time or fed up with the high costs and lack of flexibility of commercial systems are moving to open source environments. However, while they offer many advantages, like all such systems, portability of data remains an issue. Moving from one system to another, even when both support standards such as SCORM, is often a painful experience, and lock-in, whether deliberate or unintended, is a feature of almost any centralized environment.
An alternative model of hosting in the Cloud has developed in recent years and has been enthusiastically taken up by many smaller institutions, especially schools that do not have sufficient resources of their own to manage the complex software and hardware typically needed for self-hosting. In some cases, governments or consortia that work on behalf of a collection of schools or colleges manage such systems, in others they are directly paid for commercial services, and in others still they are supported by advertising or, occasionally, are free. The risk of such services is primarily in their reliability—terms of service may change, or companies may become bankrupt. However, there are other concerns: ensuring the privacy of their users is especially important where data protection laws are not strong (such as in the US), and they will sometimes be slower than campus-based alternatives. Even if their performance, reliability, security and privacy are sufficient, data portability is a significant concern. If users and their content are bound up with a particular system, the difficulties of moving to another platform are potentially much greater than even a locally hosted server may present. This is particularly significant if the interface plays an important role: even if data are portable, they may still be unusable outside the original platform without the means to present them effectively.
Synchronous Group Tools
The need for virtual teams to operate in real time (vs. distributed time) is expected to become more critical as tasks become more complex. Bradford S. Bell and Steve W. J. Kozlowski, “A Typology of Virtual Teams”
Synchronous activities raise the visibility of all group members, especially those who use the media more effectively. Moreland and Levine (1982) argue that visibility is a key determinant of group participation, and thus group performance. Early forms of group-based online learning used audio or text chats, which were augmented by video to become ubiquitous web conferencing software (Skype, Collaborate, Connect, etc.) used in formal education, business, and personal applications. These synchronous tools have evolved into immersive environments that have attracted much interest from early adopters and researchers, but few sustained educational programs or courses make extensive use of them.
Synchronous activities bring a sense of immediacy and efficiency to group processes. Although we remain appreciative of the increased freedom, choice, and reflection affordance of asynchronous groups, we are aware that many students and teachers prefer the increased sense of camaraderie that often develops quickly through engagement in synchronous activity. In a comparison of asynchronous and synchronous courses, Somenarain, Akkaraju, and Gharbaran (2010) found increased student learning, perceptions of learning, motivation and effectiveness of communications among synchronous groups.
Effective group processes are based on trust, immediacy, and a sense of the presence. Although examples from courtship by mail to the development of social presence in asynchronous text discussion demonstrate that it is possible to develop effective educational groups through asynchronous communication, synchronous communication has many advantages.
First and most important is the sense of immediacy provided by real-time or synchronous communications. Albert Mehrabian defines immediacy as communication behaviours that “enhance closeness to and nonverbal interaction with another” (1969, p. 213). He focused on non-verbal cues that are greatly restricted in many forms of online behaviour—notably those that are text-based. But immediacy also carries a sense of immediate reactions, ones that are rich in body language, voice intonation, and facial expression.
Many researchers have studied the link between educational goals and teacher immediacy (J. Anderson, 1979; Frymier, 1993; Gorham, 1988). Generally these studies find that teacher immediacy increases student motivation to learn, student enjoyment and persistence, and to a more limited degree, cognitive outcomes. Teacher behaviours associated with immediacy include use of humour, self-disclosure, addressing students by name, and asking and answering student questions. Finkelstein (2006) argues that synchronous teaching, with implied increases in immediacy, is associated with each of Chickering and Gamson’s (1987) oft-cited Principles for Good Practice in Undergraduate Education—notably increasing student–faculty contact, student cooperation time on task, feedback, and increasing diverse ways of knowing.
Despite these endorsements, synchronous learning activities are also associated with diminishing accessibility. Not all participants may be available at any given time, and the necessity for participants to gather in a single virtual place or have access to particular and often expensive equipment cannot always be met—especially if full-screen video is demanded to maximize the visibility of subtle nonverbal communication and body language. In our experience of online teaching, we have found that occasional use of synchronous technologies allows for quick bursts of immediacy that help forge group cohesiveness and serves to pace and synchronize the group, but it is best to make restrained use of the tools. Increased pacing leads to reduced learner control (Dron, 2007a, pp. 81-82).
Another drawback of synchronous activities is that they can and often are used to support regressive mimicking of classroom-based and lecture format teaching that not only bores learners but also fails to take advantage of new pedagogies and learning activities afforded in cyberspace. The familiar experience of teacher-led instruction can be transported online with regular video conferencing sessions. However, our experience has shown that the increase in complexity from dealing with off-site issues as well as impairments to clear visualization and auditory interaction create frustrations for those expecting “the same, only at a distance.” For such sessions to work there is a need to provide plenty of support and a thorough grounding in protocols to avoid confusion and failure, like ensuring an adequate gap between asking a question and expecting a response, avoiding talking at the same time, avoiding real-world distractions, and the appropriate use of text chat. It is also often a good idea, especially in large groups of novice users, to allocate a second moderator to help manage technical issues. Effective groups therefore tend to make use of synchronous technologies judiciously and ensure that the convenience cost is warranted by collaborative interaction.
Synchronous learning activities come in a wide variety of formats and media. Both audio- and videoconferencing were used extensively in distance education formats for many years before their migration to cost-effective web technology. Text chat was the first and still the most common form of synchronous online interaction, and was even used as the primary tool in the earliest forms of immersive interaction (for example MOOs, MUDs, and Palaces). Text chat is, however, dependent upon typing skills and therefore is associated with the development of shorthand forms and lingo that can exclude new users from group interaction.
We are most impressed with web conferencing software as cost-effective and accessible group educational technologies (for example, Elluminate, Adobe Connect, WebEx, LiveMeeting, DimDim, etc.). Web conferencing supports multiple forms of synchronous interaction, including voice, text, low-resolution video, and presentation support. In addition, most systems support drawing on whiteboards, breakout rooms, application sharing, polling, and group excursions in cyberspace. From an accessibility perspective, web conferencing allows very easy recording and later playback for group members who are not able to attend real time sessions. Recently, student response systems have been used in classrooms, and early results are showing increases in enjoyment, attendance, and even learning outcomes (Radosevich, Salomon, & Kahn, 2008). Student response through polling is a standard feature of most web conferencing systems for online use, thus providing a tool that enhances learning at a cost that is much lower than that associated with distributing “clickers” to campus-based students.
The use of synchronous interaction is also related to the complexity of group tasks. Simple dissemination of content (as in a lecture, or a reading in a textbook or article) likely gains little from synchronous interaction. But as the need for negotiation and collaboration increases, so does the need for real-time interaction (Bell & Kozlowski, 2002).
In our work, we have evaluated the effectiveness of extending groups across multiple schools to teach high school courses to rural students via videoconferencing technology. We found that although the videoconferencing has value, especially in terms of enrichment, along with professional and administrative value for teachers, as a primary tool for distance education it creates a rather impoverished and teacher-centric learning environment (T. Anderson, 2008).
Immersive Worlds
What for decades has promised to provide the most engaging form of synchronous activity is that which takes place in immersive environments such as SecondLife, Project Wonderland, or Active Worlds. We have studied early examples of formal educational encounters in immersive environments, and conclude that group enhancing forms of cognitive and teaching presence can be developed in these environments and that opportunities for greatly enhanced social presence abound. McKerlich and Anderson argue that “as the tasks a virtual team is required to perform become more complex and challenging, requiring greater levels of expertise and specialization, a higher premium is expected to be placed on synchronous workflow arrangements and the roles of individual team members will be more likely to be clearly defined, fixed, and singular” (2007, p. 34).
However, at the time of writing, there were numerous hurdles to overcome before such systems enter the mainstream. It is hard to learn to use them, with different controls and capabilities from one system to the next, and complexity in even simple tasks such as moving around. Although touted by their creators as the “3D web,” nothing could be further from the truth. Only the most primitive of steps have been taken to enable a truly distributed and open environment like the World Wide Web in 3D immersive spaces. It was something of a breakthrough when, in 2008, IBM technologists were able to teleport an individual (without clothes or distinguishing features) from one immersive environment to another, but little mainstream development has occurred since then. Technologically, such environments still require powerful machines to operate effectively, and so far, nearly all rely on separate downloadable software as opposed to running in simple ubiquitous clients such as web browsers. This state of affairs may not last long, however. In specifications for HTML 5, real-time, 3D, and immersive environments are being considered. Various real-time technologies are already fairly advanced— Google’s Shuttle5 (code.google.com/p/shuttle5/) provides Jabber chat and uses HTML5 support for websockets, an emerging standard for enabling various protocols to work within web browsers.
Both Google and the Mozilla Foundation are working on ways to enable virtual immersive spaces within the browser, which may lead to standardization and distribution beyond the isolated server spaces of today. If and when this occurs, we may see the flowering of a 3D immersive web, perhaps developing into something not too far removed from William Gibson’s original vision of cyberspace.
Group Toolsets in the Cloud
The ever-present closed email list has been and continues to be the workhorse of many effective groups. Email has reached a saturation point in many schools and workplaces such that one can count on learners having access to email and the ability to check their accounts regularly. This familiarity with the tools, in addition to the “push” to the attention of a group means that many groups in both formal and informal learning contexts rely on the group mailing list as the primary means of communication. Recently, large Net companies (Yahoo and Google Groups) and new Web 2.0 companies (MySpace, Facebook, etc.) have expanded and integrated new features into their group email tools to create rich group work and learning environments. These collections not only support email but also retain and organize email posts in web formats so that group members need no longer store individual copies of email in their increasingly full mailboxes. Rather, they can search and retrieve postings from the group archive. This is very useful for learners who join the group at a time after group communication has already begun. These systems also support a host of add-on features such as common calendaring, document sharing, picture archiving, group to-do lists, polls, surveys, and other tools designed to afford both synchronous and asynchronous communication among group members. A number of companies have recently stepped into the realm of educational service provision, offering richer and well-managed learning environments for group use in classes where existing tools are weak, such as Udutu and CourseLab, as well as many hosted versions of existing LMS products like Moodle and Blackboard.
Effect of Groups on Attrition
Distance learning has notoriously high attrition rates, though this is by no means true across the board (e.g., Guilar & Loring, 2008). Among the many things that help to reduce attrition rates, a central pillar is social support. While there are many factors that can lead to attrition and many mitigating factors that reduce it, sustained motivation is essential. It is very easy, without cues like the requirement to be in a particular place at a particular time, to allow other things to take precedence, so motivation plays a crucial role in success to a greater extent than it does in face-to-face learning. Ideally, that motivation will be intrinsic: rather than being coerced, cajoled, rewarded, or even working to achieve goals that align with self-image and self-worth, it is better by far to simply want to do something in the first place. However, intrinsic motivation is easily undermined; often by the very things we try to do to achieve it in the first place, such as reward systems or punishments (Ariely, 2009; Deci, Vallerand, Pelletier, & Ryan, 1991; Kohn, 1999).
According to Deci and Ryan (2008), there are three distinct components to intrinsic motivation. As a rule, if learning tasks give people control, are within their range of competence, and provide relatedness with others, they will enable intrinsic motivation to emerge. Without any of those features, intrinsic motivation is almost certain to be quashed. Although the relatedness portion of this triangle may emerge in, say, family settings, friends, social networks or public acclaim, a system for learning that embeds sociability is far more likely to succeed than one that does not. A social component is therefore an extremely important means of avoiding attrition. There are many examples of this recorded in the literature. Royal Roads University, an online Canadian institution, famously achieved completion rates approaching 100% by employing the relatively simple technique of fostering cohorts, groups of mutually supporting learners who helped one another when the going got tough, even averting disaster in classically dangerous times such as changes in job, bereavement, or illness (Guilar & Loring, 2008). A closed group is especially effective at providing such support because shared goals and values, combined with a culture of mutual support, can help to foster strong community ties.
Effect of Groups on Self-Efficacy
Self-efficacy—the belief that a learner can accomplish a goal—has long been associated with performance and persistence (Bandura, 1977) and resulted in a major theory and considerable study of self-efficacy in both classroom and distance education. In a major review of the sources of self-efficacy, Usher and Pajares (2008) isolate four sources of self-efficacy found in the considerable research literature. The largest source is mastery: having accomplished one goal leads to confidence that additional goals can be achieved. But after competency, the next two sources are decidedly related to social interactions that are common in group interactions. The first of these is labeled “social persuasion”: inducements made by other group members and especially teachers increase a learner’s sense that they can accomplish a challenging learning goal. Perhaps this is most clearly visualized in the sports group, where the coach and teammates’ almost continuous communications that “you can do it” are vivid social persuasions leading to increases in self-efficacy. The second source of socially induced self-efficacy relates to vicarious experiences, where learners are able to observe the success of peers and come to believe that they too can achieve these goals. Obviously the intense interactions that define group activities give rise to many opportunities for such vicarious experience, with resulting increases in self-efficacy.
Design Principles for Group Applications
As we have already observed, groups differ from networks inasmuch as they tend to have:
• Structure and leadership
• Fixed periods of operation and identifiable stages of development
• Explicit membership
However, things are complicated by the latent possibility that groups may evolve into networks and back again. There are two distinct ways for designers to cope with this:
1. Ignore the problem and leave the network aspect to a different application or applications.
2. Build support for transitions to network modes into the software itself.
We favour the latter solution. We will start, however, by briefly examining the features needed to support group modes. We will not go into great depth on this topic: software to support group interactions has been available for several decades, and we do not intend to suggest new or revolutionary approaches to its design here, apart from in terms of the transition to network modes of interaction.
Structure and Leadership
Software designed for groups needs to embody roles that provide affordances, capabilities, and levels of control to different people.
It should be possible to see the mapping between the group structure and the individuals and resources composing it. In other words, we should be aware of the organizational structure of the group, with clear signals for different roles. This may be as simple as labels or icons to indicate that a person is a teacher or group leader, or it can be more sophisticated. For example, we could display the organizational structure as a tree, or indicate ownership of resources and discussions by images or text.
Fixed Periods of Operation and Identifiable Stages of Development
• Any group system should be capable of having a specified beginning and end date/time.
• Resources and discussions for groups should have the facility for expiring or archiving
As groups pass through various phases, they need different kinds of electronic support, and these should not be mixed up. For instance, relics of experimental sharing and learning should not persist once groups have become self-sustaining and apply knowledge critically. Allowing or requiring resources and discussions to expire (or to be sidelined through archiving) is one approach to dealing with this issue. Another is to parcel the learning landscape in order to keep spaces associated with different development phases separate.
Explicit Membership
Groups imply membership, which also implies that those outside the group need to be excluded. Any application supporting groups needs explicit controls over not just authentication but also authorization. In addition, such a system needs support for subgroupings, including groups of individuals and the virtual spaces that they use. For example, this may be used to separate spaces for subgroup interaction (a common feature of LMSs), or at a higher level, to separate out instances of courses. This leads us to consider transitions from group to network modes.
Transition from Group to Network
It is not uncommon for groups to evolve into networks, especially in educational applications. Typically, people who have been in a class together may stay in contact, and even if they don’t there is a great deal of potential value in using the alumni of a given course to provide support, encouragement, and other benefits to new cohorts. Unfortunately, many systems primarily designed for closed groups (including most LMSs) do not make it easy to do that, and such networks tend to arise despite the system’s design rather than because of it, through email or other more network-friendly social applications (Facebook groups, for example).
To support the transition from group to network modes, it would be better if designers developed group applications that fade into networks rather than those that abruptly end. The common approach to closed course management that is used in many institutional LMSs is to archive old courses when they have ended, thereby ending a given student’s association with the course. Indeed, data models behind the applications enforce this by requiring separation for each instance a course runs. Because of the data models behind many LMSs, there is little alternative to this approach because were we to leave ex-students and their discussions active, it would be confusing to new cohorts. In unpaced/self-paced learning there are further problems as, without a specific cohort to be a member of, relics of old discussions can quickly evolve into a chaotic tangle that is counterproductive in learning. In a paced (cohort-based) course it is very valuable to make use of subgroupings for each instance of a course, but to maintain either a supertype or superclass of the course that allows users to maintain membership in the broader network.
For unpaced courses, the problem is more complex. Learners who progress through a course at their own pace, typically with discontinuous overlapping start and finish times, are in some senses a group with shared goals, a hierarchical organizational structure, clear membership and so on, but in some senses they are a set because individual ties are typically very weak, and while purposes are shared at the large scale of the course, areas of interest at any given time will typically differ.
Conclusion
In this chapter we have overviewed both the power and liabilities of group models of teaching and learning. Groups can be used by educators to create the support, solidarity, and community that encourage learners to continue the often-strenuous work of effective learning. They are also important vehicles for transmitting the cultural capital, often referred to as the hidden curriculum, which is associated with the experience of formal education.
The benefits are balanced with the tendency for groups to suffer from groupthink and serve as cliques that bar access for some to group privileges. In formal education, groups often suffer from teacher dependency than doesn’t allow learners to practice the skills or develop the self-efficacy attitudes associated with selfdirected and lifelong learning. Nonetheless, we have seen the evolution of groups from place-based entities to ones that can thrive and be effective in blended online and place-based format, and on to groups that operate effectively with only online interaction and collaborative work.
There are some notable downsides to the use of groups, one of the largest being that such approaches typically impose heavy restrictions on time and pace, and distribute control in ways that may not benefit all learners. Beyond these problems, they scale badly and are very expensive to run (Annand, 1999). The organizational complexity of managing large numbers of group-based learners and the effort involved in sustaining group technologies means that more innovative ways need to be found to gain the benefits of groups at a lower cost and without the concomitant loss of learner control that they necessarily entail.
Connectivist pedagogies appear to offer such an alternative, and with that in mind, in the next chapter we move beyond groups to the fluid and emergent structures we refer to as networks. | textbooks/socialsci/Education_and_Professional_Development/Teaching_Crowds_-_Learning_and_Social_Media_(Dron_and_Anderson)/04%3A_Learning_in_Groups.txt |
LEARNING IN NETWORKS
Most learning is not the result of instruction. It is rather the result of unhampered participation in a meaningful setting. Most people learn best by being “with it,” yet school makes them identify their personal, cognitive growth with elaborate planning and manipulation. Ivan Illich, Deschooling Society
In this chapter we delve into a detailed discussion on the social form of networks, with a focus on the learning opportunities and challenges associated with this class of social interaction. Networks are a central social form in human societies. Sociology, anthropology, business, and other disciplines have studied their function and form for many decades, and there is ample literature on social networks in a wide variety of communities. However, networks have been used to a lesser extent in formal education, at least partly because their loose form often conflicts with and can be disruptive to institutional structures. They are not bound by processes, roles, or deliberate architectural sculpting. They can be formalized, but not formally constituted. And yet networks are among the primary knowledge conduits of the world; throughout our lives, we learn from people that we know. The spread of knowledge through a network closely resembles the spread of infection: learning is contagious (Kleinberg, 2007), for good or ill.
Recently, the development of low-cost and portable devices allowing for network development and engagement anywhere/anytime has accelerated interest in and the use of networks for distance learning. In the previous chapter, we saw that group norms and customs evolved largely in face-to-face contexts, in which presence, trust, and shared environment created the background context. Today’s learning networks, however, operate and evolve primarily in a mediated context. There are new possibilities networked technologies enable that were difficult or impossible to reach prior to the advent of cyberspace. In this section we detail the underlying affordances of networks as a background to examining the learning activities and contexts that can be expected to thrive under these conditions.
Defining the Network
A network, in the loosest sense, consists of nodes (the points on the network), and edges (the connections between them). Networks are not only visible in human interactions: in nature, ecosystems, chemical systems, geological systems, galaxies and solar systems can be viewed as networks. Similarly, designed physical systems such as the Internet, transit systems, power grids, and roads can also be viewed as networks. In systems that involve humans, networks can be seen in everything from the social connections between individuals (Wellman, Boase, & Chen, 2002) to the relationships of actors and actants within a dynamic system (Latour, 2005), from the epidemiologic patterns of disease diffusion (Watts, 2003) to the interactions that occur within a city (Alexander, 1988; Hillier, 1996). Human systems share much in common with their inanimate counterparts and obey similar dynamic laws (Watts, 2003). Our focus, however, is not so much on the abstract or even physical structure of the network, but on the social structures it enables for learning.
Networks are Concerned with Individuals
It is possible to see networks in any learning engagement that involves other people, including within, across, or beyond the perimeters of a group. Networks are constituted in connections not as formal or informal processes: they are of a different ontological type than a group. Membership of a group is by definition membership of a network, but this does not negate the value of understanding group processes as distinct from the network: they are different kinds of things. Although concerned with human interaction, the social network-centric view of the world is, perhaps ironically, heavily focused on the individual. Indeed, Rainie and Wellman (2012) explicitly describe this form of engagement as “networked individualism.” It is possible for a researcher, informally or formally, to examine the topology of networks and explore their nodes and edges, and to perform analysis of the forms they take as though they were distinct entities. However, lacking a designed structure or concept of membership, from the perspective of any individual member of a network it is constituted egocentrically, as people with whom one has a connection of some sort. We do not do things for the good of the network as we do for the good of the group because this makes no sense— it is not an object as such. It is simply the description of our many connections with others, and with the visible limits of these connections.
Networks are Uneven
Diagrams and maps of social networks typically show multiple threads connecting network nodes or members in complex arrays. The hierarchical structures of groups give way to structures that are fluid, complex, and that evolve to create new linkages as old and unused ones atrophy. The network structure forces and affords individuals and sub-networks to engage in responsible decision-making for themselves rather than relying on others to make decisions or filter information flow. In aggregate, the people in a network make decisions and move in specific directions, but the direction and focus of this movement cannot usually be dictated by any individual member. Rather, in the interactions of networks, members’ directions, strategies, and ideals are created and enacted. It is, however, an oversimplification to suggest that networks are topologically flat structures where all play an equal role. Small-world networks are an extremely common form in social systems, with parts of different networks joined by highly connected nodes and supernodes that are typically of greater relative importance than those with fewer connections, at least when we are looking at flows of information or feelings. However, this is a complex area of ongoing study: while highly connected nodes with many edges are important to the spread of knowledge through a network, they are not necessarily the most influential nodes in a human system, nor do they effectively close connections among other nodes. Rather, they are necessary conduits through which knowledge flows and may be filtered or transformed.
The unevenness of networks relates not just to their topology but to their temporal characteristics. Activity and clusters within networks occur in bursts and are often sporadic, with hard-to-predict ebbs and flows. This is unsurprising given that, unlike the group, there is no intentional coordination of behavior in a network. Topics of interest emerge for a large number of reasons, and these spark conversation. Sometimes a particular blog post, article in the media, notable piece of news or TV segment may act as a catalyst for conversation. Sometimes, the internal dynamics of networks themselves spread ideas and dialogue. The spread of memes, replicating ideas, phrases, or, most often in modern cyberspace, images of cats, is easily facilitated through networks.
Networks are Uncertain
Network learning is qualitatively different from group-based interaction because it introduces elements of both uncertainty and opportunity. The audience for a networked communication is the heterogeneous members of that network who may share some values, interests, and qualities in common but, beyond the reason for the connection in the first place, are unlikely to share more. Groups share homogeneous goals and norms, whereas the differences between people and their interests in networks provide opportunities for the emergence of new friendships, development of social capital, emergence of conflict, and other unanticipated instances.
It is this openness to the possible that both attracts and repels potential network learners. For some distance learners, the lack of face-to-face interaction means trust can only be built after considerable exposure to group interaction, and they gain both personal and professional understanding of one another, combined with the trust engendered by context and norms that arise from membership in an institution or class. For others, the group’s homogeneity creates sameness and boredom, with restrictive constraints entailed by the need to work at similar times and at a similar pace to others in the group; they seek out the network for its capacity to provide exposure to the learning opportunity of the unknown.
Networks are Diverse
We are typically connected to different people for different reasons. They may be friends, we may meet them at conferences, share groups with them, interests, locations, buy things from them, meet them at a party, know their aunt: the possibilities are endless. What defines a network is the sum of the people with whom we have a connection for whatever reason. The lack of homogeneity in networks means that problems that are shared with them are viewed from multiple perspectives, increasing the potential range of solutions and creative ideas to draw from (S. E. Page, 2008).
Networks are Clustered
The corollary of there being multiple reasons that we are connected with others is that it is possible to cluster people we know into different, typically overlapping sub-networks. Subnets are characterized by Google+ as “circles,” which is a useful term that we commonly use to distinguish different parts of our network. We have different circles of friends, people who share professional interests, casual contacts, and so on. These subsets of networks make it easier to identify those who might help us in different learning contexts. If we have the technologically mediated means to distinguish them, we can focus questions or things we share on those who are most likely to have an interest or knowledge about them.
Networks Foster Cooperation
The network provides an ideal context for sharing information, ideas, and questions as opposed to collaborative working, where roles and rules are more appropriate. But sharing itself is not a unitary concept and has many culturally, contextually, and individually defined dimensions. Talja (2002) extracts from the literature on academic research communities four types of sharing activity:
1. Strategic sharing: information sharing as a conscious strategy of maximizing efficiency.
2. Paradigmatic sharing: information sharing as a means of establishing a novel and distinguishable approach or area.
3. Directive sharing: information sharing between teachers and students, or employees and employers or other networkers seeking to perform a specific task.
4. Social sharing: information sharing as a relationship- and community building activity.
Networks in learning contexts are used for each of these four tasks, and the network gains in value when any of them bear fruit, as demonstrated by networkers’ satisfaction and use.
Networks are Borderless
As Milgram (1967) famously showed and others have since confirmed, we are all connected to one another via a very small chain of people. In “Six degrees: The Science of a Connected Age,” Watts (2003) reports on experiments that confirm the chain between one person and another is six or less, whoever they may be, wherever they may be in the world. In essence, viewed from above, the world can be seen as one huge network of people.
Networks are not Technologically Constituted
Networks are constituted in terms of connections with others and, while technologies can support and enhance them, there are no consistent or defining rules, processes, or methods in a network, whether implicit or codified. Networks are not, in and of themselves, technologies. Of course, individuals may overlay all sorts of processes on a case-by-case basis, and this is often the way networks coalesce into groups: some form of codification is created that distinguishes them from a loose assemblage, including the establishment of names, purposes, ground rules, schedules, and so on. Networks themselves are diffuse, bottom-up, and have undefined perimeters. Though often technologically enabled and benefiting from technologies that reveal them, no technology other than language (at least in most cases) is required for them to form.
The lack of technology or intentional architecture means that, if they are to be used in intentional learning, more effort is needed on the part of the learner. The roles, processes, and methods embodied in groups are designed to make things easier, and they are not available to the networked learner. While the group-based learner may be actively engaged in the social construction of knowledge, he or she is seldom involved in the construction of the process to achieve that. To learn deliberately is to assemble the means and methods of doing so. In groups, they are assembled for you. In networks, you must assemble them yourself. Networked learning, as Connectivism suggests, is as much about acquiring meta-skills in learning as it is about the learning itself. In the absence of a teacher role, this typically means that the networked learner must discover sources of inspiration from within the network through role models, or discover the learning design in some other way. Typically, the process of doing so will mean discovery of instructional resources in the loosest sense of the word, leaving the networked learner in a hybrid position: employing behaviorist/cognitivist tools yet at the same time engaging in authentic social practice.
Many Learners are Loosely Tied
Internet scholars have written about the distinction between “dense bounded groups” and “sparse unbounded networks” (Wellman et al., 2002). This work flowed from the study of informal organizations in wired communities, but similar forces are at work in the socializing modes found in networked-based groups. Wellman et al. (2002) found that group and network relationships are common in both work and community contexts. They note that groups are most often associated with locally bound communities where relationships evolve through proximity, even in the absence of choice. We are forced to interact with those we live, work, and attend class with, regardless of any affection or interest. Distributed networks, of course, eliminate this constraint and allow us to form both networks and groups with people who may be very widely physically distributed. Beyond physical proximity, networks are supportive of the creation of weak ties (Granovetter, 1973) that serve as bridging connections to other groups and networks. Networks often have higher percentages of weak ties than strong ones, but each type has advantages and disadvantages. Strong ties are associated with closeness, multiplexity (multiple forms of interaction), and higher levels of intimacy, immediacy, and frequency of interaction. These are generally positive attributes, but strong links can also lead to “amplified reciprocity,” where individual freedom is constrained due to obligations of mutual support, inertia, and lack of interest in building relationships outside of the group (Gargiulo & Benassi, 2000). Networks and other models of human organization associated with weak ties offer greater diversity, provide wider and less redundant sources of information and opinion, and increase individual and community forms of bridging capital (Ellison, Steinfield, & Lampe, 2007).
Gargiulo and Benassi found that the development of social capital is not directly related to the creation of stable and secure strong ties; rather, “managers with cohesive communication networks were less likely to adapt these networks to the change in coordination requirements prompted by their new assignments, which in turn jeopardized their role as facilitators” (2000, p. 183). In rapidly changing contexts, the creation of social capital remains important, but change requires flexibility and the diversity often associated with weak ties rather than stable, strong relationships. Moreover, Burt argues that these weak ties foster “structural holes” or disconnections that allow the nimble to exploit opportunities “to broker the flow of information between people and control the form of projects that bring together people from opposite sides of the hole” (1997, p. 340). Those with more extensive network relationships are thus “at higher risk of detecting and developing good ideas, because of which they enjoy higher compensation than peers, more positive evaluations and faster promotions” (Burt, 2009, p. 46), giving them more opportunities to create knowledge, social capital, and wealth.
Networks, with their bridging of structural holes, can in principle reduce the propensity for negative and inhibiting group behaviors and culture. However, the lack of structure also means that commitment may be lower, or at least of an ad hoc and unpredictable nature. Too much diversity can also be counterproductive, leading to chaos or randomness (S. E. Page, 2011). Without some redundancy, the dynamic and changing nature of networks can leave gaps when those filling a particular niche leave the network or move to the outer limits of its boundary.
Cooperative Freedoms in Networks
The degree of freedom afforded in a network-based learning context is typically very high (see figure 5.1). This is both a blessing and a curse because choice is not equivalent to control (Dron, 2007a). Too many options, especially in a learning context where we may have little idea about appropriate tools, methods, content, or individuals from which to learn, can make it very difficult to choose between one path or another, and may leave the learner in a worse position for control than if he or she had no choice at all. The archetypal theory of networked learning, Connectivism, shows this in sharp relief. In many ways, connectivist methods are concerned with the meta-level of learning: learning how to learn in a whitewater world of constant change and uncertainty.
Figure 5.1 Notional cooperative freedoms in a network.
Time
Compared to group-based ways of learning, freedom of time in networked learning is typically high, though there are often dependencies relating to the availability and activities of others in the network. If a learning path is instigated by a particular blog post, or involves interaction with others, the availability of other people determines when and how participants might learn. This is very dependent on context though: some kinds of learning conversation in a network can spread out over years while others, such as those about a recent news topic, can be over in hours or days. One of the most distinctive features of network-based learning, as the Connectivist model suggests, is that it is typically self-instigated rather than imposed by a designer, so not only can it begin with an inspiration from an interaction with others, it can also emerge from the individual. Learning often starts with a process of creation, be it a blog post, video, discussion post, question in a forum, or simply a comment on another post.
Place
As with all cyberspace learning, freedom of place is very high in network-based learning. There are a few exceptions where location may be important, for instance where a network develops through augmentation of a physical space by geotagging or virtual cairns left by others in a network (Platt & Willard, 1998), but these are relatively rare.
Content
Freedom to choose content is, by definition, high in a networked learning model. Net-based learning is often concerned with discovering and tracing paths to content through a network, for instance, following links posted in Twitter, LinkedIn, or Academia.edu, and freely choosing what and from whom we learn. There are some subtle constraints, however. An individual’s view of the network is always limited and localized. Filter bubbles, where machines or individuals filter out all but confirming sources of data, can emerge where preferential attachment leads to certain resources, and particularly the content created by a limited range of popular network nodes that is far more likely to be selected. While the network may extend fuzzily outward to encompass almost anything available in cyberspace, the emergent organization of a network can strongly emphasize some while leaving others outside of it, only slightly connected and with little chance of being found. This is not necessarily a bad thing—most certainly, the range and diversity of content in networked learning will always be far greater than in a group-oriented learning context, and exponentially greater than in an instructivist setting. However, there is a concern that “popular” is not necessarily equal to “useful”: what appeals to a diverse collection of people who have some shared learning goals but not others may emphasize the bland, the attractive, the powerfully stated, the easily digestible, and so on. This is particularly risky because connectivist models place a great deal of emphasis on members of a network being contributors and creators rather than consumers. Content is often curated, mashed-up, re-presented, and constructed or assembled by those in the network. This is a wonderful resource when seen as a co-constructed and emergent pattern of knowledge-building, but without the editorial control that a teacher or guide in a group provides, it can lead to network-think, a filter bubble in which social capital rather than pedagogy becomes the guiding principle. So, while freedom is high, there are still patterns shaping the selection of content, and unlike those in a more constrained group setting, these may not align well with learning needs. Furthermore, the wealth of content that is proactively flung at us in social networking systems may lead to an excess of choice, and hence diminished control (Schwartz, 2004).
Delegation
While grouped forms of learning include the reassuring role of a teacher to whom one can delegate control, with the concomitant risk that the teacher may take more control than one might wish, the strong emphasis on an individual’s learning path in networked learning, especially given the read/write mode expected of networked learners, makes it much harder to delegate control to another. Networks have a social shape, not a cognitive shape, and the emergent guidance that is inherent in the form may not lead us to useful places. Because the path of connectivist learning is not carefully planned, it is not possible to fall back on a predetermined route, and the networked learner must therefore rely on the goodwill and availability of others if he or she needs to let go of the learning reins for a particularly complex or challenging sequence of learning activities. The problem is exacerbated by the fact that learners, by definition, do not know the subject they are trying to learn sufficiently well and therefore may not know how to ask the right questions, even if someone in their network may know the answers. Of course, should learners find the right person to help in their network, it may well be possible to delegate decisions about the learning trajectory to them; at this point, teaching becomes one-to-one, rather than a function of the network, with all the benefits that entails.
Relationship
Freedom of relationship in a networked context is maximized. Within a network we choose how, when, and whether to engage with others, without any constraints beyond that those we engage with must be, by definition, part of the network. Again, networks are about local interaction, not in the geographic sense, but in the sense that they are only ever perceived in relation to an individual node and its neighbors: networks can connect us with others only where connections between adjoining nodes are available to us. While a group may be viewed as a whole, a distinct entity apart from the people within it, a net is constituted only in the local connections between people.
Medium
The choice of medium in networked learning is typically very high. The networked learner is typically able to select from a vast variety of media to suit his or her needs and may deliberately cultivate networks that make one or another medium more significant. For example, networks of people on YouTube will make video a dominant form, while those in a social network for book lovers such as goodreads.com or even Amazon will tend to favor text or images.
Technology
The only constraints on the choice of technology in network-based learning are that the tools and processes we use must facilitate connection. They should directly or indirectly be connected with the network. We also acknowledge, however, that many of these tools are expensive, and thus there is an inherent constraint— especially on those with little or no disposable income.
Method
While there are no particular constraints on methods that may be used as a consequence of being in a networked-based learning context, the nature of the social form precludes the kind of controlled, paced, formalized pedagogies that may be the norm in a group-based learning context. Networks are very good for surfing ideas, following paths wherever they may lead, going on tangents, and connecting disparate ideas and skills, but to follow intentionally focused paths they are more limited. Having said that, there is nothing to prevent a learner from using the network to discover focused groups or behaviorist/cognitivist resources in order to take a structured path to learning, but the network form itself is by definition emergent and lacking in distinctive pedagogy. Connectivism, the most fully formed of networked learning theories, is more of a meta-pedagogy, specifying an approach to exploration and exploitation rather than designing a learning path.
Pace
Net-based learning typically offers a great deal of control of pace at a macro level, but the interdependence of learning with others can, like group-based ways of learning, lead to dependencies on the availability and interest of others. When a learning conversation opens up around, say, a blog post or a Twitter stream, it is important to engage in a timely fashion in order to be part of the learning dialogue. This dependence on the availability of others, is however, notably offset by the persistent nature of much networked communication. For instance, someone may respond to a blog post months or even years after it was posted, reviving interest and activity in it after a long period of dormancy. The pace of interactions and the expectation that it is a timely stream makes this less likely to occur in Twitter or similar micro-blog technologies.
Disclosure
Most computer-based systems with social networking facilities provide a significant amount of control over what is revealed and to whom, Facebook’s constant battle to remove such control notwithstanding. Assuming the technology allows it, the networked learner is free to reveal as much or little as he or she wants. Having said that, there are limited benefits to a social network if everything is kept hidden. The inherent lack of structure and norms in a network means that, with the ease of digital replication that most social networking systems provide, information provided to a small range of individuals may spread through their networks to others.
Transactional Distance and Control in Networks
Moore’s theory of transactional distance (1993) assumed a formal learning context in which a single teacher or teaching presence was engaged in a learning transaction with a single learner. We have seen that, in group-based learning, the teacher role may be taken by other learners, which can lead to a reduction of transactional distance when measured as a communication or psychological gulf, but an increase in distance when measured in terms of control.
In a networked learning context, the teacher role is distributed among an indefinitely large number of teaching presences, from blogs to peers, from key network nodes to comments on discussion posts. An individual may be both teacher and learner simultaneously. Negotiation of control in networks is a constantly shifting, emergent phenomenon in which the learner is engaged in multiple relationships, each with their own dynamics of control and psychological distances but, in aggregate, transactional distance is low on control in both of these dimensions. From a learner perspective, control can increase and communication/psychological distance can diminish. However, that comes with a strong proviso: an increase in the number of choices may, without the means to choose between options, reduce the control of the learner. Having many choices is not the same as having control (Dron, 2007a; Schwartz, 2004).
Examining this more closely, if there are just two people in a network, then transactional distance may be lower or higher depending upon the strength of the network tie, bearing in mind that, as we have already observed, a dyad may be seen equally as a group, net, or set. If, say, we post a tweet and it is responded to by a follower of someone we follow, then the communication distance is low but the psychological distance may be quite high: we do not necessarily know them or their motivations, and understand little of the context in which they are writing. If the friend that links us then responds, this not only perforce reduces the overall aggregate psychological distance but also the psychological distance between us and the original poster, because their post has gained greater validation by the response of our friend, helping us to understand more of the context and value of their original contribution.
Network Toolsets
In this section we describe some of the functionalities of the current generation of network technologies, relating them to the needs of learners who are making use of their networks for learning. Many of these functions are contained in suites of network tools such as those found in Facebook, Ning, Elgg, and others. However, whether through aggregation standards such as RSS and Atom, service-based architectures, widget-based systems, or even by embedding framesets, learners are also often able to “mashup” their own network tools to create personal learning environments. These mashups may be more or less integrated.
Many people maintain more than one network channel on their cellphones, tablets, and computers, with instant messaging applications, social network tools, and feed aggregators providing a constant flow of traffic from them. These are often bound together and linked through tools that integrate them in tablet apps, websites, and other devices: for example, a large number of iOS or Android apps allow content to be shared with other apps, such as Twitter, Facebook, or Google+ that may themselves be network-oriented applications. Given their diversity, it is thus challenging to describe in an exact sense the functions of network tools since they are constantly morphing in look, feel, and function, so our categorization is broad and flexible. In general terms, and in keeping with the individualist focus of networking, most network tools provide one or more means of representing the self, through profiles, presence tools, avatars, and so forth. Networks would be of no value without the means to communicate with others in them. As a result, network tools also provide a means of creating content and sharing it with others. These tools also normally offer facilities for building and sustaining networks of connections. We expand on these main features and some of their corollaries in the subsections that follow.
Profile Tools
The central component of most social networking systems is the profile, a means of displaying information about an individual used by others to find and add them to their networks. Profiles usually contain images (avatars) and a variable amount of other information about the person, which can range from just a name and perhaps location to a complete curriculum vitae, as well as shared content, records of interactions with others, contact details, and other information such as collectively generated reputation indicators and badges (we will explore these in depth later). Profiles serve as proxies for identity to help learners identify those with relevant interests or skills in their network, and assist them to discover more about people before connecting them to their own networks.
Content Creation and Sharing Tools
Networked learners, through participation in networks that reify their interactions, are almost always “prosumers”—people who both consume and produce network content (Bruns, 2008). Blogs, wall posts, instant messages, tweets, file sharing, video sharing, photo sharing, podcasts and many other tools for sharing content are an essential part of a modern social networking system, providing the medium and focus for further interaction to occur. The creation of content is one of the central requirements of connectivist learning pedagogies, and the means to create shared content is thus pivotal in providing tools for knowledge construction and tools for sharing and expanding on that knowledge.
Communication Tools
For network-oriented tools, there is a very blurred line between content sharing and creation tools and those whose main purpose is communication. The facility for commenting is ubiquitous, found on everything from photos and videos to shared blogs, curated items, and bookmarks, so in a sense, almost all modern social media facilitate communication. However, some network-oriented functions are concerned with direct dialogue: email, instant messaging, videoconferencing, IP telephony, SMS, direct messaging tools in social networking systems, discussion forums, and so on provide the means to contact one or more people in a network, typically managed through a list of contacts or address book. The means to carry out a sustained dialogue with one or more people in a network facilitates many social pedagogies in both the social constructivist and connectivist traditions of learning. The main difference between such tools and the embedded dialogue that surrounds blogs, for example, is the flexibility of purpose. While comments on blog posts can and frequently do diverge from the topic of the original post, the post acts as a basin of attraction, an object of dialogue that seats the conversation, and usually persists over time, while communication-oriented tools are concerned with the ephemeral process of conversation.
Presence and Status Tools
Networks allow learners to make their presence known or else conceal it, both asynchronously (typically through profile settings) and synchronously (e.g., status indicators in an instant messenger). Presence notification can support presence in physical space, as provided by the tools for mobile social networking, or for helping to identify those in social proximity who share a common interest in an educational- or discipline-related interest. Presence indicators are also being added to text, audio, and video communication and conferencing tools to allow us to see which of our friends or colleagues are available for instant answers, feedback, and interaction. Of course, this sense of presence must be under the control of the individual learner; there are times when we welcome the presence of “kindred souls,” while there are other times when we need the freedom to protect and maintain our privacy and anonymity.
Often related to presence tools are status indicators that reveal current activities, interests, or moods. These may be as simple as “at a meeting” indicators or emoticons, or may be brief text messages. Author Dron, for example, travels a great deal and so typically indicates his location in his status message. Some tools integrate with others so that, for example, a status message indicates which piece of music a person is listening to. This rich information greatly increases a sense of social presence and connectedness that reinforces weak ties and sustains an awareness of another person’s activities, making it simpler to catch up and more effectively lubricate the social wheels so that interaction is easier when people in a network more sporadically engage in richer conversation. Often, such status updates form a topic of conversation for a broad network, allowing further connections to be built and individual networks to be extended.
Notification Tools
The sporadic and bursty (occurring in bursts) nature of network interactions means that it is vital for all members to be proactively informed when people on the network are trying to connect. Contributing to a learning network and not receiving feedback or acknowledgement of that contribution quickly discourages further participation. Good networking software provides both push and pull forms of notification. Using push tools such as RSS, instant messaging, or even email provides notification to the learner when new content or communication is entered into a learning space. Quality networking tools also allow historical and persistent display and searching of these interventions, so that the learning space can be searchable and span across significant lengths of time.
Referral Tools
Some of the most successful commercial social networking software, such as LinkedIn, MeetUp, and Facebook, is based upon providing selective referrals to other persons for social or commercial motivations and effective encounters. Most of these referral systems assume that those people you regard as friends are more likely to become useful and interesting friends to one another than a random selection of individuals. Thus, mining both weak and strong connections allows us to become acquainted with, and possibly work or learn together with others, with a greater probability of developing profitable exchanges. A variety of network tools make the discovery of others easier, most notably the ubiquitous “friend of a friend” functionality that recommends people you may know. This is an example of a collective application used for networking. However, referral is often more direct and manual: many social networking systems provide the means to suggest people that others may know, and some allow one to suggest groups or sets that may be of interest. Referral may relate to other people, or communities of interest. One of the great strengths of networks lies in the ability to exploit weak and indirect network ties, a matter of great importance when the knowledge a learner seeks cannot be found within his or her circle of friends and acquaintances. As, seemingly, everyone is potentially connected to everyone else by a very small chain of network nodes and edges (Watts, 2003), it appears that someone not too distant from you in network terms may turn out to be the world’s leading expert on what you wish to know.
Information Routing
One of the key roles of a teacher in a conventional classroom is to draw attention to information and resources that are of value to learners. The Internet is awash with information, some extremely relevant to us, but most of which is irrelevant and merely creates unwanted noise in our networked environment. By routing relevant information to colleagues in our various networks, we serve as filters for one another and become critical tools of networked information management.
Emotional Support
Networks were earlier conceived of as instrumental tools to afford the undertaking of tasks and support communities of practice. But as network tools have evolved and engaged larger and more diverse sets of users, their function as tools for the emotional support of others has grown. For example, most social networks can be set to alert you of the birthday of anyone in your list of friends. Unlike earlier tools to support this type of notification, Facebook provides a variety of tools the user can employ to express their wishes on a networked friend’s “special day.” They can, of course, compose a traditional email; send an electronic card; post to their Wall, making a semi-public contribution to the recipient’s personal web space that is visible to them and their “friends”; “poke” the person to indicate that they are being thought of; post the information to a group or network to which the recipient belongs; engage in an audio, video, or text chat, or even compose an audio or video greeting. Thus, networks allow members to acknowledge and support one another in a variety of ways—most of which are totally free of charge and very easily composed.
Value of Networks in Formal Education
As our brief overview of some of the main tools reveals, networks can be valuable to learners, especially in a lifelong learning context, but also within a more structured and guided context.
An oft-cited observation has it that citizens must be lifelong learners in order to maintain their currency, employment, and relevancy in the context of a rapidly changing knowledge-based society. Rather than immersion in full-time study for a few pre-professional years of postsecondary education, policy advisors and educators now argue that learners need to develop skills, attitudes, and connections that will afford their participation in many forms of learning throughout their lives. Most educational groups, especially those that are institutionally organized and led by professional teachers, end very abruptly at graduation. Networks, however, persist and can be used as the basis of lifelong professional education and learning, as long as the participants remain in the relationship. Further, networks made up of participants from the professional world and pre-professional students serve to connect the often theoretical study of the classroom with the everyday problems and challenges of real life. Networks provide opportunities for mentoring, recommendations, and posting queries and requests for help that are heard beyond the protected environs of group-based learning. The capacity to add value and gain recognition within a network also serves students when they complete their studies. They are not only established with membership in a set of existing networks, but more importantly they have experienced and practiced the skills needed to effectively use networks throughout their professional careers.
Global Collaborations
Networks support connected learning on both local and global scales. Recent concerns over global warning illustrate the growing awareness of the connectedness of all who inhabit our globe. Many global problems will not be resolved in the absence of international dialogue and coordinated efforts. Networks afford opportunities for learners to associate, negotiate, plan, and execute projects on a global scale with others. For example, the Centre for Innovation in Engineering and Science Education (www.ciese.org/collabprojs.html) coordinates a range of projects that allow learners around the globe to share data collection and analysis in areas such as water and air quality, real-time weather, genetic variations in human body size, and other challenging and intrinsically interesting studies of life science. A similar and hugely successful project, Earthducation, has connected networks of schoolchildren across the globe to a team of researchers, and actively engages them in what Doering (2006) describes as “adventure learning,” following him and his colleagues via the Internet on ecologically inspired expeditions around the world.
Workplace Networks
Although more commonly associated with informal and non-formal learning, networks offer flexibility, exposure, and the means to build social capital that warrant more serious consideration for their adoption in formal education. There are important lessons to be drawn from modern uses of networks in the workplace. These applications retain the purposive and task-oriented functionality needed for organizations to succeed, while representing a shift in thinking away from traditionally constituted hierarchical departments and centers. The most widely known research related to networks in workplace contexts is the work of Etienne Wenger on what he refers to as communities of practice (COP). COPs usually consist of co-workers located in a common workplace that develop and share their skills as needed, thereby creating solutions to common problems. In the process of completing these tasks, they develop mutually defining identities, shared jargon, and “shared discourse reflecting a certain perspective on the world” (Wenger, 1998, p. 125). Learning networks, however, are not defined as much by a shared location or description of work, but rather by an individual’s need for task performance, learning, advice, or interpersonal support. The type of support or aid required causes the learning network to constantly morph its structure, rate of interaction among members, and communication tone in response to these tasks. A range of tools and environments support explicit group-oriented learning within a networked context, allowing groups to branch off from networks for specific learning purposes. For example, CoolSchool, presented primarily as a Facebook application, brings learners and teachers together through Facebook, providing a system for running real-time classes and requesting or offering a lesson, along with a scheduling subsystem.
There are numerous learning activities that can be imported from familiar group contexts as well as from instructivist methods based on cognitivist/behaviorist models of teaching. In many cases, discussions, debates, critiques, and presentations benefit when the audience is expanded beyond a specific group. We see this commonly in the networks that spread out from MOOCs, with Facebook groups, Twitter hashtags, and other foci providing the means for networks to develop beyond the formal group and connect with others. These less homogenous contributions add authenticity and divergence of opinion that is often the basis for enhanced motivation and learning. Even when the primary source of learning is the closed group, networks can be used effectively to expand learning beyond it. This expansion easily includes students enrolled in the program who have already completed a course of studies, and these alumni add experience and diversity to networked deliberation. Expansion to professional groups is perhaps most valuable in professional faculties, but even general studies can benefit from the experience of professionals who are in practice, have retired, or have even chosen to resign from professional life. As noted earlier, the Web’s global connectivity and data collection capacity can be used to design new learning activities. Data collected, shared, and analyzed in global contexts creates an expanded context that is inherently more valuable, fascinating, and motivating then similar activities in only local ones.
Informal networked learning presents both a challenge and an opportunity for formal education institutions. As more open and freely available educational resources become available, the monopoly of formal institutions over learning content is weakened. Similarly, as learners are able to connect with one another without mediation by employees of a formal educational institution, they gain the capacity to collaborate, share, stimulate, and support individual cooperative and collaborative forms of informal learning. The interest by governments, professional bodies, and employers in measuring and tracking competencies as opposed to credentials fundamentally threatens this last remaining monopoly of formal educational institutions (see, for example, Richards, Hatala, & Donkers, 2006). Networked informal learning acts as profoundly disruptive technology to formal education institutions. Christensen described disruptive technologies as those that are “typically cheaper, simpler, smaller, and, frequently, more convenient to use” (1997, p. xv). Since most informal networked learning is completely free to the learner, it is obviously cheaper than institutionally provided learning opportunities. Informal learning is chunked, sequenced, and scheduled by the learners themselves, thus creating appropriate-sized opportunities to engage in learning. The fact that networks are centered on the learner, not on processes and methods of groups in institutions, means that they bypass the careful controls of the institution. Facebook, for example, is commonly used by networks of students to support their formal learning activities in study groups that, on occasion, turn into mechanisms for cheating: at least, this is how universities perceive it (and in some cases they are correct).
Course Hero, for example, a website that boasts it has solutions to over half a million textbook problems, has over 265,000 fans of its Facebook group (Young, 2010). The ability of networks to easily allow learners to share and collaborate is forcing institutions and teachers to radically rethink traditional attitudes toward assessment and accreditation. Given their pivotal role in educational systems, this in turn may mean a drastic restructuring of the purpose and methods of traditional education altogether, an issue we return to in our final chapter of this book.
We have already observed that networks can be scary places for teachers who are used to being in control. Effective network teaching involves some letting go, but also recognition of where a teacher can add value, whether as a subject expert, a reassuring guide, or a shaper of the study process. It is thus concerned with a balance between top-down and bottom-up control. In a group, rich communication and an identifiable hierarchy enables a teacher to engage in dialogue to enable learning even though it is likely that structure is, at best, tenuous. It is thus comfortably within Moore’s notions of transactional distance. In a network, the fact that the teacher is just one of a myriad of signals in the environment means the potential dialogue that helps to guide the less autonomous student is diluted or lost in a cacophony of voices that struggle to be heard. It is all too easy for a student used to the comfortable certainties and cosseting of traditional group oriented institutional instruction to feel out of his or her depth and forced to make too many decisions about what and who to pay attention to.
Some of these issues may be addressed through a more structured design of the networked environment. Many social networking systems, such as Elgg, make it possible to impose a structure and appearance on a site that supports a given network, allowing the owner of a community to control the experience of the learner to a greater extent than more freeform social networks. However, that controlled space is just one of many that the student may inhabit in his or her personal or networked learning space.
Given the varieties of networks that learners participate in, of crucial importance are tools to manage, filter, and control information so as to make learning in networks efficacious. Specific recommendations include:
• Using high-quality and, where possible, open tool sets for finding, joining, forming, and supporting new and existing networks and their archives;
• Developing and deploying tools to support individual control of network filters;
• Supporting network deployment in contexts that are as open as possible;
• Using tools to support identifying, evaluating, and annotating resources by individual and collaborative network members;
• Creating linked profiles and other sophisticated search tools so that network members can come to know one another and contributions to the network are recognizable and valued;
• Using means of identity management such as OpenID to enable persistence of identity between systems;
• Allowing members to morph, parcellate, and combine networks as needs evolve;
• Using tools or processes, such as the soft security of wikis, which promote trust both of network artifacts and the people within them.
Connectivist models of learning are deliberately free from fixed learning outcomes. Because every learner’s constructed network is different, and trajectories are based on currency and emergent needs, networked learning does not take easily to the formalization of learning outcomes that underpins traditional courses. This does not mean that such outcomes cannot be stated in advance; instead, they are decided at an individual level and are constantly subject to re-examination and modification as a learner progresses, especially over a longer trajectory. In an academic world that is defined by learning outcomes, comparable courses, and assessment based on such outcomes, this presents difficulties for those attempting to enable networked learning in a formal context. A two-pronged approach of learning contracts and portfolios can help to overcome such obstacles.
Learning Contracts
One simple and effective solution to the problem of variable outcomes is to employ a learning contract, in which the learner specifies in advance what outcomes are intended and plans a learning path in order to achieve measurable outcomes. If it is to have value, it is important that this contract is negotiated with an expert, direct or embodied in a toolset, who can ensure that at least the minimum competencies are covered. If a learner wished to, say, become a medical practitioner, then it would be important to ensure that the learning undertaken is sufficient to support such a role and thus limit risks to potential patients. The use of competence frameworks can be helpful here, especially when they are designed by a variety of experts in a field.
Portfolios
While learning contracts provide a suitable mechanism for accrediting networked learning in some cases, they have limitations. In the first place, much networked learning is likely to fall outside the parameters defined for the contract, and will thus go unaccredited. This is true of almost all learning, from the most formal instructivist model to the loosest problem-based methods, and it just means that there are inefficiencies in assessment: not all that is learned is assessed. A more troublesome difficulty is that a contract-based approach does not easily allow for direct comparison of individuals, nor does it easily fit with professional accreditation requirements. Competence frameworks and expert guidance can assist to some extent but, especially where learners are already competent in some aspects of a field, portfolios can play an important role in assembling evidence of competence for accreditation.
Groups Emerge as Networks Grow
The fundamental role of facilitation, ownership, and other issues associated with leadership differentiate groups from networks. Educators, like other actors in hierarchical organizations, are used to creating learning environments in which students, as consumers, play their assigned role. Thus, many educators first approach network development as a task in which the learning activities are precisely outlined and students are commonly assessed by the teacher on their network participation. Many researchers, however, note the requirement for emergence in network learning models. See, for example, the special issue of IRRODL on emergence (www.irrodl.org/index.php/irrodl/issue/view/49). This implies that the members of the network have both the tools and authority to recreate the network’s form and function in response to changes in the environment. Author Dron (2007b) emphasizes the need to design for this change, through use of evolutionary change theories (survival of the fittest activities, modes of sharing and creation of knowledge), and the percolation of networks into new instances, or tighter groups. Dron also notes the need for network designers to delegate much of the control over the network to users; however, they must also allow the network enough central control and capacity for applying appropriate constraint to curtail abuse by spammers or other malevolent users.
The desirability of facilitation, promotion, and activism involved in leadership is a very contentious issue among network theorists. Community of practice theorists have argued (Wenger, 1998) that one cannot intentionally or artificially create a community of practice—rather they are by definition self-organizing. But at the same time, Wenger and others talk about individuals who play key “community development” functions that provide leadership to emerging networks. They go on to discuss strategies by which community developers exit from leadership roles in the community of practice when it reaches unspecified levels of size, participation, and sufficiency in governance. An individual’s power in a network comes from influence, not design.
Our notion of learning networks has much in common with the work networks discussed by Nardi, Whittaker, and Schwarz (2002), referred to as intensional networks. They point out that, increasingly, networks and not groups are the defining features of much workplace activity, which we suggest relates to the work and study associated with formal learning as well. They argue that “intensional networks are the personal social networks workers draw from and collaborate with to get work done” (2002, p. 207). These networks are activated based on opportunities or requirements for production. This may be directly associated with a formal learning activity, but more often arise when an individual turns to their personal network in order to accomplish some learning activity alone. Like Nardi et al.’s intensional networks, learning networks consist of those directly enrolled with the learner in a formal course. They also consist of colleagues, family members, friends, former workmates, neighbours, and others who can be called upon to support the learning activity. Though learning networks may be stable and used by learners for a series of learning activities or courses, they can also be temporary and called into existence for one-off learning demands. Nardi et al. note that “intensional networks are not bundles of static properties. They dynamically pulsate as activity ebbs and flows, as different versions of the network come to life” (2002, p. 238).
Similar to Nardi et al.’s notion of intensional networks is the concept of ad hoc transient learning networks (Berlanga et al., 2008; Sloep et al., 2007), which are focused on lifelong learning that is intensely learner directed. Koper, Rusman, and Sloep (2005) define a learning network as “an ensemble of actors, institutions and learning resources which are mutually connected through and supported by information and communication technologies in such a way that the network self-organizes and thus gives rise to effective lifelong learning” (p. 18). An ad hoc transient learning network provides tools enabling learners to access, engage, and evaluate learning activities, often but not necessarily as individuals in ad hoc networks. They thus encourage developers to move beyond the class and course familiar in formal education to learning designs that allow and support learners to create their own learning activities, goals, and outcomes.
Unlike those of Nardi et al. (2002), Koper et al. (2005) are clear about the technological requirements for such coordination, and their team at the Open University of the Netherlands (OUNL) has developed a range of online tools that facilitate their formation. Somewhere between a traditional group and an informal network, ad hoc transient learning networks are loosely joined networks of people with shared interests who are brought together through the use of toolsets to assist their formation. The team at OUNL address design and implementation challenges to build systems that help networked learners find appropriate learning content and paths to knowledge acquisition, connect with learners embarked on similar or related learning activities, assess their own competencies, develop personalized learning goals, and assess and authenticate self-directed learning outcomes. The computer-based technologies that underpin ad hoc learning networks play some of the roles occupied by traditional teachers and the surrounding apparatus of formal learning: enrolling learners, managing contacts, enabling the cocreation and curation of content, and assisting in the management of the learning path, though unlike a traditional group-based approach, the focus (network-like) remains on the individual learner and his or her goals, rather than a shared group purpose. The use of such tools places the systems used by Koper, Sloep, and others in the holistic generation of distance learning, moving beyond the loose networks of connectivist learning to something more guided and structured, yet still benefiting from the emergent strengths of individuals in a crowd.
The Value of Diversity
Learning and knowledge rests in diversity of opinions. George Siemens, “Connectivism: A Learning Theory for the Digital Age”
For a learner in a network, there is typically greater value to be found in diverse networks than in those that are self-similar. If a network consists of many different people with various skills and interests, then there is a far greater chance that someone in the network will have the skills and interests needed to assist with a particular learning goal. Diversity encourages growth by making the likelihood far greater of different world views conflicting and being challenged. Such challenges require learners to examine their knowledge structures, reflect on their positions, and articulate their beliefs and opinions, thereby connecting and constructing a deeper and more meaningful knowledge system.
There are many different ways of measuring diversity in a system. S. E. Page (2011) identifies three main categories of diversity: variation, diversity across types, and diversity of community composition. Variation can occur between similar people of the same type: for instance, researchers in e-learning may have different notions of how best to evaluate a learning transaction. Diversity across types is concerned with a system containing multiple types of entities such as species, topics, or product lines, measurable in terms of entropy, network distance, or attributes. What defines a type is contextually situated: for example, gender may differentiate types for some kinds of network, such as those who breastfeed, but may be completely irrelevant in others, such as those who research e-learning. Diversity of community composition, measurable by population, is concerned with the ways that different combinations of the same things can lead to different entities, such as the many and varied combinations of carbon, hydrogen, oxygen, and nitrogen used to make proteins. Which form of diversity is of most value will depend to some extent on the context and learning task. As a rule, type diversity will offer the most opportunities to ensure that someone within the network will have relevant skills. For example, if we are learning about global warming, then it will be valuable to have philosophers, climate scientists, economists, and poets within the network. However, especially where the network is one that centers around an area of expertise, it may sometimes be more valuable to find variation: for example, a learner who is making use of a network of learning technology experts in order to learn more about such things may gain more from a range of relevant skills in that area than from the presence of particle physicists or poets. Conversely, the potential for border crossing, creative connections, and transformative learning may be better enabled by a more diverse crowd, including physicists and poets.
Too much diversity can be overwhelming: the benefits of diversity are applicable only if the range of options to choose from is manageable. One of the most notable benefits of many networks (especially those that are scale-free or sparsely connected) is that they are, from the perspective of any node, limited in scope. As well as acting as a natural brake on diversity, this feature also enables variation, speciation, and diversity to occur within a large network. If everyone can see what everyone else is doing, with maximal connectivity, then an evolutionary pattern sets in where only the fittest survive, however fitness may be measured. For example, imagine a nightmarishly distorted hypothetical network that works a little like Twitter, with the twist that everyone is following everyone else. In other words, every tweet from every one of its hundreds of millions of users would be sent to every other. Imagine then that, unlike the real Twitter, this network provides no means of filtering sets of posts by topic (hashtag), nor is there any concept of age or ageing of tweets, but this system retains the network-oriented feature of allowing retweets. The chances of a new tweet surviving an onslaught of existing retweets would be minimal. Almost all that anyone would ever see would be retweets, which would mean that almost the only posts retweeted would be ones that had already been retweeted. Unless further mechanisms to limit expansion were introduced or were extrinsic to the system (e.g. some news headlines might have a large enough effect to impinge), in a rampant example of the Matthew Effect (Merton, 1968) these would soon be reduced to a few that would entirely dominate the rest.
If everything is in direct competition for attention with everything else, without further temporal or spatial variegation, there will be only one or, at best, a very small number of winners within any given niche. This is true whether we are talking about memes, ideas, patterns of behavior, or cultural expressions. Luckily, such hyperconnectivity is unlikely to be found in the wild, though larger network applications that fail to take such issues into account can and do suffer from problems caused by excessive connectivity between network members, as anyone with more than a few hundred Facebook friends is probably already aware. Attention is a limiting resource for which many posts compete. S.E. Page (2011) notes that, in a system like this that involves replication, variation and competition for survival, there are four main ways that this chaos of undifferentiated connectivity is avoided: “geographic heterogeneity (allopatry), isolation of a small subpopulation (peripatry), divergent neighboring niches (perapatry) and diverse niches in a common environment (sympatry)” (p. 95). These factors remain significant in a virtual environment as much as in a physical space. Limitation of scope (allopatry and peripatry), whether artificially induced through group formation or emerging along geographical lines, is a diversifying benefit of small communities, which inherently parcellate a set of individuals and, in many cases, impose or imply a set of shared values which develop differently from others around them. Perapatry (divergent neighboring niches) is a prime mechanism that saves networks from overconnection thanks to the innate limits of connectivity between individuals and the effects of groups that concentrate connections, which means that most networks are far from uniform. This differentiation is aided and abetted by limits to the speed with which ideas, patterns, memes and knowledge spread between nodes of networks and the clusters within them. Network diversity can also benefit from diverse niches (sympatry), such as those introduced through set-oriented mechanisms like Twitter hashtags or through individuals splitting a network into sets of individuals (circles) that relate to their different interests.
Context in Networks
Closely allied to diversity is network context. While Facebook founder Mark Zuckerberg famously proclaimed privacy to be dead (O’Brien, 2010), it is nonetheless true that people present different identities in different contexts, and are not participants in a single network but many (Dron, Anderson, & Siemens, 2011; Rainie & Wellman, 2012). This is particularly important in a learning context where the networks that relate to our academic or personal learning projects may be quite different from those that relate to, say, our hobbies or friends, and where there may be many sub-contexts that interest us, like different classes, courses, subject areas, and so on. If we are receiving a stream of information and updates via a social networking site, it is very convenient to split the stream into different areas of interest. In many cases, we may choose different social networking spaces for different networks that we belong to—Facebook for friends, LinkedIn for business contacts, academia.edu for academic contacts, and so on. Each will provide a slightly different, if often overlapping context. Alternatively, an increasing number of sites that utilize social networks provide tools for splitting networks into more manageable chunks: Facebook Lists, Google+ Circles, LinkedIn’s variegated ways of specifying relationships, Twitter’s Lists, Elgg’s collections, and so on. These mechanisms go some way toward allowing manageable diversity, albeit at the cost of having to take time and effort to manage our circles, lists, or collections.
Ownership of Network Artifacts
Debate over ownership of digital content has proved to be very disruptive issue on the Net and provides fuel for the emergence of many different forms of digital products. Publishers and media producers have seen their profits attacked, and in some cases obliterated, by the tools and techniques developed by both consumers and producers of media who often distribute their products at no cost to the user. In education we see equivalent disruption and opportunity brought about by Open Educational Resources (OERs).
The school or corporate entity that sponsored its creation has most often retained ownership of specifically designed educational content. This institutional ownership model, however, has been challenged in university contexts, where professors often lay claim to ownership of course content as a traditional right of academic freedom. This contention often leads to questions of ownership and disputes that have proven very difficult to resolve. In the worst cases these disagreements lead to “patent thickets” in which the threat of ownership and enclosure by one or more of the creators of the content makes it impossible for anyone to legally benefit from it (von Hippel, 2005).
Identity in Networks
When one moves beyond the familiar camaraderie of the group to the open network, effective management of one’s identity becomes critically important. Before discussing the particular tools provided to both reveal and conceal the personal, it is useful to review the rationale and means by which users present themselves to the outer world.
Individuals are constantly walking on a balance beam where they attempt reveal enough of themselves to gain the benefits of social interaction, discourse, and commerce. At the same time, they try to protect themselves from the crowd, so that they have places and times when their actions and ideas are allowed to develop in privacy. The goal of all but the most reclusive hermits among us is not to maximize our privacy. Indeed, maximum privacy—as in solitary confinement—is used as a punishment in many criminal systems. Neither is the goal complete openness, where no actions, ideas, or words are held privately in the self or shared with only a small number of confidents.
The Internet has irreparably disrupted this balance, leading to instances of “identity theft” and both perceived and real invasions of privacy. For example, A. Smith and Lias note that “typically victims in the US may spend on average \$1,500 in out-of-pocket expenses and an average of 175 hours in order to resolve the many problems caused by such identity thieves” (2005, p. 17). Further, the popular press and individual parents are aghast at the amount of personal disclosure engaged in by both young and more mature Net users. Conversely, cyberspace has been instrumental in the development of countless new personal friendships, collaborations, and even marriages. We often ask for a show of hands when delivering keynote speeches, querying the audience for those who know someone who has married another that they met in cyberspace. Invariably, the question reveals that many of us find camaraderie, love, and lust using the affordances of cyberspace, and specifically various social software tools.
We come to know one another through the presentation of ourselves in Net spaces. In his seminal 1959 work, Erving Goffman defined a new field of sociology that he called dramaturgical sociology (1959). He masterfully tied together metaphors of the stage and its actors to describe how people manage their “presentations” or plays for the benefit of self and others. Goffman’s plays took place in real time and in face-to-face interaction. Nonetheless, the prompts, settings, front and back house etiquette, audience and actor interactions also are performed in cyberspace, and are often amplified.
Goffman describes two types of impressions we use during the course of our presentations of self. The first are those that are carefully crafted and presented or given to the audience. The second are those displays of self that are “given off” often inadvertently, through words, deeds, gestures, or expressions. These breakdowns or partings of the curtains arouse in the audience “an intense interest in these disruptions . . . that comes to play a significant role in the social life of the group” (Goffman, 1959, p. 14). In face-to-face interaction, given off displays include style of dress, accent, body language, choice of topic, and quality of discourse. They include the many ways we can stumble both physically and metaphorically, and how we respond to the unexpected. In cyberspace, these clues are somewhat constrained and often focus on written discourse. However, as Walther (1996) and others have pointed out, a host of compensatory tools and techniques are used, even in low-bandwidth Net contexts, to create forms of hyper-communication that compensate and in some ways create enriched contexts for developments of the self that exceed those available in face-to-face contexts. As cyberspace evolves to support immersive, video conference, and other rich forms of interaction, we see continued means by which participants add novel channels of communication to present themselves.
Thus, cyberspace affords its actors a powerful set of tools which they can use to present themselves. But what exactly are they presenting? Higgins (1987) notes three quite distinct psychological entities that actors present to others. The first is the “actual self,” the set of attributes that the individual actor possesses and displays, perceived by others. The second is the “ideal self,” those attributes that the actor wishes to possess, and which defines his or her hopes and aspirations. Finally there is the “ought self,” those attributes belonging to both the actor and those of importance to them that define what they perceive others expect of them. We shall see that cyberspace provides ample opportunity for presentation of each of these senses of self—the challenge for both actors and audience is to differentiate the context, time, and space in which each is presented.
A final attribute of the stage upon which we present ourselves is the role of others—both actors and audience. Goffman goes into some detail developing his stage metaphor to include interactions between audience and actors and the backstage discourse among teams of actors. Networks also support these interactions. As we have seen in group interaction, the discourse and collaborative activities team members engage in is critical to learning and the production of learning artifacts.
In networked interaction, exchanges between both active and potential network members are much more complicated. The complications arise most obviously in response to the size and fluid nature of network actors. But of even greater importance is the diminished certainty as to the nature of the audience. Network members share similar interests in the topic, ideas, or activities that motivate their membership and participation in the network. Yet they also have additional ideas, cultures, customs, and activities that are not shared, and some may be fraught with dissonance among other network members, especially when considering connections beyond those of the first-order—friends of friends and the like.
Membership in Networks
Unlike groups, for networks to operate effectively, participation needs to be as freely and widely accessible as possible. For this reason, the P2P Foundation uses the term “equipotency,” which implies that each member of the network has the potential and power to participate in the network (p2pfoundation.net/Peer_to_ Peer). Network participants have ample opportunity to witness the network’s dependence on participation from large and diverse populations. The culture that evolves within the network therefore emphasizes openness and invites contributions from as wide a population as possible. Further, networks encourage members to join and participate in other networks, thereby providing conduits to cross-pollinate and invigorate existing ones.
Participation on the physical level is open to all who have access to cyberspace—a capacity nearly universally available in developed countries, but sadly unequally distributed in some developing countries at the present time. However, with the development of very low-cost hardware, the increase in portable and handheld devices, and the deployment of machines with mesh networking, we can expect physical access constraints to decrease rapidly in the near future. Nevertheless, network value may also be restricted to those who are able to adapt to the fluid culture, languages, and linguistic clues that are used to sustain networking cultures. Those whose technical skills are very limited, who harbor a deep distrust of network technologies, or who are comfortable only in highly visible and defined hierarchical organizations may find networking contexts both frustrating and suspicious.
Equipotency also speaks to the power of network members to define the extent of their participation in a particular network. Since networks offer a wide variety of participative roles, members must decide for themselves what roles they wish to play and be able to amend them as desired. They are free to define the extent of their participation, and to adopt roles of leadership, support, encouragement, or silence as required. Equipotency assumes a deep respect for democratic ideals, in that network members are free to define their own expectations and practice, while respecting the rights of others to do so as well. This freedom is not anarchical, though. Participation in the network requires a shared commitment to a common interest, goal, or activity of the network. Network members come to understand through observation that the realization of their object of cooperation will happen when they coordinate and distribute their activities, skills, and talents in effective ways. Thus, organization, leadership, planning, and coordination evolve within the network and are viewed as legitimate means of achieving network goals.
Networks and Social Capital
Social capital has long been seen as an important facilitator and indicator of readiness for social activity. Through collaborative interaction, action, and discourse, groups and networks build social capital. Resnick notes that “use doesn’t use it up; when a group draws on its social capital to act collectively, it will often generate even more social capital” (2001, p. 2).The social capital thus created empowers both individuals and their network(s), affording them increased opportunity, capacity, and a sense of efficacy that are used for subsequent individual and social actions.
Burt (2009) focuses on the value accrued to the individual by the exploitation and growth of their social capital. He discusses the role of a broker, someone who spans two groups or networks and serves as an introductory facilitator for more extensive social, and often, economic transactions. While Burt’s work is especially relevant to business-oriented networks such as LinkedIn, it also points to the role of the teacher as one who brokers connections—not only to content, ideas, and facts, but to individual groups, networks, and collectives who can be called upon to expand and apply the ideas studied.
Designing Network Applications
There are many books, websites, and papers that purport to provide formulae and techniques for designing successful social networking sites. While we will be highlighting some of the more obvious common features of these, our intention here is to focus on those that are significant in a learning context. Successful networkbased learning is not just about building large numbers of connections (as in, for example, Facebook or LinkedIn), though numbers do matter (Rainie & Wellman, 2012). It is more about building systems that make it possible to gain the greatest value in a particular learning context. A small network of the right people is far more valuable than a large network of those who will not provide much help, although it is true that the chances of finding that small network are higher if we are more networked in general.
The looser aggregation of networks compared to groups leads to its own set of design problems. Networks do not, by definition, involve the same levels of commitment and purpose that define groups, do not have the same social hierarchies and structures that bring comfort and security in groups, and are less tightly controlled and defined. Indeed, most do not even have a name and, when they do, it is a label more than a definitional term.
Design to Encourage Participation
Unlike groups, there are seldom external structures and social clusterings that drive the membership in learning networks. While membership in a group may be the precursor for the formation of a computer system to support it, networks tend to arise through participation from the ground up. It is certainly possible to intentionally seed a network, but it is usually not so easy to define its membership in advance. It is therefore important for any software and surrounding systems designed to support networks to pay close attention to making participation (as well as ending participation) as easy and painless as possible.
Some aspects of encouraging people to join a network are mainly a marketing concern: if the intention is to seed its growth, the purpose of the network should be clearly stated, well-advertised in the right places and, more than anything, the right people should be encouraged to join, remembering both Reed’s law and Metcalfe’s law: individuals should be well-connected, well-known, or both. Some are a matter of design for applications to support the network:
• Make the process of joining clear: make the joining and login process simple and well-signposted;
• Make the process of joining simple: use of OpenID, Facebook Connect, simple forms, or progressive engagement. (Porter, 2008, p. 93)
Design to Encourage
People to Stay While much of the dynamics of a network application are determined by the interactions of people within it, there are many things that may be done to make it more likely that networks will persist and thrive. Techniques such as sending push reminders about new content via email, notifications when a user’s content has been “liked” or commented upon, tools such as recommendation or referral systems to sustain network growth, and above all, making compelling content easy to find can help here.
Design for Change
Evolution occurs as much in groups as in networks but, commonly, the evolution of groups is an intentional process that at least passes through, if it is not derived from, the higher hierarchical levels of control within. In the network, the meaning of the word “evolution” begins to shift far closer to the specialized Darwinian notion of the term.
Conclusion
In this chapter we have seen that, though some of the tools may be shared in common with groups, networks are a very different social form, one that is fuzzy, bursty, emergent, and unbounded. Central to this difference is that fact that networks impose different and fewer structures and methods on their members, which means that they play a far more significant role in determining their own course of learning. Perhaps ironically, this most centrally social of forms is focused almost entirely on the individual and that individual’s relations with others in the network. | textbooks/socialsci/Education_and_Professional_Development/Teaching_Crowds_-_Learning_and_Social_Media_(Dron_and_Anderson)/05%3A_Learning_in_Networks.txt |
LEARNING IN SETS
While the network has proved to be a useful structural principle and, in a minimal technical sense, underlies every social form enabled by the Internet and other networked technologies, and groups are well founded in practice and literature, they are not always the most useful way to look at the social structures that emerge in cyberspace. Sometimes we do not know people in any meaningful way, so “network” is too strong a word for our engagement, and sometimes we are not members of shared groups, yet people can make a big difference to our learning. In this chapter we will describe how the set, a simple aggregate of people and the artifacts they produce, can provide meaningful learning opportunities and how it differs from group and net social forms. Unlike previous chapters on nets and groups, there is not a copious bounty of literature to call upon that discusses sets because few, if any, researchers have explored their use in a learning context.
This is uncharted ground, and much of what we write here will be relatively new in academic literature, though the set has not gone unnoticed by the blogging community and popular press, nor by millions of users of social media. Eldon (2011), for example, observes that set-oriented social interest sites such as Twitter, Tumblr, and Pinterest have experienced massive growth. These are still often inaccurately referred to as “interest-based social networks” (Jamison, 2012): just as early network-oriented applications were called as “groupware,” there is a tendency to see systems through the lenses of what we are familiar with, and we are currently familiar with social networks. Though under-researched as a social form, especially for learning, sets are important. It is not accidental that relational database technologies used to store and retrieve information about people and things in the world are based on sets, because categories matter, both to people and machines. To a significant extent, the ways that we categorize the world shape our experience of it, and represent what we know of it (Hofstadter, 2001; Lakoff, 1987; Wittgenstein, 1965). We do not just know. We know things, which fit into categories, and this is important. As Lakoff (1987) puts it, “Without the ability to categorize, we could not function at all, either in the physical world or in our social and intellectual lives” (p. 6). Categories according to Lakoff, were classically seen as things ‘in the world’ that we could simply identify through their common properties. Wittgenstein (1965) both problematized the issue and slightly sidestepped it by suggesting that, for at least some of the categories that we use, this is simply not true. Instead there are family resemblances in which things we identify through a single category may share some but never all of the same properties, and the boundaries that we put around particular categories are not fixed but socially constructed. More recently, thinkers such as Lakoff and Hofstadter (2001) have shown the deep psychological, social and linguistic complexity of the ways that we categorize things, showing how metaphorical meanings are not just a feature of language but fundamental to understanding, without which we would be unable to build cognitive models of the world around us. Categories allow us to symbolically represent collections of things in ways that are meaningful to our social, intellectual, and practical needs, while letting us extend our understanding across fuzzy boundaries, making connections and drawing analogies from which we construct our knowledge. In many ways, knowing the right names of things is a crucial step towards understanding them. This has an important pragmatic consequence in the context of the current enquiry: when seeking to learn, especially in academic disciplines, we typically begin by thinking of topics, areas, or categories into which our new knowledge can be classed and named.
Defining the Set
Sets as a social form are made up of people with shared attributes. There are indefinitely many attributes that may be shared by individuals, which may be specific or relate to coming within a range of values: location, height, IQ, choice of automobile, and so on. Most of these attributes will be of little value to a learner, but some might. In learning, particularly useful set attributes might include a shared interest in a topic, a shared location, a qualification in a particular subject area, or a shared outlook. In order to be useful, it should be possible to identify a set and to interact or share with people in it. In this sense, there is a minimal requirement for a mechanism for sharing and communicating with others in a set. Like groups and nets, sets rely on a substratum in which they are situated and observable.
Sets are About Topics and Themes
The notion of the set bridges both people and things. For instance, one may find resources that are part of the set of writings about networked learning, as well as people with an interest in networked learning. The social form of the set simply refers to any collection of people, and in a learning setting this is often related to artifacts that they produce or seek. In typical cases, what causes us to identify the set is the topic, artifact, place, or site around which they aggregate.
A concrete example of this is a page on Wikipedia. While groups and networks can and do develop around Wikipedia pages, the central thing that draws people to both edit and read a Wikipedia article is an interest in the topic it addresses. Beyond that, there need be no social engagement, no direct communication, no exchange of information, not even a shared purpose. The boundaries of this particular social set are the page, and beyond that boundary is everything else. While networks and groups may develop in support of topics and pages, and various inducements are provided by the site to reveal one’s identity, such as greater editing rights, the ability to move articles or participate in elections, they are not a necessary feature of engagement with others on Wikipedia.
People may simply be identified by IP address which can be entirely anonymous (for instance, if an edit is made in an Internet café). This is not an uncommon occurrence. In one survey reported on Wikipedia itself of editors who made 500 or more edits (placing them among the most prolific), 5 out of 67 editors were identified only by IP address, not name (en.wikipedia.org/wiki/ User:Statistics#Case_1:_Anon_Surprise.21). In 2005, Voss found that, across different language sites, anonymous edits accounted for between 10% (Italy) and 44% (Japan) of all edits made. It is notable, however, that it is increasingly difficult to find such statistics in recent research papers. The strong academic focus on networking in most research publications on social software means that anonymous edits are often deliberately excluded from results of studies (e.g. Nemoto, Gloor, & Laubacher, 2011; Wöhner, Köhler, & Peters, 2011) which tells us more about the biases of researchers than the nature of Wikipedia.
Similarly, when we create hashtags for public posts in Twitter, they are a signal that defines a set for anyone with an interest in the topic defined by that hashtag. When we search for such a hashtag, we rarely have any particular interest in or knowledge of the people that created it: they are just a set of people who have tweeted about that topic. Of course, Twitter supports a profound net form as well with the mechanism of following but, through the hashtag, it is equally powerful as a means to support sets.
Individual Identities are Seldom Important
Identities of people that are revealed in sets may be hidden, anonymous or, even where they are revealed, of relatively little consequence to others in the set. Those who engage in sets are typically more concerned with the subject than the identities of the people that constitute them. One of the characteristics that tends to be indicative of a set mode of interaction in cyberspace is that names of participants, if available at all, are often abbreviated to usernames, without the associated translations into real names or profiles found in networked and group modes of engagement. This does not mean that everyone in a set is unknown: sets overlap with networks and groups. We may participate in a set with people we recognize and people we do not know, and we may come to know people by their consistent pseudonyms. However, most of the time, the identity of the individual, even when known, is not the most important factor when engaging on a set-oriented social system.
Sets are seldom bound by temporal constraints, nor do they demand the use of particular tools or technologies, though both can be important in certain contexts: without the means to discover things, it would be hard to put them into sets. In the broadest sense, sets are found within networks and groups. Indeed, groups and nets can always be viewed as sets, and subsets of sets. A group is a set of people who are members of the group, and a network is a set of people who are in some way connected with one another through direct or indirect links. Similarly, one may find sets of groups, sets of nets and, of course, sets of sets.
Sets are not Technologies
At their simplest, sets are simply assemblages of people with shared attributes. They have borders that are defined by the categories that make them, but while the process of categorization might be considered vaguely technological, this stretches the definition of “technology” further than we would like. There are therefore no innate technologies that are required to engage as a set. Having said that, there are many ways that technologies can play a role in establishing, forming, and facilitating a set, beyond simply providing a real or virtual place where people with shared attributes may congregate. Tools like search engines, tagging systems, databases, and classification tools sometimes play a key role in making set modes of engagement possible in the first place. Such tools often take the place or augment the capacity of a human to organize and classify people and things.
Why Distinguish Sets from Nets?
The reason for distinguishing the set is twofold. In the first place, the ways in which we interact are different when the attribute(s) forming the set matters, rather than the people with whom we engage or the mission of the group. In the second place, the operations we can perform on sets are quite different from those that we perform on networks and groups, a factor of great significance when we come to talk of collectives. Many collectives are the result of set-based aggregations and transformations.
The Benefits of Anonymity
In some cases, the lack of an easy way to identify an individual who is learning in a set may be beneficial, especially when dealing with sensitive topics that require him or her to reveal things that may be uncomfortable or embarrassing. This may be due to the nature of the topic under discussion. For example, many medical sites, counselling sites, and sites relating to socially difficult things that people do not always want to reveal to their networks or groups take on the set social form. This is even true where the site appears to use the same tools and processes as a network or group site, simply because of the extensive use of more anonymous identities. In other cases, the value of anonymity in the set lies in selective disclosure. Self-determination theory suggests that there are three pre-conditions for intrinsic motivation in a learning task: feeling in control, feeling competent, and feeling relatedness with others (Deci & Ryan, 1985). If people are concerned about their level of competence, then fear of negative reactions from peers and teachers may reduce their inclination to share, leading to a vicious circle of doubt that undermines confidence, contribution, and motivation. In a group setting, one of the roles of a teacher is to reduce that sense of doubt, to offer encouragement and positive reinforcement to build confidence.
In a network, that safety net is often lost, because things released into the network may be seen beyond their original context. The products of learning are usually safe to reveal, but the process may be less so. Where anonymity is allowed, fear of disclosure will be lower. However, this is a double-edged sword, and there is a fine balance between the gains and losses that will vary according to context. Anonymity also reduces the significance of social capital (Nemoto et al., 2011) and the benefits of knowing one’s peers, as well as feeling pride in a job well done that is recognized by a peer group, thereby reducing motivation on the axis of relatedness. If contributions are truly anonymous (as they are in, say, an anonymous Wikipedia page edit), rather than simply anonymized (as they are when pseudonyms are used on a question-and-answer site) then there are no opportunities to gain social capital by merging set-based interactions into net-based interactions.
Identity and the Set: Tribal Underpinnings
While in many cases, membership in a set may have no significant impact on an individual and there are many ways to be a member of a set without even being aware of it, there are also many forms of set membership that are central to a person’s identity. Race, gender, nationality, (dis)ability, sports team supported, fashion preference, profession, religion, and so on are crucial to a person’s sense of being in the world and, much like a group (and unlike in a net), those who self-identify with a set may identify people outside it as “other.” On some occasions, such identity is of little or no consequence: for instance, we may feel a distant kind of camaraderie that makes us wave or honk our horns when we see someone else driving the same kind of car or riding the same kind of bicycle. On other occasions, identification with a set means much more. The starting point for understanding this lies, obtusely, in the realm of groups and group dynamics.
E.O. Wilson (2012) suggested that group evolution has played a large role in our development as a species, and thus we depend on identifying with sets of others, or tribes we belong to. For Wilson, the dual driving forces that form us— individual survival and things done for the good of the group—determine our ethics and social being. The sociality of our species places emphasis on survival as a characteristic of the tribe, band, or larger group rather than the individual. In modern societies, this evolved aspect of our being has become more complex because we do not see ourselves as part of a single set but, typically, of many. Crosscutting cleavages, diverse sets that intersect across many axes (S. E. Page, 2011), mean that we may feel a sense of identity with more than one set of people—a football team, a nation, a set of people with particular abilities or disabilities, and so on. A heavy metal fan who sees another person wearing a t-shirt advertising their favourite band may treat them as a member of the same “tribe,” making assumptions about other shared attributes that relate to lifestyle, preferences, and behaviors, though those people may also be supporters of hockey teams, believers in a particular religion, or other sets that are also meaningful to their identity and create feelings of allegiance. Likewise, those wearing religious symbols such as crosses, turbans, veils, or beads may signify not just membership in a set of religious iconography-wearers but also a complete ethical, social, aesthetic, cultural, ontological, and epistemological outlook, as well as being parts of other sets. Of course, religious tribes do not simply relate to identity but often drift into group modes of social organization, with hierarchies, prescribed behaviours, and rules of membership—the borders are blurred and variable.
Tribes are equally prominent in academia (Becher & Trowler, 2001): people are self-categorized by and identify with others sharing subject areas, uses of methodologies, schools of thought, interests in particular topics, past membership of institutions, classes of qualification, and many more attributes. Some sets are viewed by their members as mutually exclusive despite cross-cutting cleavages such as shared membership of institutions or professional bodies. What to an outsider may seem like remarkably similar things can be the cause of tribal divisions, to the extent that different languages evolve around them. For example, those who make use of activity theory are typically looking at the same things and using the same words with very similar purposes to those who employ actor network theory, yet seldom do the two tribes meet, and if they do, there are many ways they misunderstand one another. The mutually exclusive sets we belong to, though intersecting with other sets that cross those borders can lead to conflict, creative or otherwise. If they were completely isolated from one another then it would be of no consequence, but the cross-cutting cleavages bring them into juxtaposition.
Tribal sets, which involve many different attributes and a sense of membership, are potentially powerful social forms for organizing, motivating, and coordinating activities of members. Membership in a tribe can help create social confidence: knowing that others in a set share common beliefs or attributes can help to reduce the fear of the unknown that may beset those engaging with an unknown community. Conversely, they carry many associated risks when compared to sets that relate to a single attribute. That strong sense of identification can lead to heightened emotions when those who disagree are involved, especially thanks to the naturally anonymous or impersonal modes of engagement that tend to be found in sets. For instance, challenges to religious or political beliefs, criticisms of bands, sports teams or even tastes for certain cellphones can lead to harmful and bitter flame wars. This is one occasion where a transition from set to more interactive nets or group modes is not always helpful.
Cooperative Learning: Freedom in Sets
The social form of the set resembles that of the net in many ways, but without the social constraints where actions of others can strongly affect learning. Sets offer the greatest freedom of choice of any of the forms (Figure 6.1), though it is important to note that this does not necessarily equate to greater control, because too many choices without guidance or the means to make critical decisions is not control at all (Garrison & Baynton, 1987).
Figure 6.1 Notional cooperative freedoms in sets.
Place
Like all cyberspace learning, there are usually few limits on where a set-based learner can learn. However, there may be some constraints that depend on the attribute chosen to form the set itself, notably where geographical proximity is a significant factor.
Content
There are few limits on content in a set, and most revolve around content: people who are interested in x, people who know about y, people in a place. However, a major issue affecting all set-based learning is that it is not always easy to find the appropriate classification scheme to define the set in the first place. There are an indefinite number of ways to categorize anything, and it is an active, learned, social behaviour to do so (Lakoff, 1987; S. E. Page, 2008). The learner is often faced with a “chicken or egg” problem of not knowing which classifications relate to what he or she needs to learn, because he or she does not know what classifications are applied within a given domain.
Pace
There are virtually no constraints over pace in set-based learning, save for those that are intrinsic to the nature of a particular set. For example, those with an interest in sunsets may have limited opportunities or interest in gathering at other times of the day, and the set of those who attend a particular event will not exist long before or after the event.
Method
There are virtually no constraints over choice of method in set-based learning. However, it is very much up to the learner to choose the learning methods that are appropriate, and without much control over delegation, the difficulties for learners lie in finding appropriate methods.
Relationship
Sets are typically highly diffuse and impersonal, even though there is total freedom to choose with whom one may interact. Sets are often conduits into the more personal and social forms of engagement of nets and groups, however.
Technology
The main technology constraints in set-based learning are those of compatibility: the set exists in a particular technological environment or a constrained range of environments. There are therefore constraints imposed by the chosen instantiation of any given set: in order for the set to form, it needs a technological substrate to take hold in, and unlike a network, there cannot be alternative channels to a set that are not provided by its aggregator. That said, it is possible for individuals to amalgamate sets from multiple sources, in effect creating a set of sets or, maybe more accurately, a network of sets. Some kinds of set instantiation demand certain types of technology in order for the set to be visible: those that can aggregate, such as tagging or folksonomy systems, or those that can be aware of location, for example. In certain cases, technologies may determine or at least partially determine the set: owners of iPhones, for example, or those who use a particular app.
Medium
Medium form is irrelevant to set-based learning, unless the medium itself defines it, as in a set of writings, videos, songs, or media-constrained attributes such as colour or loudness. A set can, in principle, consist of any number of different media with shared attributes such as subject or theme. Time Because sets are about attributes of people and things, there are few if any time constraints affecting engagement in a set.
Delegation
While it may be possible to find people who are interested in something—a piece of software, a place, an idea, and so on—it is not always easy to sort out the valuable from the peripheral, misleading, or useless. Without even the social capital available in networks to guide people in a set, all content and dialogue is potentially suspect, and lacking other mechanisms, either net- or collective-based, there is no one to whom control can reliably be delegated. Sets provide a lot of choices, but the information required to exercise those choices may be limited.
Disclosure
The relative anonymity of sets means that people making use of them are able to retain some measure of anonymity and, on the whole, can be extremely selective about what they disclose and to whom. Having said that, sets only have value insofar as people do disclose knowledge and information, so while personal disclosure is highly controllable, it is necessary for people to reveal information in order for them to function at all.
Transactional Distance and Control in Sets
In a set, everyone is equally distant from everyone else in terms of communication, unless it is formed around a teaching presence: for instance, a Khan Academy tutorial creates a very high transactional distance between the tutorial creator and the learner who is using it, though this can be reduced if the creator of the tutorial engages in activities designed to feign a type of interaction by, for example, asking questions of oneself as if they originated from a live student or engaging in asynchronous discussions around the video tutorial. In such a case, that particular interaction drifts firmly into the networked social form with known individuals, albeit held together by weak and transitory ties in dialogue with one another. Within the set itself—that is, the people who are the discussants in the tutorial— transactional control, in the sense of the learner’s ability to choose what to do next, is absolute: a set is defined by intentional engagement around a topic. While there may be some dependencies on whether or not a reaction is forthcoming when a problem or concern is posted to a set, sets are decided upon and identified by the learner, who is free to seek people with shared interests. There is neither the overt or implicit coercion of the group, nor the social coercion of the network.
Dialogue is, in most senses, freely possible and strongly encouraged, and therefore the communication aspect of transactional distance between learners in the set is very low, though it can vary considerably in intensity and volume and, like in the net, become a distributed aggregate value. For example, an online forum or bulletin board makes the process of exchanging messages very straightforward and largely unconstrained. However, the psychological gulf between one learner and another is typically very high, because those in the set may neither know nor care much about one another. While caring can be an important attribute in both group and net social forms, in a set the person as a distinct human individual seldom matters at a personal level. If they are visible at all, people often become ciphers, anonymous or near-anonymous agents with which to interact. Most importantly, the great number of choices available to set users does not always equate to control. Whether sufficient help is given with making choices or not depends on the nature of the others in the set, the topic, the degree of familiarity that the learner has with it, and many other factors. Transactional control may therefore not be as great as the number of choices suggest. Transactional distance in the set is a complex phenomenon that, as in the net, is difficult to pin down.
Learning in Sets
Sets and Focused Problem-Solving
Sets are most useful to learners who are fairly sure of what they wish to know or at least the broad area of interest. Much set-based learning occurs “just in time,” concerned with finding out something of value to the learner now, rather than a continuing path. For instance, we may visit Wikipedia, a Q&A site, or Twitter in order to discover an answer from the set of people who have posted on this topic to a question or perhaps establish a starting point for further investigation.
Sets and Focused Discovery
Another common use of sets is to maintain knowledge and currency in a topic or area of interest. For instance, we may subscribe to a feed on a site such as Reddit or Slashdot in order to get a sense of the buzz around a certain topic. The majority of people who use such sites are not actively engaged with the network, but visit or subscribe to them because of an interest in the areas that they discuss. Because such sites are socially enabled, we may contribute ideas, pose problems, seek clarification, and use the other contributors to construct our knowledge, thus helping us to become experts within a subject area, not just to find answers to particular questions or suit specific needs.
Sets and Serendipitous Discovery
Beyond that, just as we find overlapping networks, we also find overlapping sets. It is a rare set-based interaction that keeps within the precise limits of the topic of interest, because people have many and diverse interests, often revealed through exposure to cross-cutting cleavages. Thus, as we find with networks, sets sometimes provide opportunities for serendipitous discovery beyond the immediate area of interest. This is frequently enhanced through the use of collectives, especially by recommender systems that suggest other articles, posts, or discussions that may be of interest.
Another way that sets can aid serendipitous discovery is when we spot trends or patterns in behavior. For example, if one were sitting indoors and noticed that everyone outside was using an umbrella, he or she can learn from the set that it is raining. Similar things happen online: an aggregated RSS feed, for instance, might contain multiple versions of a trending story, which might therefore pique one’s interest. We may discover in a set-based conversation subtleties and areas of interest in a subject we were not formerly aware of. There are subtle blurs here, however, between sets, nets, and collectives. Such trends may be spread through social networks as memes, or be generated automatically by aggregators that combine set behaviors and that, consequently, drive the trend.
Sets and Multiple Perspectives
The vastness of cyberspace means it is rare to find only one site or page connected to a particular set. Topics are typically represented in different ways in various places and often present multiple perspectives, points of view, and ontologies, going far beyond the diversity found in nets (where we might see bias due to affiliation and similarity with others to whom we are connected). This has value in many ways. Every learner is different from every other, with different prior knowledge and experience and different preferences for learning, so the presence of multiple perspectives makes it more likely that one or more will fit with cognitive needs.
Perhaps more significantly, multiple perspectives require learners to make judgments, choose between alternative views, or reconcile them. This active process of sense-making is one of the cornerstones of connectivist approaches to learning: differences are embraced and nurtured because the result is a richer connection and more deeply embedded learning. Differences require us to establish our own points of view, and to better know why we hold them. Multiple perspectives also broaden our outlook, enabling us to see connections that a single point of view, such as one we gain from an intentional teacher, may obscure. For example, to one individual, the set of things connected with e-learning may be limited to what can be found on the World Wide Web, whereas to another it covers any computer-enabled learning activity, while for yet another it refers to pedagogies of cyberspace. By combining these perspectives, a learner may find a valuable intersection or broaden his or her outlook and discover other related issues and areas of interest. The flip side of this benefit is that, much as in a net, it is up to the learner to make sense of conflicting views that he or she discovers. This can be a powerful and creative learning opportunity or, if the area is new or complex, may increase confusion and reduce motivation.
Sets can Support Formal Learning
Sets are of value as part of an individual’s self-paced learning journey, even in a formal setting. For example, at Athabasca University, undergraduate students start work on courses at any time and follow their own schedules within a six-month contract period. They seldom know other students in their course, and though the course itself is highly structured and led by tutors and teachers, the social form for student-student interaction is far more akin to a set than that of a group. There are few social interactions, no process-driven group engagement, few social norms, and few (if any) rules of engagement with other course members. They are not a cohort. They are just a collection of people bound together by the attribute of working over the same period on the same course. While students are not directly working with others or at the same time as others, they often benefit from the presence of others either directly (through contributions to question and answer sites), or through artifacts that others have shared. Course discussion forums provide both a repository of prior questions and answers, and a place to pose and answer such questions, though in our experience we find that set-based learners rarely engage in extended discussions. We should observe that, though very close to sets, these are tribal groups: there are still norms, expectations, and regulations as well as membership exclusions that make them set-like groups rather than pure sets.
Breadth Versus Depth
Broad sets are useful when learning is exploratory and the questions themselves may be unknown. A set of students in an Athabasca University course or a subscriber to an RSS feed from a popular gadget review site will be open to a broad number of ideas and content that fall within a range determined by the shared attribute. At the other end of the spectrum, a person in search of an answer to a single question may turn to a social set-oriented site such as Wikipedia for answers that rely on the set’s specificity, or a site that is so broad there is likely to be someone who knows the answer to any question. For a specific problem, the perfect set would be the global set of everyone. However, it is important that the two sets—people with specific problems and people willing and able to give specific answers—intersect, and that they can find each other. Where a site or service is specific and narrow, this is achieved by being in the same virtual location. For a more general purpose site, it is common for experts to classify themselves into sets, and/or for the site itself to be divided by classifications, often hierarchically organized or with a folksonomic, tag-based approach for identifying subsets. Once again, search engines play an important role in filtering out specific subsets of interest.
Categories of Things
Sets are defined by shared characteristics. They are communities of homophily. Sometimes they are intentional, and sometimes they are latent in what is shared. For example, as I look out of my window now, I see a set of people who are currently sharing the same general space as me. Most are pedestrians walking by with whom I do not and will never share a connection beyond, at this moment, being in a shared space. However, if some event occurred (perhaps a whale poking its head out of the water) then that attribute of shared space may become significant because it would enable learning to occur. We would probably talk about what we were seeing and, in the process, learn. Someone might identify the whale, someone else might mention previous sightings, and another might say how unusual it is to see one in these waters. Others, seeing the set of people gathering and sharing the attribute of staring at the whale, might come and join us, perhaps contributing to the shared learning moment. For a transient few minutes, we would become a learning community, ad hoc and fleeting. When the whale leaves, the significance of the space recedes. Some may perhaps make connections and become networked as a result, but as a collection of people learning together, our shared context would no longer matter. In rare cases, the set may even coalesce into a group that continues to gather at other times and locations as whale watchers. Similar processes happen all the time across cyberspace.
We search for answers and solutions based on their attributes such as subject, keywords, and tags, or explore topics in Wikipedia, brushing against those with shared interests, knowledge, and learning, and then moving on. Indeed, a set-based way of learning has been the norm since the invention of writing. As soon as the volume of available material became impossible for one human to track, we relied on classification systems to discover books, papers, and reports, and latterly other forms of media. Writers, especially of non-fiction, have a set of attributes in mind when writing books: subject, expected level of ability, background, language and so on define the sets for whom something is written. The same is true for all media used for learning.
Categories and Taxonomies
Categories are ways of putting things into sets and are one of our primary means of sense-making. To a large extent, how we think is determined by how we categorize the world (Lakoff, 1987). Our categories evolve as we learn. Expertise can be seen as an increased ability to both ignore attributes that are insignificant and to subdivide things that, to non-experts, appear to occupy the same categories (S. E. Page, 2008). Some of the work of a teacher is involved with helping learners to identify and focus on categories that are significant in a subject or skill being taught, to see both big patterns and small distinctions. Traditionally, categorizations of learning content tended to be performed by trained or otherwise knowledgeable individuals who would classify books, papers, journals, and media for easy discovery and organization. The builders of taxonomies created ordered sets of things, sorting them into easily identified clusters and groupings.
For the most part, taxonomies have a tendency to be hierarchical. It is no accident that ontologies used in the Semantic Web, though capable of taking any network form, are typically hierarchical in nature as they refer to sets, subsets, and further subsets of objects that are relatively easy for both humans and computers to navigate and understand. However, the world is not always so easily categorized. Many sets intersect, and connections are often more in a network structure than a hierarchy. For this reason, faceted approaches to classification, browsing, and navigation have gained much ground in recent years. Faceted classification allows objects, people, or data to be classified in any number of “facets” from which different combinations of set attributes can be selected for various classification purposes. Ranganathan’s facets (2006) have found particular favour in the library community, offering a structured schema that takes full advantage of the intersection of multiple sets to find things we seek. Although it can cause difficulties when allocating objects in a physically ordered space such as library shelves, a faceted classification scheme lends itself well to computer-based organization. Perhaps more significantly from a learner perspective, facets provide ways of seeing the same things differently. By breaking out of a networked or hierarchical model of thinking, facets encourage a set-based view of the world where multiple orientations can be explored. If experts define such facets, then they offer a means of seeing the world from the perspective of different experts. However, when defined by a diverse crowd, facets may actually offer greater value.
S. E. Page (2008) argues, using fundamental logic and empirical data, that a random set of people will frequently provide better problem-solving in aggregate than a set of experts because of the greater diversity of perspectives, heuristics, interpretations, and predictive models they share. For Page, interpretations equate loosely to categorizations—they are ways of dividing up the world by lumping things together. Combined with predictive models, they provide a means of describing the world and, more significantly, taking effective actions. On the social web, interpretations are reified in the form of tags, metadata supplied by creators and users of content that help others to interpret and discover sets. In combination, the aggregate of such tagging is known as a folksonomy (Vander Wal, 2007).
Folksonomies
The growth of social media has concurrently seen the growth of a bottom-up method of faceted classification in the form of social tagging, whereby any resource (bookmarks, photos, videos, blogs, and so on) is tagged by one or more individuals. A machine to enable discovery of similarly tagged resources that others can find aggregates their classifications. These folksonomies define sets of things with shared attributes most commonly known as tags, and they can be used to guide a learning journey. Because of the diversity of interpretations of the world that such tags represent, they are a powerful way for learners to identify and explore both the vocabulary associated with a given subject area and the different ways that the area is conceptualized. Anticipating our discussion of the power of the collective in the next chapter, when combined in a weighted list such as a tag cloud where tags that are more frequently used are shown with greater weight through visual cues such as size, font, or colour, they can indicate not just the range of interpretations of the world that the crowd uses but also the relative importance of such interpretations in aggregate. Kevin Kelly has identified tags and the hyperlink as the two most important inventions of the last 50 years (2007, p. 75).
There are many set-oriented uses of tags in which learners help others to learn. Twitter hashtags help us to find discussions, snippets of knowledge, and hyperlinks to further resources from which we may learn. Flickr Commons (http://flickr.com/commons/) is an exercise in mass tagging, involving tens of thousands of people categorizing public domain photos for the benefit of themselves and others, allowing users to easily find relevant photos in huge collections. The cataloguing and discovery of images is a wickedly complex problem, because even the simplest of holiday snaps can be categorized in an indefinite number of ways (Enser, 2008). The social tagging in Flickr Commons is a great example of how a large, anonymous set of people can create value for others without any kind of social interaction. Some photos in the public domain collection have been tagged thousands of times, with tags identifying people, places, objects, themes, subjects, concepts, colours, and hundreds of other attributes that may be used to split objects into sets. Bookmark sharing sites such as Delicious, Furl, and Diigo are heavily dependent on tags that people provide to categorize websites of interest according to topic.
As well as enabling the set to help its members make sense of the world interpreted by others, the act of tagging itself is a metacognitive tool that encourages the tagger to think about the things that matter to him or her, helping the process of sense-making, embedding reflection in the process of creation, and thus enhancing learning (Argyris & Schön, 1974). This process may be aided by systems that suggest additional tags, previously applied by others, similar to tags first chosen, which helps to decrease a potential multiplicity of synonyms from becoming tags, but also limits variability with both positive and negative results. We will return to other downsides of tagging later in this chapter.
Tools for Sets
There are many tools available that offer and enhance set-like modes of learning. Typically, most set-oriented applications are not exclusively dedicated to the set, also providing tools to branch into networks and, in some cases, groups. We describe a few of the main examples of the genre below in order to provide a sense of the range of tools and systems that can be used in set-oriented learning.
Listservs, Usenet News, Open Forums, and Mailing Lists
For decades before the invention of the World Wide Web, people engaged in posting on bulletin boards, anonymous FTP servers, newsgroups, and other topic-oriented services with great enthusiasm. Though many of these developed into rich networked and group communities, with emergent or imposed hierarchies and complex economies driven by social capital, several others celebrated open engagement around subjects and themes without significant social ties. Such services are still very common today in the form of social interest sites—Pinterest, Wikia, and learn.ist being prime examples—sites dedicated to different kinds of software and hardware, and many more.
Socially-augmented Publications
It is rare to find any form of publication in the wild that does not allow some level of anonymous user interaction—newspapers, magazines, public blogs, and the like, all offer engagement at a public level, frequently anonymous or where the identity of the person making comments is irrelevant, concealed, or ambiguous. There is a fine dividing line between the anonymous set orientation of these and the networked mode of engagement, and many combine the two. Sometimes, networks are explicit in trackbacks, where one blog comment leads to a different blog site, or through engagement in a conversation by known individuals. Much of the time, the comments are from people that no one else in the dialogue knows, nor wishes to know.
Tags, Categories, and Tag Clouds
Folksonomic classification, where bottom-up processes are used to tag content, are archetypally set-oriented. When using tags to find content, our concern is not with the individuals who create them but with the topics that they refer to. Hashtags in Twitter, tags in Delicious, Flickr, and many other systems provide a set-oriented way of cooperative resource discovery. Sometimes, sites will use a combination of top-down categories and bottom-up folksonomies. For instance, Slashdot, Reddit, Digg, and StackOverload provide ranges of common topic areas around which posts occur.
Search Terms
When we enter a search term into a search engine, we are typically seeking a set of things that share the attributes of the keywords or phrases we enter. What we get back, if all has gone well, is a list of items where others have used those terms. Thus, the search engine mediates between creator and seeker, enabling a simple form of one-to-one dialogue between them. However, the intentions of the creator may be very far removed from the intentions of the seeker, even when he or she is skilled in the art of searching. Unfortunately, as we have already observed, expertise is in part a result of being able to use categories effectively and a learner will be unlikely to know which terms are most appropriate to his or her needs in a novel field of interest. The sets returned, in such cases, may be highly tangential and confusing. For example, if a learner enters a search for “evolution” with the intention of learning more about the theory, then the list of results are likely to include many ideologically driven creationist sites (often deliberately manipulated through search-engine optimization to appear on the list), sites using the word in the pre-Darwinian sense (like the evolution of a design or concept), a film by Charlie Kaufman, a number of beauty products, and plenty more results of little value. Like the tag, the search term is highly susceptible to various forms of ambiguity. Unlike most tagging systems, search terms may be refined. A search for “Darwin’s theory of evolution” will result in a more focused set of results, but again, the anonymity of the set will mean that the learner is in conversation with not only evolutionary theorists and historians but also creationists. Bearing in mind that our hypothetical learner knows little or nothing about evolution, this places him or her in great danger. Without a theoretical framework to understand the manifold weaknesses and failings of the creationist point of view, he or she may learn inaccurate ideas that will make understanding the correct theory more difficult. Complexity theorists might view the potential range of useful and less useful results as a rugged landscape: there are many possible solutions or “peaks” that may be fit for the purpose, but climbing one (even a low one), will make it significantly harder to move from there to a higher, more useful peak (Kauffman, 1995).
While most search engines follow the logic of the set in an abstract sense, many make use of the set of people more explicitly in algorithms that mine similarities between searchers. Some, such as Google’s use of PageRank, also use networks to help provide relevant results. We shall return to this powerful use of the set in our chapter on collectives.
Social Interest Sites and Content Curation
Sites such as Pinterest, Learni.st, Wikia, Scoop.it, etc., allow people to share collections of related content—in brief, sets. Curated content can be created by individuals, groups, and networks as well as sets of people, and can be directly authored and/or collected from elsewhere, but however it is created, it provides a set of resources that are clustered around a topic of interest. Many more general social sites provide tools for the aggregation of content around a topic or theme: YouTube Channels and Facebook Pages, for example, provide thematically organized content where the set is at least as important as the network or group that is associated with it. Though the genre has been common throughout the history of the social net, going back to (at least) Usenet News and bulletin boards, in recent years there has been a significant growth in social curation sites, not to mention sustained growth in older social bookmarking sites like Delicious, Diigo, and Furl, sharing options for personal curation tools like Evernote or Pocket (formerly ReadItLater), and ways of using more general-purpose tools like Facebook Pages or Google Sites to assemble and share information on a topic. Curated sites or areas of sites are concerned with niches—areas of interest that are often very narrow—for instance, food (e.g., Foodspotting.com) or fitness (e.g., Fitocracy. com). While most niche sites can be used by groups and often involve nets, publically available niche sites based around topics are deeply set-based in nature.
The vast majority of niche sites make extensive use of folksonomies for organization, often combined with a more top-down and hierarchical categorization system. From a learning perspective, curated sites combine many of the advantages of a traditional, teacher-created content-based behaviourist-cognitivist learning resource with the added value of sets, and optionally, nets and groups. Social curation sites, as the name implies, embed the ability to tag, rate, discuss, and comment. Not only that, most curated content can be re-curated, mashed up, and aggregated, extending the value by recontexualizing it for different communities and needs. Thus, different kinds of conversation can develop around the same content, new connections can be made between different topic areas, and the value of diverse perspectives and interpretations can be heavily exploited.
Shared Media
Many rich media sites share tutorials and exemplars, some user-generated, some more top-down but with associated discussion or comment options. YouTube, TeacherTube, The Khan Academy, Flickr, Instructables, and many other sites offer rich learning content around which set-oriented discussions and learning can evolve. Media act as anchors for learning a particular topic. Wikis are flagship setbased tools. Wikipedia, Mediawiki Commons, Wiki Educator, and a host of other reference and sharing sites are based around categorized content. While many wikis do support sets and networks, the primary engagement in a wiki is nearly always focused around content rather than social interaction.
Arguably the poster child for set-based learning, Wikipedia is without a doubt the most consulted encyclopedia ever written, and one of the top two tools for learning on the Internet today, the other being Google Search. If ever anyone expresses doubt that online learning has a future, we have only to ask him or her to what they turn to first when seeking to learn something new. In many cases, the answer is “Wikipedia” or “Google Search.” Wikipedia organization is complex and highly social, yet it has few identifiable groups and very little in the way of networks. The vast majority of interaction is indirect, mediated through edits to pages by a largely anonymous or unknown crowd; most editing or visiting a page because they are interested in the topic it describes. In other words, they are part of a set with the shared attribute of interest in a topic.
With a similarly vast number of users, YouTube is another set-based system that is extremely popular for a wide range of uses, many educational in nature. Social networking in YouTube is not its main feature, and much of the interaction that occurs is centred on specific videos or clusters of videos (collections) rather than people known to one another. While the number of educational videos on YouTube greatly outnumbers those found on any other site, including Facebook, other similar sites like TeacherTube and SchoolTube provide services that are focused specifically on education. The benefit of such sites is their greater focus on formal learning, making it easier for learners to identify reliable and useful resources without the distractions of Lolcats and music videos. They are niche sites that contain further sub-niches or subsets categorized in ways designed to link learners with content and consequent interaction. Thus, the choice of the site itself acts as a means of classifying and organizing learning resources along set lines.
Locative Systems
Places are attributes shared by people who are in the same location. A wide range of social applications have been designed to take advantage of geographical co-location, from restaurant finders (e.g., Yell, Around-me, Google Latitude), to game playing as a means of discovering one’s locale (Geotagging, FourSquare) to cooperative shopping and dining (Groupon). Many mobile apps make use of location information to both discover and post information relating to the locale: FourSquare, Google Latitude, Geotagging, and many more tools allow persistent interactions to occur around a place. Locations thus become augmented by the activities of people who inhabit them, with the location serving as the defining attribute of the set of people who visit geographical spaces.
Augmented Reality
2D bar codes such as Semacode, QR codes, and similar technologies enable physical objects to be tagged. These bar codes are used for advertising, allowing people to snap photos of codes using cellphones or similar devices and receive either small snippets of information, or more commonly, hyperlinks to websites providing further information. While these have some potentially valuable educational applications, they are not usually socially enabled. However, a particularly promising approach to learning as a set in a location is to provide virtual information via cellphone, tablet, or more sophisticated devices such as Google Glass, and to allow people to leave virtual cairns or tags that others may discover in the space if equipped with a suitable device.
Crowdsourcing
A particularly powerful use of sets in learning is found in question-and-answer sites and other approaches to crowdsourcing work, problem-solving, and creative construction. From simple Q&A sites such as Quora to more complex brokerages for skills and services, the crowdsourced solution to learning problems is popular and thriving. Again, many of these sites shift between network and set modes, sometimes intentionally, sometimes seamlessly. For example, Amazon’s Mechanical Turk or Innocentive both provide a mediating role between those with problems and those able to provide solutions, typically using set-based characteristics to match the two, and facilitate the exchange of money between the parties. Other systems, such as Yahoo Answers and Quora, are less obviously incentive-driven: while social capital often plays a role, in which case interactions drift toward network-based models, many people contribute answers because they can. Altruism is a deep-seated human characteristic that has evolved in our species: one need look no further than the fact that people frequently risk their own lives to save those of strangers to see this fundamental urge in action (E. O. Wilson, 2012).
One of the most obvious ways to exploit the wisdom of crowds is to ask a question. Assuming the question is meaningful and has a correct answer, there is likely to be someone somewhere in cyberspace who knows it. Two giants of networking have tackled this opportunity in quite different ways.
Yahoo Answers is one of the older user-generated answer sites. Modelled after the wildly successful Korean site Naver Knowledge iN (www.naver.com), Yahoo Answers allows users to post and answer questions with no fees or concrete rewards. Questions and their responses are categorized and lightly filtered to remove obnoxious or nonsensical material. Users provide answers, and the questioner decides or allows the crowd to select the best one. Obviously, the site provides some value to users who can search or browse the archives for answers to relevant questions. Like all social sites, Answers gains value in proportion to the number of users. To support and encourage participation, Yahoo offers “points” for contribution. Five months after its launch in December 2005, Yahoo Answers was publishing nearly a half million questions per month, which generated nearly 4 million answers, an average of 8.25 answers per question (Gyongyi, Pedersen, Koutrika, & Garcia-Molina, 2008).
As in many publicly available sites, Yahoo Answers contains a great deal of “noise,” or questions and responses that can charitably be classified as silly or inane. Interestingly, many of the questions seem to be posted to stimulate discussion as much as to obtain a definitive answer. A question posed by the user Gothic Girl illustrates both noise and a discussion stimulator: “What is your favorite food??? (it can be candy too, i say that’s food)” received 41 answers! Alternatively, a question by Katie R. in the Math section, “If I calculate the variance of a collection of data to be .235214, does this tell me that there is large variance (that the data is spread out) or that there is relatively little variance?” received a comprehensive answer with examples from a top contributor whose profile explains “by education and profession, I am a statistician.”
Rival answer sites such as Answerbag.com and Quora, a more network-oriented Q&A site, are developing rules and practices that attempt to better organize questions and answers and support the development of communities among their members. For example, they allow members to develop searchable profiles and engage in discussion via comments to either questions or comments. Google took a more traditional approach for Google Answers, a more commercially oriented service, allowing users to post bounties between \$2 and \$200 for solutions. Rafaeli, Raban, and Ravid (2007) analyzed all questions and answers submitted between 2002 and 2004, and found that over half of the 78,000 questions asked were successfully answered with an average payout of \$20.10. After four years of operation, Google discontinued accepting questions and answers, and described the project as an interesting experiment. Its failure in the face of Yahoo’s continuing success has raised an interesting debate in the blogosphere. It seems that many want to ask questions, a few want to answer, but few want to pay and even fewer want to handle the logistics of accounting, curtailing spam, and all the other issues that challenge Web ventures. This also speaks to the dangers of extrinsic motivation reducing the motivation to answer (Kohn, 1999). It is a very notable feature of most surviving Q&A sites that the rewards are intrinsic, and often provided for completely altruistic reasons, with no hope of even social capital being accrued. In recent years, StackOverload sites have become extremely popular because they offer not only set-based interaction but also a collective-based method of identifying useful answers, organized by those perceived as being the most accurate or beneficial.
The use of answer sites creates an additional option for teachers and learners that provides a more current social resource than more traditional web or print sources. This query of the crowd is however less definitive and reliable than more traditional reference resources including those such as Wikipedia, which garner much more critical and comprehensive review by peers for accuracy, connectiveness, relevance, and authority. Some learners use answer services merely as a means to lighten their workload, and as a consequence, likely diminish their learning by posting homework questions in search of “easy answers.” Not surprisingly, this abuse of the crowd has given rise to the DYOH (Do Your Own Homework) movement.
Nonetheless, question and answer sites may prove useful for topical questions where discussion of especially socially constructed issues among answerers may be a forum to generate knowledge not available in more traditional resources. A review of the popular sites also reveals examples of explicit content that would be offensive and inappropriate for many learners.
TeachthePeople.com is another startup site that provides “experts” with server space to which they can upload teaching and learning materials in many formats, into “learning communities.” The site shares ad revenues with “teachers” that are dependent upon the number of learners who access the site.
Crowdfunding
Increasingly, learners are funding their learning with the aid of the crowd. Crowdfunding sites for students such as Upstart (www.upstart.com) or Scolaris (www.scolaris.ca) match sets of people interested in funding learners with donors. While many still rely on group forms for this role (governments, families, companies, and so on), the set has proven to be surprisingly effective for connecting those in need with those who wish to give. Because such applications tend to be one-off requests, networks have little or nothing to add, save in helping to verify identity and, occasionally, allowing prospective funders to find out more about students seeking funds.
Risks of Set-based Learning
Reliability
The relative anonymity of sets makes it significantly harder to gain a strong sense of the reliability of content produced by the crowd than it does in groups and networks. The Internet is notoriously filled with distortions, lies, and falsehoods of many kinds, but even when data is accurate and meaningful, it does not mean that it will be of great value to a particular learner at a particular point in his or her learning trajectory. The problem is made worse by the fact that, sometimes, people deliberately mislead or distort the truth.
In the absence of cues such as the presence of advertising, an excess of exclamation marks, or a lack of references, there are three distinct ways that reliability of knowledge gained through sets can be ascertained inherent in the social form. The first is correlation: if more than one similar answer to a problem can be found in a set, then it increases the probability that the answer is reliable. The nature of sets, however, makes this a risky approach, because people in sets influence one another and it is very common for falsehoods to be propagated through and across them, each wrong solution reinforcing those that come before. The second is disagreement: where multiple perspectives and solutions are presented, this typically leads to argument, and by analyzing the strengths and weaknesses of the arguments, the learner can come to a more informed opinion about the correct solution. Disagreement is usually a good thing for learners in sets, because it encourages reflection on the issues and concepts involved, enabling learners to form a more cohesive view of a topic. Third, beyond the inherent capabilities of the social form, other social forms can play an important role in establishing veracity: we may, for instance, trust opinions voiced in our networks, turn to a group for discussion, or as we shall see, make use of the collective to establish reputation or reliability of information provided in the set.
Anonymity
On the whole, the relative anonymity of the set has notable benefits to the learner. There can be greater openness and keenness to participate, especially when topics involve sensitive personal disclosure. Where the crowd is contributing to, editing, and evolving a resource started by others (e.g., a Wikipedia article) the anonymity makes it far easier to make edits because editors are unlikely to feel as beholden to earlier authors as they would in a group or network. When using wikis in a group, we have found that the strong ties, roles, social capital, and the politeness that this leads to can significantly deter members from editing what others have labored to produce. This may be a particularly strong tendency in the authors’ two native countries, Canada and the UK, both known for cultures of politeness, but it seems likely that the more learners know one another, the less inclined they will be to modify one another’s work in the peculiarly mediated world of the wiki, at least without extensive use of associated discussion pages or other dialogue options. However, the flip side of relative anonymity is that it makes it more likely for people to be treated impersonally, as ciphers, with feelings that can be ignored or, as we see in the case of Internet trolls, manipulated for fun. From the early days of Usenet News and bulletin boards, we have seen large anonymous communities brought down by flame wars and trolling.
Another drawback of anonymity is that the motivation to participate is significantly lower than in groups or networks. If individuals are not recognized and identifiable, there is sometimes less social capital to be gained, and there is no sense of being beholden to other individuals, either because they are known directly to us or because of the written or unwritten rules of a group. Size can play an important role in overcoming this limitation. Where many people are engaged, such as might be found on a large social site like Twitter or Wikipedia, there are more likely to be others willing to share and participate at any given time. The Long Tail (C. Anderson, 2004) means that someone, somewhere, is likely to share the same concerns, no matter how minor the interest.
The Trouble with Tags
Tags are a useful way to harness the collective wisdom of the crowd, and we will return to more advanced ways that they can be used in the next chapter on collectives. However, folksonomies suffer from a range of related issues and concerns.
Context and Ambiguity
Especially when learning, the meaning of tags may be closely connected with the context of use. The same word in a different context can mean something different, even though the dictionary definition is the same. For example, if an expert tags something as “simple,” it means something quite different than if the same term was used by a beginner. Equally, “black” might designate a color, a race, or a kind of humor, among many other things. “#YEG” is a hashtag commonly used by residents in Edmonton to refer in Twitter posts to the city, yet it also is the designation for the Edmonton International Airport. The word “chemistry” used about an image might refer to the subject of chemistry, or equally to the bond between two lovers in a different context. In some cases, the same word may have multiple distinct meanings in a dictionary. Context is also important when dealing with lexical and syntactic ambiguities where longer descriptions are applied. For example, “Outside of a dog, a book is a man’s best friend; inside, it’s too hard to read” (attributed to Groucho Marx (van Gelderen, 2010, p. 42)) or “they passed the port at midnight."
Bruza and Song (2000) describe a diverse set of categories that might become tags: S-about (subjective-about, broadly scalar qualities), O-about (objective-about, broad binary classifications), and R-about (contextualized to a group of users). R-about is particularly interesting, as it suggests that different communities may use the same terms differently. This is confirmed by Michlmayr, Graf, Siberski, and Nejdl (2005), who looked at the properties of tags describing bookmarked sites on the Web obtained from Delicious. They postulated that those who bookmarked similar sites and described them with similar tags would share other tags, interests, and perhaps, already belong to, or be interested in developing, existing networks or groups. They found, however, that users who tagged similar sites did not have large intersections of other resources that they tagged. An average of 84% of sites bookmarked by users who share a common site were not bookmarked by other users sharing a common bookmark. Furthermore, they found surprisingly little correlation between folksonomic tags and those developed as a component of the more formal tagging systems developed by the Open Directory Project (www. dmoz.org). This suggests that folksonomic classification may serve personal and perhaps group needs, but beyond showing popularity and tag cloud images, the extent to which inferences can be drawn based on folksonomic tags or the taggers is limited without further examination of context.
Homonymy
Sometimes, especially in English, the same word means more than one thing. These are subcategorized as homographs, heteronyms, and homophones. Homographs are spelled the same but with different meanings: for instance, bat (an animal) and bat (a stick for hitting balls). When the pronunciation is different, they are usually referred to as heteronyms: for instance, “bow” (a ribbon tied in your hair) and “bow” (to lower your head). Equally, homonyms may be homophones (sounding the same but spelled differently), for instance “through” and “threw.”
Synonymy
Even where terms are distinct, more than one term may be used to tag the same thing. Some are obvious: for instance, “people,” “persons,” and “person” refer to very similar resources. Stemming dictionaries and tools like WordNet can deal effectively with such simple cases. In other cases, the words have quite distinct and precise meanings that are not synonymous, but will typically be used to describe the same object: for example, e-learning, online learning, and networked learning, at least for some, refer to the same set of objects. This can be a particular problem when using metonyms—for instance, “Hollywood” to refer to the US film industry and the place where it is most concentrated—where the term is not only a synonym but also is ambiguous.
Binary versus Scalar Tags
Nearly all tag-based systems treat tags as simple binary classifications which, in some instances, are what is needed. However, many tags are fuzzy and constitute fuzzy sets (Kosko, 1994): something may be fun or less fun, red or more red, cute or less cute (Dron, 2008). Golder and Huberman (2006) list seven distinct varieties of tag: identifying what (or who) a resource refers to, identifying what it is, identifying who owns it, refining categories, identifying qualities or characteristics, self-reference, and task organizing. Very few systems, notably those created by author Dron, make use of fuzzy tags that allow degrees of membership in a set (Dron, 2008; Dron, Mitchell, Boyne, & Siviter, 2000). We hope to see more such systems appearing in future, but they are beset by the inevitable complications of entering and using fuzzy tags. Binary tags take little effort to create, and are typically a comma-separated list of words. Fuzzy tags require not only the tag but also its perceived value to be entered, and raise further issues as to how they are presented and aggregated—for instance, should the values be simply averaged, or should there be some form of weighting based on number of uses too? Such problems also beset simple rating systems on, things like review sites, and the solutions are similarly imperfect: showing numbers of ratings separately, for example.
Lack of Correlation
These and other related concerns matter considerably when learning in sets, because a learner may find it harder than an expert to distinguish context and ambiguity, not be aware of relevant synonyms, or fail to observe closely related but distinct homonyms. While it can be argued that the process of discovering such uncertainties is an effective way to become adept in a given subject area, this may equally reduce motivation and increase the time needed to learn something new.
Sets in the Online Classroom
Within a formal, group-based educational setting where cohorts of students work in lock-step with one another on shared activities, set-based tools and communities can provide great augmentative value.
While traditionalists throw up their hands in horror at the problems that emerge from students using Wikipedia in traditional courses, citing concerns about reliability, superficiality, and plagiarism, the online encyclopedia has a place in almost any learning transaction. It is a wonderful way to enter into a topic, providing not only a fairly reliable overview (especially in academic topics) but also links, references, and further reading that can greatly assist the exploration of a subject area.
Moreover, many teachers have reported success in encouraging students to make active contributions to the site: they create pages, correct errors, and engage in the often rich discussions that emerge around a particular page. However, volunteer Wikipedia experts have also complained about the mess of forked (or unrelated) articles, and poorly written or incomplete edits that some students have left. In true wiki spirit, there is an editable page on Wikipedia (en.wikipedia.org/wiki/Wikipedia:Assignments_for_student_editors) discussing how to make the most effective use of a Wikipedia article as a writing assignment for students.
Similarly, tutorials available through sites such as the Khan Academy, eHow, WikiHow, HowStuffWorks, provide not only useful supplements to classroom learning but also a chance to engage with others, to see how they conceptualize and mis-conceptualize subjects and topics, and gain a sense of their own knowledge in relation to others. Within a formal setting, the widespread availability of varying quality resources that can take the place of some of the traditional roles of a teacher makes it possible to “flip” the classroom (Strayer, 2007), a term that describes what many teachers have always done: leave content for self-guided homework and concentrate on richer learning activities in the classroom. Content discovery and activities that in more traditional settings form the material of the learning process, whether online or not, can be offloaded to the set, allowing the teacher to concentrate on social knowledge construction processes that are more appropriate to a grouped mode of learning.
Teaching Set Use
We have noted that one of the major problems with set modes of interaction, as well as one of the greatest opportunities, is anonymity. This means that it is vital for users of sets to develop well-honed skills in identifying quality, relevance, and reliability of both people and resources. Teachers in conventional courses can play an important role here, modeling good practice, providing feedback, recommending strategies, and offering opportunities for safe practice.
Self-referentially, the set itself can provide resources and clues about the reliability of information found within it, particularly if it incorporates collective tools that emphasize reputation, provide ratings, or show other visualizations that give hints about the value of a contribution or individual. Even where that is not the case, it is often possible to follow conversations and identify which participants hold the upper hand in controversies or disagreements.
One important role for the teacher wishing to make use of sets is to define or identify relevant vocabularies and narrow down the attributes by which sets are classified. This may simply be a question of sharing vocabularies, identifying relevant search terms, and providing exercises that use the appropriate wording. However, the diversity of views and vocabularies that may be discovered also open up many opportunities to explore the ontological assumptions of a subject area, and much can be gained from comparing and contrasting different ways of seeing the world as a result.
The choice of appropriate sets is an important one, and relates to the purpose and context of the learner. A diverse crowd may be useful in solving some problems and less effective in others. Generally, when learning, a set of experts is better than a random set, or one made up of beginners, or things they come up with will be entirely random. But too narrow a focus may mean they will not meet the needs of the learner. Sometimes, proximal development is an issue. A set of subject experts is probably not useful to help learn the basics of a subject because the vocabulary and assumed knowledge of the set may not just render the subject incomprehensible but actually demotivate the learner. For beginners, it is better to find a set of expert teachers, explainers, demonstrators, and co-learners, each of whom has a certain amount of knowledge. The set will represent a range of perspectives and views of the subject, which together will offer diverse opportunities to connect existing knowledge to new discoveries.
Designing and Selecting Set-oriented Applications
There are two main issues that a set-oriented system needs to deal with: publication (or sharing), and discovery (or finding). On the one hand, there needs to be sufficient data organized effectively so that sets can be discovered and formed in the first place. On the other, it should be possible to use tools to find, organize, and make use of them.
Unless a networked application or site is highly focused on a finely differentiated subset, it is almost a defining characteristic for a set-oriented application to have the means of classifying content. The most popular approaches to this are to offer top-down categories or topics, bottom-up tags, or both; some go further in providing RDF-based ontologies or faceted classification schema. Search tools are also vital, in some cases circumventing the need for explicit categorization, though use of metatags, keywords in titles, and other cues still play a strong role in helping the search system to find what you are looking for. A richer search system is often valuable: at its most extreme, this might take the form of a visual query tool that generates SQL or similar commands to extract data from a relational database.
Curation tools are of particular value in set-oriented applications. Users should be provided with the means to collect and assemble content, and to create it. This may be as simple as a wiki—the popular Wikia site, for example, which is making great efforts to be a social networking site and build group-like communities, is a predominantly set-oriented application almost entirely wiki-based. It allows people to create tagged wikis and provide anonymous edits, much like Wikipedia. Other tools, such as learni.st and Pinterest, provide tools for aggregation that allow people to assemble content around particular topics, with a focus on presentation and classification. RSS feeds and other push technologies that provide channels, such as listservs or mobile apps making use of social site APIs, can be very valuable in certain kinds of set-oriented, curated content application, allowing a learner to identify a particular set or subset, which can feed him or her with a stream of information. This is especially relevant to broad sets that provide rich content around a subject area. Such aggregation may be less important on question and answer sites or similarly narrow-focus social systems, where engagement is unlikely to persist beyond dialogue relating to the presenting problem. Curation tools gain value if they are able to use common standards such as HTTP and RSS to retrieve content and metadata. Where access to otherwise restricted content is needed, such as from a closed network system, it is also valuable to provide the means to access them through their APIs. For our own Elgg-based site, Athabasca Landing, we created tools to use and provide authenticated RSS feeds, tools for importing feeds into different site media (such as wikis, blogs, and shared bookmarks), and tools to embed Google Gadgets.
Beyond the set, site analytics that monitor usage and hits on various pages or artifacts can also be useful in providing feedback, indices of value, and even fodder for advertising services to a set curator.
Relational databases are ideally suited to set modes of interaction because of their formal basis in set theory. However, looser kinds of database management systems may have greater value for some kinds of set data, especially where either very high performance trumps the need for accurate classification, or classifications are fuzzy, unspecified, or shifting.
Like all other social applications, communication and sharing tools are a prerequisite in set-based systems, with a greater emphasis on sharing than that found in network or group social systems. Because of the sporadic and bursty nature of set interactions, tools to notify people via other systems such as email or SMS are useful.
Verifiable identification of an individual in a set-oriented application is seldom as important as it is in networked and group applications, though profiles that reveal interests, skills, and purposes are very helpful in filtering for useful topics of interest. That said, one of the biggest difficulties when dealing with sets is determining the, accuracy, truthfulness, and trustworthiness of others in the set, so it is helpful to provide a means for allowing people to reveal some kind of persistent identity, even if it is pseudonymous and shifts between one set and another.
Another range of potentially valuable tools for set-oriented applications are those that provide controllable filtering. Given that there may be diverse viewpoints, and that some content may be boring or disagreeable to some members of the set, it is important to allow features such as the blocking of individuals, filtering based on keywords, and tools that enable learners to focus on specific things—again, curation tools are useful, as are personal “dashboards” that enable a learner to assemble collections of content and dialogue. It should be noted that filtering is a potentially double-edged sword. Though well-suited to anonymous engagement in a set, in network or group applications it can impose implicit censorship on members and thus play a powerful role in shaping the community and reinforcing its values, creating an echo chamber or filter bubble (Pariser, 2011) that may have harmful and unforeseen effects. Because sets, by definition, do not involve any distinct community, filter bubbles are less problematic, assuming that other sets addressing similar concerns are available for those that find their interests or beliefs are excluded.
Associated with the relative anonymity of their members and perhaps more than in any other social form, sets are frequently intertwined with collectives. It is rare to find a set-oriented application without at least some collective features and/or a large amount of editorial control. Rather than dwell on this in detail here, we will return to it in the next chapter.
Conclusion
Sets are a ubiquitous social form we all engage in both on and off the Internet. The characteristic forms of social engagement that emerge in sets in a learning context typically have to do with cooperation rather than collaboration. Set-based learning is about sharing ideas, resources, tools, media, and knowledge, and engaging with others on an ad hoc, transient basis. On many occasions, others will make use of what we have shared without our knowledge or consent: the value of the set therefore grows over time. Once persistent dialogues start to occur, set-based systems blur into net-based systems: one of the most notable uses of sets is as a means for forming networks and, occasionally, groups.
Arguably the greatest value from sets comes when they are the social form behind collectives, and the most effective sets make extensive use of collectives by creating structure and dynamic processes to drive them and capitalize on their features. We turn to collectives in the next chapter. | textbooks/socialsci/Education_and_Professional_Development/Teaching_Crowds_-_Learning_and_Social_Media_(Dron_and_Anderson)/06%3A_Learning_in_Sets.txt |
Learning with Collectives
But here is the finger of God, a flash of the will that can, Existent behind all laws, that made them and, lo, they are! And I know not if, save in this, such gift be allowed to man, That out of three sounds he frame, not a fourth sound, but a star. Robert Browning, “Abt Vogler”
So far we have looked at collections of people. Networks, sets, and groups are aggregations of individuals that define the relationships, norms, behaviours, and activities they perform, together and alone. We have seen that, though nets and sets offer many benefits to the learner, the loss of the technological structures of groups combined with the lack of teacher input can place a large onus on the learner to make decisions he or she may not be suitably equipped for, potentially leading to sub-optimal paths and, occasionally, fear and confusion that stands in the way of effective learning. In this chapter we turn to a different kind of entity, composed not of people but an amalgamation of their actions and products. We describe this entity as the collective. The collective can, under the right circumstances, replicate or even improve upon the organizational value of groups, networks, and sets without the overhead of group processes, and take on many of the roles of a teacher. Collectives are thus crucial to realizing the potential of the crowd; they are perhaps more than anything else, what gives modern social software the potential to be a truly radical departure from traditional educational approaches. We are only beginning to realize the benefits of collectives for learning, and there are many pitfalls and obstacles to overcome before they can fulfill their promise, some of which we address in this chapter. Collectives may be teacher-like, but without great care, they can be very bad teachers.
This chapter is organized much like those on groups, nets, and sets, but the emphasis in each section will be somewhat different for two main reasons:
• A collective plays the role of a teacher, not of a collection of learners. We are interested therefore not so much in how to learn in a collective as we are in how a collective can teach, or how we can learn from collectives.
• In cyberspace, a collective is usually a cybernetic technology, composed of both people and software. We will thus pay more attention to technological design principles for collectives in learning.
In terms of learning, the relationship is not between many and one or many and many in the same sense as we find in a group, set, or net, but is instead a oneto-one relationship between an individual and a single entity composed of many parts. Thus, in many ways, a collective plays the role of a teacher in a one-to-one dyad. The potential benefit of collectives as educational tools is great. Done right, they offer the benefit of human judgment as a driver of artificial intelligence. Traditional AI approaches attempt to mimic the thinking behavior of humans or other creatures, whether as a direct analogue (e.g., neural nets) or as an identifiably alien means of giving the appearance of thought. Collectives do neither: done right, they are simply a means of mining and using crowd activities to create wisdom. If we are able to harness such tools to help the learning process, then the wisdom of the crowd could guide us on our learning journeys. Mishandled, they can magnify and enable mob stupidity, and will only guide us in unhelpful directions.
Different Meanings of Collective
The word “collective” may stir up many associations of loss of personal identity. There is something threatening about the loss of individuality associated with the hive mind or fictional Borg collective, of course amplified when human choice to participate is eliminated, as exemplified in the Borg’s assertion, “Resistance is Futile.” Sandberg (2003) explores this concept, drawing unfavorable analogies between hive minds and those of humans, where the benefits of the super-organism are available only to those who have given up their individualism. Turchin and Joslyn in their Cybernetic Manifesto similarly describe metasystems that are created “when a number of systems become integrated so that a new level of control emerges” (1989, para. 5). They show that these higher control systems have developed from the control of movement, through control of individual thinking to the emergence of human culture. Again, we don’t like the coercive connotation of the word control, but we acknowledge that as life has evolved into more complex entities, metasystems are necessary for survival. However, there is no reason that a human collective should subsume its participants. It grows as a result of their activities, in principle taking nothing away from the individuals who form it. We see collective activity in a more tool-like fashion where one exerts individual agency to exploit an affordance provided by collective tools. We realize activities in cyberspace are constantly being extracted and shared at high speeds, and that there is a great risk to becoming enmeshed in a single world view, or caught in an echo chamber as the victim of a filter bubble (Pariser, 2011). But we don’t think this entails more loss of control than what we give to a traffic engineer or a radio station traffic reporter counting the number of vehicles using an intersection at any given moment. Indeed, it is less controlling because the whole Internet is only one URL away, and we do not need to use that intersection to get there. As the Internet ingeniously routes itself around damaged nodes, knowledge of the collective activity and possibility helps us make individual decisions. A collective is an addition, not a subtraction.
Of course, the collective can and often does make mistakes, and we see evidence of groupthink, erroneous or slanderous meme proliferation, filter bubbles that strain out uncomfortable ideas, echo chambers that amplify mundane or even evil ideas, path dependencies, preferential attachment, confirmation biases and more, not to mention illegal or immoral extraction of individual and identifiable activity from collective activities. There are potential dangers in collective creation that need to be dealt with through careful design, and we will discuss these at greater length, but such weaknesses are not strictly features of collectives: misuse and inefficiencies accompany all forms of human organization. One must judge the value of the tool’s use as compared to these costs, and the collectives of which we speak are tools, not mindsets. Even though, as a quotation attributed to Marshall McLuhan (1994) reminds us, “we shape our tools, and thereafter our tools shape us” (p. xi), we need practice and time to develop tool use in ways that allow us to optimize our individual and social selves in a complex universe. Resistance may not be futile, for in the resistance we recreate the technologies to meet our individual and social needs.
Many authors have attempted to grapple with what defines collective intelligence, but in ways that significantly depart from our usage. Malone, Laubacher, and Dellarocas (2009) describe a set of design patterns for different forms of collective intelligence of which the Collective itself, as we define it, is only one. For many, collective intelligence is the result of the combination of coordinated behaviors that represent the ability of a group to solve bigger, more complex problems, or to solve simpler problems more effectively than an individual alone could. Howard Bloom, for example (2000, pp. 42–44), lists five essentials for this kind of successful group intelligence:
• Conformity enforcers—mechanisms to ensure similarity among members
• Diversity generators—mechanisms to ensure some differences
• Inner judges—mechanisms to enable individuals to make their own decisions
• Resource shifters—mechanisms to reward success and punish failure
• Intergroup tournaments—competitions between subgroups.
Howard Bloom’s notion of the collective is both broader than ours, and narrower. Broader, because he sees collective intelligence as a combinatorial effect of many intentionally coordinated individuals, in which technology may play only a supporting role. Narrower, because his concern is with leveraging conventional group processes to achieve a good outcome. A slightly different way of viewing collective intelligence is provided in the field of distributed cognition. This is similarly concerned with a form of collective intelligence that is spread among many, including the artifacts they create: cognition necessarily occurs with others as a result of the shared objects and tools we use, and in the different skills and abilities of people who work and learn together. These definitions are compelling, but differ from our more bounded use of the term as they are concerned with ways we consider collective intelligence to spread among individuals and their artifacts, not as a distinctive agent in itself. We are not just concerned with collective intelligence as a form of distributed cognition, but with distinctive individual entities. This is why we call them “collectives” rather than “collective intelligence.” We are treating the combined behaviors of crowds as identifiable objects that in their own right embody a kind of collective intelligence.
Defining the Collective
Collectives are composite entities made up of the aggregated effects of people’s activities in groups, sets, and networks. In the natural and human world, collectives are commonplace. They are emergent, distinct actors formed from multiple local interactions between individual parts, either directly or mediated through signs, without top-down control. For example, ants leave a trail of pheromones when returning to the nest with food, and they act as a guide to the food for other ants, who leave their own pheromone trails in turn, thus reinforcing the trail and attracting other ants until the food runs out, when the trail evaporates (Bonabeau, Dorigo, & Theraulaz, 1999). The collective is the combination of ants’ interpretations of the signals they leave and those signals, which lead to the self-organizing behaviour of the whole that is distinct from the behaviour of any single individual. Similarly, a crowd gathered in a street acts as a magnet to individuals to join the crowd, which in turn increases the attraction of the crowd. Trading in currency, stocks, or shares reciprocally influences the market for those items, encouraging buying or selling by others, which in turn affects the behaviors of those who initiated the action and those who follow. It is not solely the actions of individuals that affect other individuals, but the emergent patterns left by the multiple interactions of many that engender changes in the behaviour of single individuals. Each individual interacts with a single collective of which he or she is a part.
Collectives can be intentionally created and mediated: for instance, when a teacher asks for a show of hands, or voters vote in an election, individual decisions are aggregated by some central authority and in turn influence the later decisions of those who make up the crowd. This can, for instance, help to swing undecided voters one way or another in an election. In cyberspace, a collective is often this kind of intentionally designed cyber-organism, with a computer or computers collecting and processing the behaviors of many people. Such collectives are formed from the intentional actions of people linked algorithmically by software and made visible through a human-computer interface. It is partially composed of software and machines, partially of the individual behaviors and cognitions of human beings. It is important to distinguish the role of the mediator in such a collective from an independent artificial intelligence. For example, a search engine that returns results solely based on words or groupings of words is not mediating the actions of a crowd: it is simply processing information. However, if that search engine uses explicit or implicit signals from its users or preferences that are implied by links in web pages—such as Google’s PageRank—then it is making use of the aggregated actions of many people to influence those who follow: it is a collective. It can be seen as a substrate for interaction more than a processing machine. While natural phenomena like ant trails and termite mounds are utilizing the physical properties of the world, computers allow us to manipulate the physics of interaction and create new ways of aggregating and processing what people have done, greatly extending the adjacent possibilities.
Groups, sets, and networks are defined by membership, commonalities, and relationships between people who usually share a common interest. Collectives involve no social relationship with other identifiable persons at all, unless social relationships happen to play a part in what is being combined. A collective behaves as a distinct individual agent: we do not interact with its parts but with the whole, to which our own actions may contribute. A collective thus becomes a distinct and active entity within a system, with its own dynamics and behaviors that are not necessarily the same as those actions of the individuals who caused it.
Collectives as Technologies
Most human collectives can be thought of as cyborgs, composed of human parts and a set of processes and methods for combining them that are, whether enacted in people’s heads or mediated via a computer, deeply technological in nature. As much as groups, collectives are defined by the technologies that assemble them. Just as a group is inconceivable without the processes and methods that constitute it, a collective is inconceivable without an algorithm (a set of procedures) enacted to make it emerge. While an algorithm is essential, this does not necessarily imply a technological basis for all collectives: there are plenty of natural collectives, such as flocks of birds, herds of cattle, swarms of bees, and nests of termites that are not assisted by any technology, at least not without stretching the definition of “technology” beyond bounds that we normally recognize. However, when an algorithm is enacted as a piece of software, as is the case in most cyberspace collectives, the collective is part machine, part crowd.
Some Corollaries of the Collective
From our definition of a collective, it follows that
• Someone or something has to perform the grouping of actions that make up the collective. This may be distributed among the collection of individuals, or centralized by an individual or machine.
• The subset of specific actions to observe must be chosen by someone (or some collection of people, or by a machine) from the range of all possible actions.
• What is done with the aggregated or parcellated behaviors has to follow one or more rules and/or principles: an algorithm is used to combine and process them.
• The result has to be presented in a form that influences actions by individuals (who may or may not have contributed to the original actions). Were this not the case, then the collective would have no agency within the system, and there would be little point to creating it in the first place.
We illustrate the collective graphically in figure 7.1. Note that individual components of the collective can be people, machines, or both, at each stage of the process.
Figure 7.1 A model of how a collective forms.
A collective often involves a feedback loop of mediated and transformed interactions. Behaviors of individuals are
• Captured (by observation or by technological mediators such as computers or vote collectors) Processed and transformed by algorithms (which may be applied by those individuals or by some other agent, human or machine) and
• Fed back or displayed more or less directly to those and potentially other individuals who, in turn, affect their behaviors.
A computer may or may not be involved with any part of that continuum. Significantly, it is possible for all the necessary processing and presentation that drives the system to be facets of individuals’ cognition and behavior, as we see in the formation of crowds on a street. Each individual makes a decision, the aggregate forming a crowd, which itself then acts as a recommendation to join the crowd, thus driving its own growth. The crowd is both a sign and the result of that sign. Equally, even when a collective is mediated, the computation and presentation may be performed by a human agent: a teacher collecting and summing a show of hands in a classroom to allow students to choose between one of two options, for example, is using collective intelligence to affect his or her behaviour. The decision that a teacher makes is not based on dialogue with an individual but with the complete set he or she aggregates, so that the whole class becomes a decision-making agent. Sometimes both human and computer are combined.
People and/or machines may perform the shaping and filtering. This may occur at several points in a continuum:
1. During the selection of relevant actions filtering is likely to occur, where the machine (controlled by a programmer) decides which actions to record from which people.
2. During processing, where the machine allocates priority or relevance in order to produce rankings and/or reduce the number of results returned.
3. During presentation, where the machine filters the items displayed or shapes the form of the display so that some are more prominent than others (e.g., through visual emphasis, list order, or placing at different points in a navigational network or hierarchy).
Because a collective may be seen as an individual agent, then recursively, it is possible to treat one as a part of other collectives. For example, when a collective such as Delicious, CoFIND (Dron, Mitchell, Boyne, et al., 2000) or Knowledge Sea (Farzan & Brusilovsky, 2005) is used to aggregate links pointing to other sites on a single page, that page is treated by Google Search (a collective) very much the same as one that has been created by an individual person. This recursion can reach considerable depth.
Stigmergic Collectives
The term “stigmergy,” from the Greek words for sign and action, was coined by the biologist Pierre-Paul Grassé to describe the nest-building behavior of termites and other natural systems where indirect or direct signs left in the environment influence the behavior of those who follow, leading to self-organized behavior.
Many collective systems are stigmergic, and in nature they afford many advantages. Stigmergy fosters actions and ideas that collectively allow the performance of “problem-solving activity that exceeds the knowledge and the computational scope of each individual member’’ (Clark, 1997, p. 234). Stigmergy can be seen in many systems, from money markets (where money is the signal), to nest-tidying in ants (where untidiness is the signal). It is rife in cyberspace, influencing search results returned by Google, for example (Gregorio, 2003), and is the foundation of educational systems that employ social navigation (e.g. Dron, 2003; Dron, Boyne, & Mitchell, 2001; Farzan & Brusilovsky, 2005; Kurhila, Miettinen, Nokelainen, & Tirri, 2002; Riedl & Amant, 2003), allowing users to become aware of the actions, interests, categorizations, and ratings of others.
Many systems that collect and display user-generated content have some stigmergic characteristics whereby individuals are influenced by the collected behaviors of the whole. For instance, users are influenced by the ratings or number and depth of postings to a forum, or by the number of viewings of changes on a social site’s front page. In each case, the system provides an interface that shows some aspect of crowd behaviour, which in turn affects the future behaviour of individuals making up the crowd.
Non-Stigmergic Collectives
While very common in collective applications, stigmergy is not a defining characteristic of a collective, or at least, not in a direct and straightforward manner. There is a variation on the theme that is as useful and in some ways superior to the self-organizing, dynamic form in which the choices and decisions of a crowd are mined, applying similar principles to other collectives to identify some decision, trend, or calculation. Such systems are almost all based around the use of sets, because those in groups and nets are usually far more aware of one another’s actions and are influenced by them. Classic examples of the genre are recommender systems and collaborative filters that make use of independently mined actions or preferences to identify future interests or needs. This is positive because as Surowiecki (2004) pointed out, crowds are only wise when they are unaware of what the rest of the crowd is doing. By definition, stigmergic systems break this rule, at least on the face of it. There is compelling evidence that Surowiecki’s assertion is true. The disastrous out-of-control stigmergic effects that fuel bank runs, where the people withdrawing money serves as a sign for others to follow suit, shows all too clearly the potential downside of people being aware of others’ actions. Similarly, Salganik, Dobbs, and Watts, (2006) show that when people can see the choices others have made for rating songs in a chart, it profoundly alters the overall charts: social influence in their study made for unrecognizably dissimilar chart results when compared to independent choices, and when compared to individual choices, the rankings are less satisfying for all concerned. This is not an entirely simple equation, however.
Author Dron performed a study to explore the influence of others’ choices on behaviour that showed a mix of behaviors from copying to rational decision-making, and on to deliberate obtuseness in selecting items that were as different from the items selected by others as possible (2005a). At the time, such effects seemed surprising: the expected behaviour was that people would generally make worse choices by copying those who came before, not deliberately avoid such behaviour. These results are, however, borne out by other research. Ariely (2009), for example, discovered that the beer-ordering behaviors of individuals in a group, as opposed to independent individuals, was significantly different. In this experiment, participants showed a tendency to deliberately order differently from their peers, even though their preference without such influence might have been for a beer that had already been ordered. While the influence of earlier people can skew results of collective decisions so that they are, at best, only as good as the first contributor, aggregated independent choices are far more successful at eliciting crowd wisdom.
We have a tendency to be influenced by decisions that came before, whether positively (we follow them) or negatively (we deliberately do not follow them). This is clearly evidenced on social sites such as Twitter, where what is “trending” or most popular is promoted, leading to sometimes vast waves of viral interest. However, as we have already observed, this can be problematic. There are some simple solutions, however, which do not limit crowd wisdom but still bring the benefits of adaptation and dynamic change that a feedback loop engenders. The most effective of these is the simplest: to introduce delay to the feedback loop (Bateson, 1972). If a crowd does not know what the rest of the crowd is thinking, then it is far easier for it to be wise. This is evident when poll results are displayed as an incentive to vote, but only after one’s preferences are entered. Flickr uses this to good advantage when supplying tag clouds for the previous day, the previous week, and overall: recent tag clouds are seldom valuable, though they can occasionally show the zeitgeist of the crowd. But as delay creeps in, they provide more relevant and potentially useful classifications.
While many collectives are not directly stigmergic, stigmergy may nonetheless re-enter the picture when results are returned to individuals. Google, for example, mines independent implicit evaluations of websites, but because it plays such a prominent role in helping people find pages of interest, it is more likely that pages appearing at the top of search results will be linked to, therefore reinforcing the position of those that are already successful in a stigmergic manner.
Cooperative Freedoms in Collective Learning
While the collective is not in itself a social form, and so is not directly comparable to individual, group, net, and set modes of learning (it relies upon those social forms in order to exist at all), there are some distinct benefits that emerge from its effective use. Most notably, although it will often inherit the limitations of its parent social form(s), it can be a gap-filler, adding freedoms that might have been unavailable in the other social forms. We do not present our customary diagram of cooperative freedoms for the collective, because it depends entirely upon the kind involved, but we describe some of the ways that collectives contribute to, or in rare cases, detract from cooperative freedoms.
Time
Collectives tend to inherit the limitations of the social forms they arise in. For instance, those that emerge in immersive and other synchronous contexts tend to appear in real-time, though timeline-based tools can add extra richness to such experiences and, if they are recorded, can add layers to the original interactions, for instance by showing patterns that may have occurred within the original interactions of earlier participants. Donath, Karahalios, and Viegas (1999), for example, used this to good effect in the stigmergic ChatCircles system, which was otherwise constrained to real-time dynamics. Similarly, when they emerge out of discussion tools, they can distil or mine patterns from them. For example, one of the earliest collaborative filters used for learning, PHOAKS (People Helping One Another Know Stuff), provided its recommendations by mining discussion forums for links to resources, and used those as implicit recommendations to others (Terveen, Hill, Amento, McDonald, & Creter, 1997), thus allowing freedom of time to engage with the system separately from the actual discussion that generated them.
Place
As with all networked tools, collectives provide few limitations on the location learning can occur in, except where they emerge in real time from collocated crowds.
Content
Freedom of content depends a great deal on the form that the collective takes. Many are used as recommenders of people or content, suggesting an assortment of alternatives that narrow down the choices that can be made. The effect of this can be very large and is always significant: the chances of a user clicking one of the first two items presented by Google Search, for example, are many times higher than they are for him or her clicking the last item on the page, even when results are deliberately manipulated to show the “worst” options first (Joachims, Granka, Pan, Hembrooke, & Gay, 2005). Interestingly, however, the chances of the user clicking on middle-ranked resources are even lower than they are for clicking the last item on a page. When we trust the collective, belief in its accuracy frequently overrides even our own judgments of quality (Pan et al., 2007). In some cases, such as when a user clicks “I’m feeling lucky” in a Google search, there may be no choice presented at all. Of course, we must remember that the user is always free to search somewhere else or for something different. We are aware of no collectives as yet that are used coercively; their role is always one of persuasion.
Delegation
The ability to delegate control to a collective is dependent on context. In many ways, accepting a recommendation or allowing a collective to shape an information environment is to intentionally delegate control to someone or something else. However, the typical context of collective use in current systems is that of the self-guided learner who has made an active decision to use the collective. Thus far, there have been few attempts made to use collectives to shape an entire learning journey, and those who have tried have not succeeded.
Relationship
Apart from the use of collectives to recommend people or shape dialogue use, collectives have very little effect on freedom of relationship. However, because a collective is an active agent akin to a human in terms of its behavior, it is often possible to engage more or less fully with the collective itself. Typically, one gives information to the collective in order for it to provide better information or advice. For example, the more information it has about you, the more Google will give you personalized and accurate results if you are logged in as a Google user, (i.e., it is more likely to give the results you are looking for). Similarly, many collaborative filters use explicit ratings and/or preferences (e.g., MovieLens or Netflix for movies, Amazon for books) to improve the accuracy of their predictions of what you may like.
Medium
As a rule, collectives are neutral to medium: they may or may not place constraints on the media used and, as we have already observed, they are usually used in a context where the learner has control over whether and which collectives are used for learning.
Technology
Many collective systems work equally well across various technology platforms. Again, however, the details depend on the precise context of use: a system that uses one’s location, for instance, is constrained to uses where the technology can provide that information.
Method
Once again, the context determines whether collectives provide a choice of method. Because they are mainly used by independent learners, the choice of method is more dependent on the learner than on the tool. Collectives on the whole act like controllable teachers, allowing the learner to choose what method suits him or her best. Very few existing collectives apply any intentional pedagogy, and this is an area that demands greater research.
Pace
There are few occasions where pace makes a difference when using a collective for learning, though there are sometimes constraints due to the time it may take for a collective to gain a sufficiently rich knowledge of both individuals and crowds to provide useful help. The vast majority of collective systems suffer from a cold-start problem: they only offer value when sufficient numbers of actions have been captured, so until then, there is no reason to use them, which creates difficulties for them to gain sufficient numbers to begin with. Most systems deal with this by making use of previously shared information (e.g., Google mines links from websites, PHOAKS mines posts in Usenet News, Facebook uses EdgeRank, and Delicious uses browser bookmarks), information from other domains (e.g., Amazon book “likes” may be used to identify similar people in order to recommend movies), or by using automated guesses based on content similarity or approximations from statistical data to provide reasonable recommendations earlier in the system’s development when there is insufficient crowd data.
Disclosure
For any collective to work at all, some disclosure of actions is required. However, in most mediated collectives, this is essentially anonymous. Though we may fear the motives of companies that provide collectives, this is a fear of disclosure to an organization, not to the collective itself. Where software is performing aggregation, it knows who you are but other people in the collective rarely, if ever, do. There are some exceptions, especially when collectives are concerned with establishing reputation. In such cases, there is a double concern: first, that one must disclose information about oneself to the software and, in principle, its owners in order to participate; and second, that it involves the delegation of one’s reputation to the crowd. In such cases, fear of exposure may be justified.
Transactional Distance in Collectives
The collective, as an emergent entity composed of a collection of people in sets, nets, and occasionally groups, plays the role of a teacher in a learning transaction, guiding, suggesting, collecting, clustering, and re-presenting the knowledge of the crowd. A learner interacting with a collective is engaged with something dynamic and responsive in a way that is quite different from engagement with a static book, website, or video, yet without the social engagement he or she experiences when interacting with an individual human being. At least for the foreseeable future, there will be little or no psychological connection between a human and a collective, or if there is, it will be one-way: collectives do not care about individual people. From the point of view of the individual, interacting with the collective is seldom more psychologically engaging than interacting with any artificial intelligence. This is not to suggest that the interaction is not powerful for the individual concerned, and one can claim that two-way communication has meaningfully occurred, just as with Furbies, Tamagotchis, or more recent AIBOs and Paros (robot baby seals intended to provide companionship for the elderly— www.parorobots.com; Turkle, 2011).
The two-way dialogue with a collective can occur in many ways. One of the motivations behind Kay & Kummerfeld’s (2006) scrutable user models is to allow people to talk back to the collective, which otherwise can make decisions on the behalf of users that are not helpful. Many people have deliberately watched content that they would not otherwise choose on collaborative filter-based TiVo devices, for example, to stop the machine from making wrong or embarrassing predictions about what they would like to see (Zaslow, 2002). A very distinctive feature of collectives is that the individuals who interact with them are also typically a part of them, active contributors to the collective intelligence. This is distinct from our engagement with people in social forms: we may be part of a net, set, or group, but the individuals within them are still distinct, and at least in principle, identifiable. The collective is an active individual agent of which we are a part. All of these complexities make transactional distance between learner and collective a very unusual but quite distinctive phenomenon. The collective creates high structure, shaping the information space that the learner inhabits, but the learner is part of the collective, and in many cases can control the results, whether through direct intervention (e.g., in Netflix, specifying the kinds of movie he or she would like to see), behavior modification intended to affect results, or simply by choosing from one of multiple options.
Examples of Collectives
Rating Systems
The majority of systems that provide a means to implicitly or explicitly rate someone or something make use of collectives. These vary in sophistication from simple aggregators to full-blown collaborative filters, where ratings are used to compare an individual with the crowd, and on to rich metadata that provide ratings across a range of dimensions.
A few examples include Slashdot Karma Points and categories, Facebook Likes, Google+ Plus-ones, and countless systems that provide Lickert scale-style ratings such as Amazon and YouTube.
Collaborative Filters
Collaborative filters are recommenders that make use of similarities between people (e.g., people who share a similar pattern of interest for things like books or movies) or similarities between crowds of people implicitly or explicitly liking particular items (e.g., people who bought this also bought that). Some examples are Amazon Recommends, Netflix, and MovieLens.
Data Mining and Analytics Tools
A number of collective applications mine existing content in order to identify patterns, preferences, and structures that might otherwise be invisible. For instance, Cite-U-Like and Google Scholar provide recommendations based on citations to scholarly papers, Google Search ranks results according to the number of links mined from web pages, and PHOAKS looks at links in newsgroup postings to identify implicitly recommended articles.
Swarm-based Systems
Swarm-based systems mimic the behaviours of groups of ants, birds, fish, and other naturally occurring crowds in order to bring about self-organization in a crowd based system. These are most often used to control work of very simple robots to collectively complete a complex job. Tattershall and his colleagues have used this process to provide sequencing recommendations for learners (2004; van den Berg et al., 2005). Though it can work reasonably well with a closed corpus such as a conventional course where there are limited potential paths and defined goals, this kind of approach falls flat in the large open corpuses of set and net interactions. Particle swarm optimization systems take a slightly different approach, and are typically used in goal-oriented systems to optimize multiple behaviors towards a single solution. They are sometimes used with Genetic algorithms (GAs) to rule out inappropriate resources to filter results (Huang, Huang, & Cheng, 2008).
Ant Colony Optimization Systems
Systems using ant colony optimization techniques make use of virtual pheromones to capture paths and actions taken by the crowd in order to adapt content, presentation, process, sequence, and other elements of a user’s experience. Some examples are AACS (Yang & Wu, 2009) and Paraschool (Semet, Lutton, & Collet, 2003).
Social Navigation Systems
Systems that employ social navigation capture browsing behaviors and actions such as tagging or commenting in order to modify an interface to emphasize or (sometimes) determine certain paths at the expense of others. For example, CoFIND used rank order, font style, and font size to indicate resources that are viewed as useful by the crowd (Dron et al., 2001). Educo used representations of individuals as clustered dots surrounding resources that were more widely used (Kurhila et al., 2002), Knowledge Sea 2 used color depth to indicate more visited resources (Farzan & Brusilovsky, 2005), and CoRead used different highlight colors to indicate passages of texts that have been more or less highlighted (Chiarella, 2009).
Social Network Discovery Engines
The vast majority of social networking sites use some means of discovering others with whom to connect. The algorithms may be quite simple, such as link analysis to discover friends of friends. Indeed, the commonly used FOAF protocol was explicitly built to exploit this. Others may simply identify other people in groups an individual belongs to, but some can be more complex, taking into consideration profile fields, browsing behaviors, and the content of posts. A sophisticated example is Facebook’s EdgeRank, which takes a range of factors (a trade secret) including not just connections but numbers and frequency of interactions into account when presenting content, as well as numerous set-oriented factors (Pariser, 2011). In a learning context, we have provided an Elgg plugin that assists discovery of both friends of friends and people in shared groups (community.elgg. org/plugins/869921/1.2/suggested-friends-18x).
Crowdsourcing Tools
Crowdsourcing systems typically rely on user-generated content in response to a particular problem, question, or project request. While some rely on the person posing the problem to sort through potential solutions, such set-oriented applications are very often enhanced with collective tools that solicit implicit or explicit ratings from the crowd in order to rank the effectiveness of the solution: these include Yahoo Answers, Quora, Amazon Mechanical Turk, and Innocentive.
Tools to Assess Reputation
A number of systems mine data such as citations and references in order to discover experts rather than content; for example, Cite-U-Like and Connotea. There is abundant literature on refinements to these approaches (Ru, Guo, & Xu, 2008; Smirnova & Balog, 2011). Social networking systems such as LinkedIn make use of networked endorsements to provide a collective indication of reputation within a field while others, such as academia.edu, make use of citations and papers to help emphasize reputation within a field.
In many network-oriented systems, the connections explicitly made between one individual and another by “friending,” providing links in a blogroll, commenting or linking within blog posts and so on, provide the necessary recommendations for us to trust others. If someone I admire admires someone else, that acts as an effective indicator of reputation. It is an old technique that can be quantified and turned into a collective with relative ease: weighted citation indexes use the same kind of approach to indicate the significance of an academic paper.
Going beyond those we know in a large network, reputation (apart from for a few of the most well-known people within the network) can be harder to identify, and collectives rapidly become the most important tool for identifying value. Systems such as Slashdot, Spongefish, or Graspr can be remarkably effective self-organizing learning resources because of the methods they use to identify reliable/useful contributors and resources. Slashdot and Graspr (now defunct) both make use of a karma-based system, whereby “good karma” is gained through a variety of crowd-driven mechanisms.
Spongefish (a how-to site that folded in 2008) took a simpler but more comprehensible approach where coins denoted social capital for a teacher. In each system, there is an economy: those who already have points/coins are able to distribute them to others, thus ensuring that reputation is decided by those who already have a reputation, an approach that ensures at least some assurance of quality. However, the failure of so many systems points to the difficulty of getting algorithms right and designing interfaces that do not overwhelm their users with complexity. Slashdot (with its tagline, “News for Nerds”), one of the earliest, and still the best of collectives, survives largely due to its target user base that not only tolerates but also revels in its complexity.
Within an educational setting, such systems can offer several affordances. For example:
• Learners can be encouraged to gain reputation and submit that as part of a formal assessment. Used with care, and bearing in mind the risks of subverting such a system, this can offer motivation in the right places.
• Learners can use such systems to identify resources and people of value, thus filtering out those who may be distracting or misleading.
• Learners can be encouraged to rate/rank/pass on points or coins to others, encouraging critical and reflective thinking and encouraging them to engage more deeply with the community.
Learning with Collectives
In previous chapters about groups, nets, and sets, we have labeled this section “Learning in x.” In this chapter we deliberately describe this as learning with collectives, as the collective is an active and influential participant in a learning process, far more akin to a teacher or content than it is to a collection of people. At once human and mechanical, the collective is an alien kind of teacher engaged in a dialogue with its parts.
There are many roles teachers must play in a traditional educational system. Here is a short list of some of the main ones:
• Model thinking and practice
• Provide feedback
• Design and assemble learning paths
• Schedule learning
• Convey information
• Clarify and explain complex topics
• Assess learning
• Select and filter resources and tools for learning
• Care for students and student learning
• Provide a safe environment for learning
The majority of these roles, if not all, can be played by a collective to some extent. It should be noted, however, that enthusiasm, caring, passion, and many of the most valuable personal attributes of a teacher will not be present, though they can be mimicked by a collective. The collective plays the functional roles a teacher might perform.
Modelling Thinking and Practice
Little will substitute for observing a real teacher modelling good practice and demonstrating how he or she thinks about an issue, but of course collectives occur within social communities where such things are already possible. However, some kinds of collective can be used to promote and aggregate such behaviors. Karma Points and ratings, for example, can combine to show the informed user not just relevant content, but also the cream of the crop—not just a single teacher, but the “best” of those who contribute to a discussion or a debate. The collective is, by the judgment of the crowd contextualized to the needs of the viewer, an “ideal” composite teacher.
Providing Feedback
Even a simple rating system of “thumbs-up” or page view counters can tell a learner his or her work is valued. However, this is not particularly rich feedback, serving a motivational purpose more than offering guidance. Moreover, in some cases this can be demotivating, if it is viewed by the learner as an extrinsic reward (Kohn, 1999). Such guidance is still more a function of the social modes of engagement, group, set, or net, than of the collective. Having said that, a range of collective systems have been developed that provide somewhat richer feedback, including the nuanced rating system of Slashdot, and the more freeform “qualities” used in CoFIND (Dron, Mitchell, Siviter, & Boyne, 2000). These systems allow ratings across multiple dimensions that, at least in the case of CoFIND, can be pedagogically useful. People may, for instance, choose to rate something as “complex,” “complete,” or “well-written,” thus giving valuable feedback that in some ways betters that of an individual teacher, if sufficient ratings are received. Such systems also show rater variability, which itself can be more instructive than the stated preference of a single teacher (even a wise one).
Designing and Assembling Learning Paths
A number of social navigation-based systems provide weighted lists of recommendations of what to do next (Brusilovsky, 2004; Dron, Mitchell, Siviter, & Boyne, 2000; Kurhila et al., 2002; Wexelblat & Maes, 1999). Others have used techniques such as ant-trail optimization, swarming, and other nature-inspired techniques to offer recommendations (Wong & Looi, 2010; Semet et al., 2003). Many recommender systems that use various forms of collaborative filtering similarly present alternatives of what to look at next, based on previous behaviours of other learners (Drachsler, Hummel, & Koper, 2007; Freyne & Smyth, 2006; Hummel et al., 2007). However, it has proved difficult to do more than present suggestions for the next step in the path. Generating a plan of activities for a learner to follow poses significantly greater challenges, though many have tried (Pushpa, 2012; van den Berg et al., 2005; Yang & Wu, 2009). There are several reasons why they have not yet been wholly successful: learning is a process of change in which it is hard to predict in advance how a learner will develop as a result of each step.
When teachers design courses well, they do so based on their experience and conceptions of the topic as well as pedagogical considerations and knowledge of learners, resulting in an assembly that is intricately connected and cohesive, involving deep content knowledge, and importantly, an understanding of how to tell a story about it. Many adaptive systems have attempted to do the same and can work fairly well for individuals or group-based learners, but few (if any) have succeeded when dealing with an open corpus of knowledge, which is commonplace in net-based and set-based learning situations.
Some have used ontologies for connecting sequences of resources that are collectively generated (Karampiperis & Sampson, 2004). Though computationally elegant, this has been a profound failure from a learning perspective. The main reason is because pedagogically appropriate paths are not the same as expert opinions of the relationship between one topic and another. Even assuming a sufficient body of material can be effectively marked up and put in relation to others, subject discipline relationships seldom translate into good learning paths.
A promising approach is to combine recommendation methods with expert-generated curricula (Herder & Kärger, 2008) and these are relatively easy to generate in a constrained set of well-annotated resources within a group-oriented institution (Kilfoil, Xing, & Ghorbani, 2005; van den Berg et al., 2005).
Scheduling Learning
Closely related to the design of curriculum and learning paths is the means to synchronize activities and pacing. This has long been an important role for a teacher, often played by an institution in group-based learning, and is a common characteristic of group-based approaches, but it is usually difficult to achieve in network and set learning. However, collectives can take on some of that role. The simplest tools for this task allow an individual to specify a list of possible dates and others to indicate their availability. The tool aggregates potential times, and automatically or semi-automatically, suggests the most appropriate time when as many learners as possible are available. Plentiful free tools of this nature such as MeetingWizard, Doodle, Congregar, Setster, and Tungle are available on the Web and, in some cases, for cellphones.
Conveying Information
On the whole, collectives are not used to convey information from the ground up, but to collect, filter, refine, order, and display information that already exists. They provide ways to organize information rather than generating it in the first place. This organizational process can be quite powerful, however. Slashdot, for instance, is able to tailor content to specific needs, and allows relevant and reliable posts to provide nuanced insight into the topic under discussion that greatly surpasses what any individual teacher might be able to say on the subject, simply due to diversity and breadth of coverage. Other systems can help to visualize a complex subject area or social connections that might otherwise remain hidden (Buckingham-Shum, Motta, & Domingue, 1999; Donath et al., 1999; Vassileva, 2008).
Perhaps one of the most important sources of learning content today and a notable exception to the norm is Wikipedia. Wikipedia arguably uses stigmergic and similar collective processes, largely enacted in the minds of its contributors, underpinning and affecting ways that pages grow (Elliot, 2006; Heylighen, 2007; Yu, 2009). Basically, people are affected by signs left by other people in the environment but, for the most part, this is simply an anonymous mediated dialogue, a set-based interaction where each edit builds on the last, but without the distinctive self-organizing character of a true stigmergic system. However, there are a few genuinely stigmergic elements. Changes made by others affect not just content but also style, in ways that are analogous to stigmergic processes in nest-building ants or termites. Similarly, the use of wiki tags—metadata that relate to the content of pages—leads to predictable patterns of editing: the tags act like pheromones that guide others in their editing (den Besten, Gaio, Rossi, & Dalle, 2010).
Wikipedia also provides some embedded intentionally designed collective tools, such as pages showing trending articles that are truly stigmergic: frequency of use and editing affects the behaviour of others that follow. While it does include some collective elements, it is important to observe that Wikipedia is more of a farm than a self-organized jungle, and its power lies in its organizational and automated tools for assuring quality, not in collective processes. The collective aspects of the system simply help to shape its development rather than playing a major role in content production.
While difficult to generalize beyond specific contexts, there have been some interesting collective approaches to the creation of artwork, many of which have persisted and grown for ten years or more: www.absurd.org, www.potatoland. org, or snarg.net, for example. More recent systems like PicBreeder (PicBreeder. org), Darwinian Poetry (www.codeasart.com/poetry/darwin.html), and a wide variety of music evolution systems (Romero & Machado, 2008) use the crowd to choose between mutated forms of artworks, thus acting as an evolutionary selection mechanism. As a means of reflection on what creates value, this may be useful in an educational context. The potential for actually providing educational resources beyond such specific domains, however, seems limited.
Clarifying and Explaining Complex Topics
Collectives can be used to extract meaning and sense from a complex set of materials. For example, CoRead is a tool that allows collective highlighting of texts, in a manner similar to that employed on Amazon Kindle devices (Chiarella, 2009). Learners can see other learners’ highlights, and a simple color scheme is used to indicate which words and phrases have been highlighted the most. This allows those who come to a text to identify the words and phrases that others have found important or interesting. Similarly, tag clouds within a particular site or topic area can help learners to get a sense of the overall area and keywords associated with it. This can be particularly useful where the tag cloud is combined with a collaborative filter showing recommended tags that appear with selected tags more often, as can be found in Delicious. By viewing associated keywords, the learner is able to make connections and see generalizations that situate a topic within a network of ideas and concepts.
Assessing Learning
Several social systems provide rating tools. In many cases, these are simply variations on good versus bad: simple “thumbs-up” links such as Facebook “Likes” or Google +1s, for example. Unfortunately, this is seldom valuable to a learner seeking feedback on the success of his or her learning, unless the context is highly constrained, because there is not sufficient information to identify the reasons for the “like.” It can, however, work reasonably well within a group, especially in a large group such as one found in a MOOC, if the meaning of “good” and “bad” has been explicitly identified within that context. In sets or nets, there are few opportunities to provide such constraints.
Moving beyond simple ratings, some systems contextualize ratings within specific sets of qualities or interest areas. This can provide far more useful feedback on learning, though typically at the cost of far greater complexity for the people contributing ratings. CoFIND (Dron, Mitchell, Boyne, et al., 2000), for example, allows learners to not just rate a resource as good or bad, but to use fuzzy tags known as “qualities.” Qualities are tags with scalar values attached, allowing their users to both categorize a resource and say that it is more or less good for beginners, complex, detailed, accurate, reliable, authoritative, well-explained, or nicely structured. This kind of rich feedback can be very helpful. However, it is harder to use qualities to tag items than to use more conventional discrete tags, because their users must not only provide a category but also a rating for it. Other systems such as Slashdot provide a more constrained list: its basic comment filter allows users to identify whether comments are insightful, informative, interesting, or funny, which assists in filtering content and also helps the poster to know how others feel about the post. Though not intended for assessment, LinkedIn endorsements provide an intriguing and effective way to use collectives generated from networks to judge an individual’s skills. Skills that have been tagged in a user’s profile may be endorsed by those who are in that user’s network, thus providing a collective view of a person’s accomplishments that is both bottom-up, and in aggregate, trustworthy. LinkedIn makes good use of reciprocity, social capital, and individual vanity: when someone has endorsed you, it is hard to resist viewing your growing list of endorsements, and the site then prompts you to endorse others based on the skills identified in their profiles.
Selecting and Filtering Resources and Tools for Learning
The selection and filtering of resources and tools for learning is an important role for most teachers and is, in principle, what collectives do best. This is the role that Google plays when providing us with search results, using many collective processes to help assure quality and relevance of the results that it provides. Likewise, when Amazon provides recommendations of books we may want to read, it employs item- and user-based collaborative filtering techniques to make it likely that we will find something of value. Both are powerful learning tools, and this point has not been lost on the academic community. Over the past two decades, there have been many systems explicitly designed to use the crowd to recommend resources in a learning context (M. Anderson et al., 2003; Bateman, Brooks, & McCalla, 2006; Chiarella, 2009; Drachsler, 2009; Dron, Mitchell, Boyne, et al., 2000; Farzan & Brusilovsky, 2005; Freyne & Smyth, 2006; Goldberg, Nichols, Oki, & Terry, 1992; Grieco, Malandrino, Palmieri, & Scarano, 2007; Huberman & Kaminsky, 1996; Hummel et al., 2007; Jian, 2008; Kurhila et al., 2002; Tattersall et al., 2004; Terveen et al., 1997; van den Berg et al., 2005; Vassileva, 2008).
These systems include approaches such as social navigation, swarm-based methods, collaborative filtering, rating, and many more. When done well, crowdbased approaches to recommending resources, parts of resources, people, and tools have many benefits. Many hands make light work, and a crowd (especially a diverse set) can trawl through far more resources than an individual teacher. Depending on the way the collective is constructed, crowds can also be wiser than individuals (Surowiecki, 2004), succeeding in identifying facts or quality where individuals may fail.
Resource discovery is of great value in a formal setting. Because of the focused nature of closed groups in educational institutions, resource databases can become an extremely valuable facility, allowing the group to engage in developing a read/ write course with relatively little effort. This offers a wide assortment of learning and practical benefits:
• It reduces the cost of course production
• It keeps the course current and topical
• It gives students a strong sense of ownership, which in turn increases motivation
• It provides a simple means of learning by teaching: selection of resources, combined with some ranking and annotation, encourages reflection on both the resource and the learning process (i.e., how and in what ways it is helpful to the learner in his or her own learning process)
• It multiplies the possibilities of finding good and useful resources, leading to a far greater diversity and range than a single teaching team could hope to assemble alone.
It is best if such systems include at least some form of collective ranking, so that students can vote resources up or down, or provide implicit recommendations by clicking on links that can be fed back to the crowd through social navigation features. If such a system is not available, the next best thing is the capability to annotate or comment on links other learners have provided: the presence of comments can act as a simple stigmergic indicator of interest, positive or negative—both have value. If the system itself does not allow anything of this nature, then it is better to either use a more free-form system such as a wiki, or to go beyond the managed environment and make use of systems such as Delicious or Furl to create closed lists of bookmarks where commentary and tagging is allowed.
Tag clouds are a potentially powerful means of making resource discovery easier in a group, once resources have been added to the system. Within groups they are often different from and can sometimes offer greater value than those in large networks, because they adapt more quickly to the changing foci of the group. In a teacher-dominated environment, they can provide a more constrained and closed folksonomy than one allowed to develop without such control, a sort of hybrid of top-down control and bottom-up categorization. In some circumstances this can be useful: a shared vocabulary, if understood by all, helps to make sense of a subject area as well as making it easier to locate relevant resources. By categorizing the world, the teacher is enabling students to understand it better.
Caring for Students and Student Learning
There are some vital things teachers do that are far beyond the grasp of collectives. As our analysis of transactional distance in collectives suggests, the psychological gap between collective and learner is about as big as it can get. We know that other people have helped the collective to provide us with information, structure, process, or design, but that does not help us to feel closer to them and there are virtually no ways they can care for us or what we do. Collectives are only part of a solution to providing a rich and rewarding learning experience, and some things are, at least for now, best left to humans. Having said that, collectives can provide a gauge to let us know that unspecified others care about us: the “plus ones” or “likes” from popular social sites can improve a sense of social well-being and worth, albeit seldom with explicit pedagogical intent. They can also provide support for establishing connections with those humans. There are even aspects of the caring role a collective can play. For instance, they can be used to help nurture and guide learners to become more engaged and motivated (Glahn, Specht, & Koper, 2007).
The field of learning analytics has been experiencing rapid growth in recent years. It draws from a variety of fields: web analytics, educational data mining, adaptive hypermedia and social adaptation, and AI. Its purpose is to uncover indicators of learning, obstacles to learning, and information about learning pathways to help guide learners’ journeys. For teachers, it improves teaching methods and discovers weaknesses and risks before they become too dangerous. Some have extended this purpose to include analytics that interest administrators, institutions, and employers of teachers but, though such uses can and do have an impact on learning, we are of the opinion that it is no longer about learning when the process is applied this way: it is more a question of teaching analytics or institutional data mining.
The value of learning analytics to a teacher’s ability to provide care is that it allows him or her to become a part of a collective, in much the same way that asking for a show of hands to check if students have understood a problem in a classroom uses the crowd to change behavior. Processed results that inform a teacher of the progress of students leads to changes in his or her behavior, and thus can help the teacher to provide more assistance when needed. For example, if analytics show that, in aggregate, many students are having difficulty with particular lessons or concepts, the teacher can be more supportive in those areas. Analytics can also help to identify particular learners or groups of learners who are at risk. It can help to uncover patterns in behavior for disparate students or identify commonalities that lead to difficulties. For example, if it appears that most of those who submit work after a certain date or who lack particular qualifications have difficulties, then the teacher can intervene to advise them of the dangers. In effect, the teacher becomes part of a crowd-based recommender system.
Dangers of the Collective
While collectives can play several teacher roles in a system, they do not always make good teachers. There are many ways in which a wise crowd can become a stupid mob.
The Matthew Effect
The Matthew Effect, coined by Merton (1968) from the biblical aphorism attributed to Jesus “Whoever has will be given more, and he will have an abundance. Whoever does not have, even what he has will be taken from him” (Matthew 13:12). In the specific learning contexts examined here, this saying can be interpreted as a result of path dependencies and preferential attachments that set in early in a collective system’s development. If the system affects behavior (e.g., it encourages clicking of one resource or tag, or suggests people with whom to connect), then those who gain an early advantage are far more likely to retain it and be more influential than those who come later. The rich get richer while the poor get poorer. A classic example of this is presented by Knight and Schiff (2007), who discovered that early voters in US primary elections have around twenty times the influence of late voters on the results. This is because media reports the relative swings of voters, which in turn influence those who are undecided as to how to vote. Voters want to make a difference, usually by being on a winning side or, occasionally, to defend a candidate in danger of losing. Similarly, Salganik et al.’s (2006) study of artificial pop charts, mentioned in Chapter 6 shows strong Matthew Effects on music preference.
Many collective systems suffer from this problem. Google’s search results are a particularly prominent sufferer from the Matthew Effect. Because Google mines for links that are treated as implicit recommendations (L. Page, Brin, Motwani, & Winograd, 1999), and because people are far more likely to click on the first few links in the search results (Pan et al., 2007), this means that they receive greater exposure to pages that are already popular. Of course, it is only possible to provide links to sites that one already knows about (Gregorio, 2003) so such links are more likely to appear in the future. Because Google commands such a large share of search traffic, the overall effect is quite large. Many systems provide checks and balances to prevent rampant Matthew Effects from overwhelming new or equally valuable resources. Some use deliberate decay mechanisms (Dron, Mitchell, Boyne, et al., 2000; Wong & Looi, 2010), some introduce deliberate random serendipity, while others, including Google and Facebook, use a wide range of algorithms, collective and otherwise, to massage results so that there are no single persistent winners.
Unfortunately, many collectives occur without deliberate planning or forethought. For example, the presence of many or few messages in a discussion forum can act as an incentive or disincentive to others to contribute to a discussion, or a rating system can be used, as in Salganik et al.’s (2006) study that does not prevent runaway preferential attachment. The spread of viral memes in a population is another example of the Matthew Effect in action, where repeated exposure from multiple channels spreads through a network with increasing repetition (Blackmore, 1999).
Filter Bubbles and Echo Chambers
As Pariser (2011) observes, collectives play a very large role in the creation of filter bubbles. A recommender system, be it Google, Amazon, Slashdot, or any other system that filters and weights resources according to implicit or explicit preferences, runs the risk of preventing us from seeing alternative views to those we already hold or accept. This can function recursively and iteratively, especially where implicit preferences are mined on our behalves, creating a “bubble” over us that allows only similar ideas to those we already hold to penetrate. If what we see is limited to a subset of possibilities, then there are great risks that we will increasingly be channeled down an ever more refined path until we only see people we agree with and things we already know. For learners who, by definition, wish to move beyond their present boundaries, this can be a particular issue. As long as there are many alternative channels of knowledge this is not a major problem, but with increasing aggregation of data through things like tracking cookies, especially when we are using more personal devices like smartphones and tablets, the number of channels is quickly diminishing.
In a single browsing session, Felix (2012) reported that Facebook alone sets well over 308 tracking cookies without the user granting any explicit permissions, and these can be used by any subscribing sites to customize content and presentation. The lesson this teaches is that it is not always wise to join Facebook, but if one does, blocking tracking cookies using browser add-ons like TrackerBlock for Firefox, or AVG’s (currently free) do-not-track browser add-on may help to prevent many recommendations based on past activity. A simpler but less reliable approach is to ensure that one is not permanently logged in to a particular commercial social system. The penalty to be paid for such methods is, however, a loss of functionality: things such as Facebook “like” buttons will no longer work, for example. While one of the worst offenders, Facebook is far from alone in performing wide-ranging tracking. Google’s many services, for example, make extensive use of knowledge about who you are to shape the kind of results you receive from their search engine.
Sub-Optimal Algorithms
To err is human, but a collective can really make a mess of things. While the results of a Google search or a recommendation from Amazon or Netflix can be remarkably useful and accurate, they can equally be off the mark, unsuitable to our learning needs and, even if valuable, there may be better alternatives. The recommendations of collectives may be better than those that come from the reflective and critical skills of a human curator, but it depends on many things, notably the selection pool, the algorithm employed, the means of presentation, and the kind of problem being addressed. Despite the best efforts of many researchers and developers, we are some distance away from a perfect set of solutions for all learners and contexts.
Deliberate Manipulation
Another problem with collective systems is that it is hard to build them in a manner that prevents abuse by those who understand the algorithms and presentation techniques they employ. For example, author Dron had a student who added his own work to a self-organizing link-sharing collective system, and who then made use of the naïve social navigation methods the system employed to emphasize and de-emphasize tags, (which was little more sophisticated than a clickthrough counting system at the time), to promote his own website. Although the system did stabilize in the end as people realized where they were being sent and found it wanting, for a while his site became quite popular. More problematically, the experience left other students feeling less trust in the system. It would be nice to think that this problem had gone away with the increasing sophistication of social systems but, at the time of writing and for at least the past year, Flickr’s recent tags are dominated by advertisements and other more dubious content that fails to represent the wisdom of the crowd and results from intentional abuse. This particular collective within Flickr is to all intents and purposes useless but, sensibly, Flickr employs a wide range of other collectives at different time scales capturing different actions so that they may still be usefully employed to find things of good quality and interest to many.
Loss of Teacher and Learner Control
Like networks and sets, collectives pose issues of control that take away some of the traditional power of the teacher in an educational environment. Author Dron has been writing and using collective applications since 1998 and has experienced both more and less delightful results. For example, when he placed his own lecture notes in a collectively driven link-sharing system, (which used advanced tagging and annotation along with self-organizing algorithms to raise or lower resources in ranking according to perceived usefulness), he found that they did not always stay at the top of the list, and once vanished into the second page of results. While it is possible that his notes were terrible, previous evaluations of them had been good and they had been used internationally by other teachers. Instead, this seems to be a positive sign that the collective was better and made more useful recommendations, a supposition borne out through interviews and observations (Dron, 2002) but still potentially bruising to a teacher’s ego.
Lack of Pedagogical Intent
Most cybersystem users have “wasted” time following links suggested by systems. Learning is hard work, and more often than not requires focused effort. Collectives are not great at reinforcing such solitudes. The wisdom of the crowd requires the crowd to share a purpose of learning. For example, when using a system with a combination of wiki- and MOOC-like elements which are self-organized according to a combination of stigmergic principles and a design inspired by Jane Jacobs’s principles of city design (Dron, 2005b), postgraduate students studying the effects of using communication technologies actually wound up creating a set of resources about chocolate, which interested them more than the subject at hand. Apart from the students’ interest in chocolate, there were two main causes of this: on the one hand, this was group work and a poorly defined context and lack of direction made it unclear what was expected. On the other, the process was self-reinforcing and ran out of control, a common problem in stigmergic systems, whereby the rich get richer and the poor get poorer (the Matthew Effect). The combination was good for learning about chocolate, but less effective as a means to think about how we are affected by communication technologies. This was an experimental system, and the episode helped to establish and refine principles for limiting such divergence that we discuss in this chapter.
Shifting Contexts
A collective that has evolved for one purpose may be counter-productive when used for another. For example, collaborative filters that identify preferences based on past preferences may be of little or no value to learners because, having learned what they need to, they no longer require similar things (Drachsler et al., 2007; Dron, Mitchell, Boyne, et al., 2000). In a different context, we need a different collective.
Design Principles for Collective Applications
Collectives are predicated on the existence of collections of people, whether in groups or networks. A collective application, perhaps to a greater extent than network, set, or group applications, is potentially far more influenced by the designer, so it is no coincidence that this section of this chapter is larger than those on designing for social forms.
As a cyborg, a collective consists not only of the actions and decisions of individuals but also of the algorithms and interfaces designed by its creator. People are the engine that drives the vehicle, and on occasion perform most of the work in giving it form and function (for instance, in deciding whether the level of threading in a discussion forum is too great or too little to be of interest), but the vehicle itself usually plays a far more significant role in the application than in those designed for networks and groups.
It is important to identify those elements that relate to each of the stages of a collective application: selection, capture, aggregation, processing, and presentation. This must include the things that our programs will do, what we expect people to contribute, and which actions to monitor. Without such a guiding heuristic model, we are likely to be surprised by the results.
In the following subsection we provide a range of issues and heuristics to be considered when designing collective applications for learning. It is not difficult to create a collective application, but it is more complex to create one that helps people to learn. This is very much an overview of large design patterns rather than a guide to building collective applications for learning. Knowledge of collective intelligence mechanisms such as Pearson Correlation, Euclidean Distance, neural networks, and Bayesian probability is very useful, even essential if one is to seriously engage in building such systems, but we will not be covering these technical issues here. Instead, we refer programmers who are interested in the mechanics of collective applications to Segaran’s Programming Collective Intelligence (2007), which is an excellent primer on the topic and relates almost exclusively to the kind of collective we speak of here. The socially constructed wiki, “The Handbook of Collective Intelligence,” (scripts.mit.edu/~cci/HCI/index.php?title=Main_page) is a more formal but less practically oriented treatment of the topic that also covers related ways of thinking about collective intelligence.
Parcellation
As Darwin (1872, chapter XII-XIII) was the first to observe, parcellation is an important feature of an evolving system. This is especially significant when considering large sets, nets, or groups of a tribal form. Without some means of separating out smaller populations, path dependencies mean that the Matthew Effect keeps the successful at the top of the evolutionary tree and makes a system highly resilient to small perturbations, such as new or different ideas. To enable diversity, the evolutionary landscape must be parcellated in some way. This is why many of Darwin’s greatest insights came from his visit to the Galapagos Islands, where different species had evolved in isolation. In a learning context, a massive site like YouTube would be of little value if it were not possible to separate out subsections: videos of cats would likely overwhelm those of broader educational value. Similarly, it is possible to parcellate according to temporal scale, paying more attention to, for example, recent and topical items than to an entire body of posts spread over many years. To illustrate the issue, tagging systems in large networks have a tendency to display very uniform and bland sets of tags. For example, over the past six years, over 80% of the most popular Flickr tags have stayed the same, despite a massively growing and presumably changing collection of people that use the system.
The reinforcement caused by existing tags combined with a stable set of generic interests in photography—the tag list includes many obvious ones such as “portrait,” “landscape,” and “black & white.” This means that the list remains very stable over time. Of the less than 20% of tags that changed in that period, most were related to large-scale shifts in interest caused by external factors, such as the season of the year and the popularity of movies. In 2005, for example, New Zealand was a much more popular tourist destination as a result of the Lord of the Rings films than it is today. Smaller groups, conversely, will create tag clouds of popular tags that change as the needs of the group evolve, reflecting change as it occurs. Small populations are more dynamic, and follow the same pattern of parcellation that we see in populations rapidly evolving in natural environments. This situation points again to the importance of parcellation: the smaller the subset, the more likely it is that relevant content will be discovered because the collective will be operating within a more precise context. Evolution happens fastest in small, isolated populations (Darwin, 1872; Calvin, 1997). Natural ecosystems exist in a highly variegated landscape that is frequently divided by borders which species find hard or impossible to traverse.
Relationship of Collectives with Groups, Sets, and Networks
Collectives may form in any size group or network. However, while a number of collectives have equal applicability whether they arise in groups, sets, or networks, some kinds are more relevant to one than the other. For example, in closed groups it is rarely a significant issue to identify the trustworthiness, reliability, and roles of members: it is part of the definition of a group that there will be leaders, that people will know or could come to know other members, and that shared norms and supportive behavior will arise. In sets, this is far from the case, and there are many collective applications concerned with discovering and establishing reputation, from eBay to Slashdot. Conversely, the fact that we do know more about the goals and needs of people within a group makes some kinds of collective application more effective in groups than in sets. For instance, simple rating systems, especially in large networks, are seldom effective in sets because the needs of people across the set vary widely. However, in a closed group, simple ratings can give an accurate and useful reflection of a group’s opinions and beliefs that is valuable within that closed context. In networks, the greatest value of collectives is in mining connections between people to identify relevance. Often, such recommendations are hybrids that also consider set attributes. Facebook’s EdgeRank, for example, takes into account professed interests and keywords extracted from content users post or read.
Evolution
Because the content of social sites largely comes from users, they are shifting spaces and, in many set and net forms, there can be a great deal of content of extremely variable quality. Especially once we start to employ collective processes to organize this information, a social site may be seen as a bottom-up organization, an ecology of multiple postings, discussions, videos, podcasts, and more, all competing with one another. As in natural evolution, there is replication with variation. Good ideas spread and become refined, changing to fit the perceived needs and interests of their viewers and participants. When designing a collective system it is therefore important to mindfully introduce selection pressure, prevent out-of-control Matthew Effects, and allow the crowd to sculpt the collective as efficiently as possible. This can be achieved in many ways, through active culling of poorly rated resources, the use of weighted lists through tag clouds or ordered search results, capturing successful paths, and selective or weighted display, among other things.
Diversity
For evolution to occur there must be sufficient diversity so that novel solutions have a chance to compete. The Matthew Effect may stifle diversity but, especially in groups, there is also the risk of groupthink setting in. Parcellation is one way to assist diversity, but it is equally important to create isthmuses between populations, to allow ideas and problems to seep beyond isolated islands. A little randomness can go a long way: it is worthwhile to introduce random results here and there that allow novel and seldom-used resources to be shown.
Constraint
Like natural systems, the evolution in a social site exists within a landscape. Some aspects of this landscape are comfortably familiar—spatial layouts, structural hierarchies, colors, and pages. Others have more to do with process—the algorithms, formal or informal rules, and temporal constraints imposed by the software. How we build the landscapes in which collectives form can have a massive impact on their effectiveness. Whenever we make a design decision regarding the structure or behavior of our software, we are shaping the landscape in which the ecosystem will develop: if we create oceans, we will get fish. If we build mountains, we will get mountain goats. Constraints can be very useful, allowing the designer to consider not just a broad and unspecific crowd but also one that is using the system with the intent to learn. For example, it may be a valid and helpful constraint to deliberately filter out certain forms of content from the results based on the target audience, or to create top-down categories that relate to anticipated interests. Active shaping can also be used to specify the kinds of activity the user is expected to engage in and make learning more purposeful. For example, using wording like “provide tags that describe the value of this resource to you as a learner” can help maintain a focus on pedagogical rather than less valuable tags.
Very few attempts to use collectives thus far have embedded more than a passing attempt at pedagogy. Collectives have been used as tools within a broader pedagogically driven context, applied within a constrained traditional group context, or relied on as simplistic models of human learning. There is a desperate need for programmers to design systems that use collectives with pedagogic purpose and an architecture built for learning, and to do so in the open world of sets and nets rather than the closed academic groups that most adaptive systems have been created for, if they are to reach their full potential. Google is a wonderful learning technology, but it is not designed explicitly for learning and often recommends resources that are not ideal for a learner’s needs.
Context
Particularly in educational settings, the broader context in which we use our social software can play a crucial role in determining the shape it takes. For a collective to have value, it should be derived from and used in a context that relates to current learning needs. As we demonstrated with the group of communication studies students who taught one another about chocolate, it is very easy for a collective to bend to a different set of needs and interests than those that are of most value. In some cases, context can be flexible. Collaborative filters, for example, typically base their recommendations on past interests, which may be poor predictors of value when context changes; but with small adaptations that allow a learner to deliberately specify interests at the time of searching, these filters can still be useful as long as others in the crowd have also specified similar contexts. Unless a system is extremely tightly focused, tags and/or pre-specified categories or topics can help to make a context clear. Tags are most useful when there are alternative means of ensuring that ambiguities will be minimal, for instance by limiting results to those of a specified sub-community through categorizations or special-purpose sites, or by making use of collaborative filtering mechanisms to identify people with similar needs and interests. Another way to make context more relevant is to consider recent items preferentially to overall items rated, increasing the chance that the results are relevant to the current context. This helps to deal with the problem that, once we have learned something, we rarely need to see other resources to help us learn it some more. In some systems, such as CoFIND (Dron, Mitchell, Boyne, et al., 2000), a decay weighting, proportional to relative activity and use of the system, is applied to older resources so that they disappear from the list of recommendations.
Scrutability
Many of the algorithms that generate collectives in cyberspace are trade secrets, jealously guarded by their owners. Outside of Google, Amazon, Facebook, and similar commercial organizations, and beyond the relatively small amount of published work they produce, we can only guess at the means they use to aggregate the wisdom of the crowd to shape our experiences. Where possible, the behavior of algorithms and the decisions that they make should be explicit, or at least be discoverable. If possible, users should be able to adjust the workings of algorithms and what they display to suit their changing needs. For an end user, however, it is not necessarily a bad thing that some of the details are kept secret. As Kay and Kummerfeld (2006) and Dron (2002) have discovered, while scrutability of algorithms and the ability to adjust weightings is much to be wished for, it increases the complexity for the end user, often with little or no benefit. One way to reduce that complexity is to provide templates, wizards, or a fixed range of settings that fit most needs. However, for those willing to make the effort to fine-tune the collective to their needs, it should be possible to access a wider range of settings as well. Amazon provides a good example in making use of broad-brush algorithms by default, but allowing individuals to provide explicit ratings to improve their recommendations, and to specify items to exclude from the pool used for recommendations. In principle, it is better to allow people to make adjustments at the time when they are needed, rather than as a general setting, but this again increases cognitive complexity.
Conclusion
This has been a long chapter that, though it has covered much ground, has barely scratched the surface of the teaching and learning benefits of using collectives. We think it is worthwhile to spend time on it because collectives are central to opening up cost-effective, responsive, socially enabled lifelong learning. We have seen that nets and sets afford rich and varied opportunities for learning but, unlike groups, they are not technological forms and thus do not provide the supporting processes that have evolved over hundreds of years of educational group use. Collectives have the potential to be organizers of learning, teaching presences that can guide and assist learners according to their needs, while allowing them to retain control of the learning process and engage in rich, social learning. Although we have had collectives since the dawn of human civilization, the scale of cyberspace and the potential of social software to generate new and more complex forms of collective makes it perhaps the most significant distinguishing feature between the new generation of online learning and what came before it.
The capacity to examine large-scale networks, and especially sets, allows us to catch glimpses of the group mind that were invisible before, and exploit crowd wisdom in new and pedagogically valuable ways. The dangers of mob stupidity should not be underestimated, however. In entrusting our learning to the crowd we are also entrusting it to the algorithms, both within the minds of the people in the crowd and in the software that aggregates and transforms their use. Careful design of collective applications for learning and mindful awareness of their strengths and weaknesses can go a long way to increasing their reliability, but it is also important for learners and teachers to develop collective literacy: to know what collectives are doing, how their learning experience is being shaped by them, and to know where the dangers lie. In chapter 9 we explore these and other dangers of social software in greater depth. | textbooks/socialsci/Education_and_Professional_Development/Teaching_Crowds_-_Learning_and_Social_Media_(Dron_and_Anderson)/07%3A_Learning_with_Collectives.txt |
STORIES FROM THE FIELD
In this chapter we discuss a range of examples of social systems used for learning that employ the different social forms we have been speaking of. These are not case studies. Rather, as the chapter title suggests, they are stories, exemplars that illustrate how our model can be used to illuminate different ways of teaching and learning. Beyond that, the stories provide concrete examples of some of the issues and concerns that emerge when attempting to implement a social system for learning, and some of the benefits of doing so.
Our focus will be on a small subset of the systems that we have actively played a part in developing or have created ourselves, each based on the Elgg social framework; a toolset for creating social software environments. This is partly because we know more about these systems than any others, but mainly because they have been informed by, and have informed, our evolving model of crowd based and social learning. While we have worked with and developed a wide range of other social software systems, these have been either small-scale or constrained by the limits of the tools.
Elgg has provided us with a full palette of possibilities to create a social software environment, and the relatively large-scale institutional uses of these systems have made it possible to examine a broad range of issues that arise. We will begin by briefly describing the context and some of our early attempts to both use existing tools and create our own, and the lessons learned from them. The bulk of this chapter will be concerned with the development and uses of Athabasca Landing, an Elgg-based system that we have been working on for the past three years. It is introduced with a discussion of two Elgg-based systems that we worked with prior to that, which taught us some valuable lessons in social software design and management. We will describe uses in both self-paced and paced distance education online courses, and ways that learning has happened outside formal courses, concluding with some observations on the knowledge bridges that have formed between different learning contexts, courses, and experiences.
Learning Management Systems
Like all pioneer online teachers, we have been exposed to and created courses using a variety of computer conferencing discussion boards, initially with static web pages and associated newsgroups, next with learning conferencing systems, and then using early and later versions of multi-functional learning management systems (LMS) or, as they are referred to in the UK, managed or virtual learning environments (MLEs, or VLEs). Indeed, in the late 1990s and early 2000s, author Dron was co-leader of a team that created such a system. It is thus from first-hand experience that we can assert the organizing metaphor of the LMS has always been the classroom. The vast majority of LMSs have been designed to automate and virtualize processes, pedagogies, methods, and procedures that already exist in institutions and business, and are thus quintessential group environments. Learners are typically assigned to groups by the institutional register, and are presented with a host of management, interaction, and content display tools. Notably, these groups are nearly always paced by the instructor and they march along in sync, typically for a semester of study.
LMS systems almost always feature strict role definitions wherein teachers, or in some cases only course designers, add various interaction modules and the content. An LMS is a very different technology to a teacher than it is to a student. Some kind of assignment drop box and resulting gradebook display serves to automate the reception, marking, and return of assignments, along with the transmission of records of student achievement and class participation to the registrar. With rare exceptions, anonymous participation is prohibited and students are forced to be personally responsible for their contributions and comments. The closed nature of the LMS course serves the group well, as it both defines who is a member of the group, and provides a degree of privacy and opportunity for growth of trust. We have often heard teachers decree that “what happens on the LMS stays on the LMS,” and despite the technical capacity for cutting, pasting, and reposting in the public domain, students generally accept the benefits of the closed online context.
The mirror of functionality between the campus classroom and the LMS context is both the system’s greatest strength and weakness. Teachers are presented with online equivalents of classroom activities—discussions, presentations, grade books, quizzes, and so on—that have long been institutionalized and become familiar social architectures of formal education. Thus, there is a relatively familiar learning path along which comfortable patterns can be transformed from faceto-face to online contexts—albeit with the added novelty of mediation and timeand place-shifting. However, this tight transposition from classroom to online also militates against the exploitation of new affordances, notably networks and sets that can be harnessed for social learning online. The closed group environment typically prohibits networks of learners, notably those from other sections of a program, alumni, and those with similar interests and learning needs, from contributing to the learning context. The strict privacy control prohibits sharing and commenting, and thus limits opportunities for social capital growth beyond the immediate group. Commonly, the pervasive enrolment control means that contributions from previous cohorts or knowledge resources built through time scales that extend beyond the course completion date are lost—in effect, every cohort starts the learning journey afresh, with no opportunity to benefit from the insights or learning of students who came before. It does not have to be that way, but given the surrounding organizational requirements and habits learned from centuries of face-to-face teaching processes, it is this path of least resistance that is usually taken.
We are not alone in thinking about, building, and testing systems that “go beyond the LMS,” and in the next sections we discuss our efforts to do so.
Elgg
In 2005, Dave Tosh and Ben Werdemuller von Elgg released a social software system based on their research into personal learning environments they called Elgg. The system acquired its name because Ben, whose family name is Elgg, ran a website with that name and that is where the first system first resided. Like many developed at that time, Elgg sought to provide a fairly complete social software solution, including blogs, social networking, groups, wikis, file sharing, social bookmarking, and content curation.
While the early 2000s saw many social software systems emerge, from its inception Elgg had some distinguishing features that separated it from the crowd, at least partly due to its evolution within the context of research into online learning. Chief among these was an extremely fine-grained, bottom-up set of access controls. There is no single privacy setting that meets the needs of all potential users. What for one user is an inherent right to free expression and an important way to build social capital through creation of an online identity is for others an invasion of privacy. Moreover, these settings must be dynamic, as one blog message may be thoughtfully restricted to a circle of tight friends, or for a teacher, while the next might be addressed to a network, and a fourth meant for reading across the Internet. Thus, each user (and notably not just the teacher) should be afforded the capacity to set the permissions level on everything they create (Figure 8.1).
Figure 8.1 Screenshot of Elgg’s fine-grained access controls.
Community@Brighton
Author Dron was previously employed at the University of Brighton, UK. It is a traditional campus-based university, centered in the city of Brighton & Hove but spread across many campuses in different communities around the south coast of England. After sporadic and independent efforts throughout the 1990s to provide a range of virtual learning environments, including one designed by the author, in the early 2000s a Blackboard-based learning management system was established that integrated with student record systems and other tools, known collectively as “studentcentral.” The course orientation of student central and the hierarchies of control that it embodied made it hard to adapt to learner-controlled methods of teaching, and made us painfully aware of the shortcomings of LMS systems. In response, Community@brighton was created by the university’s Learning Technologies Group in 2006. Based on the Elgg framework, it was an attempt to provide a richer online social space to bind this distributed community, embed learning beyond coursework and the university, build richer social networks, and perhaps most significantly, enable methods of teaching and learning that were difficult or impossible in the existing studentcentral system. In particular, it was meant to increase opportunities for learner participation and control (Stanier, 2010).
The system was set up so that everyone at the university was automatically given an account, making it possible to claim that it was, at the time, the world’s largest Higher Education-based social network, with some 36,000 registered users, growing over the years to nearly 100,000 members at the time of writing. A total of 79% of all those who might log in did so at some point, though few persisted and fewer contributed, with only 4.5% active after two years of operation (T. Franklin & Van Harmelen, 2007).
At first, growth was impressive and the system was used in a wide variety of situations, including academic, social, and support settings. A particularly powerful illustration of its value was its key role in the prevention of a student suicide (T. Franklin & Van Harmelen, 2007). Many innovative uses were made of the system, including some popular alternate reality games to introduce prospective students to the university community (Piatt, 2009), and some innovative pedagogical uses (Dron & Anderson, 2009).
Author Dron was an avid promoter of the system. He was one of the most active contributors to the site, providing presentations and exemplars to colleagues and brought in invited luminaries from the world of online learning to promote the ways it might be used to enhance learning. This, combined with the facts that most viewed the system in a frame within the studentcentral system and students were forced to subscribe to course groups, led to an increasing perception of the site as simply an extension of the existing, institutionally controlled learning management system. Its use polarized, and as alternatives like MySpace, and later, Facebook became more popular, the social and support uses diminished.
A further blow was dealt when, in 2008, the system was upgraded to a new and very different version of the Elgg software which, though more modern and functional in design than the original and far more architecturally elegant, stripped away some of its most important friendly, useful, and usable features, and worse, resulted in the loss of some of the content and presentation work that many had invested in, as well as rendering all existing hard-coded links to parts of the site unusable. Among elements that were lost were the ability to import RSS feeds from other sites, and the means to receive comments from users who weren’t logged in. This removed much of the beyond-the-university value of the site in one fell swoop. Other things that were lost included the Presentation Tool, a portfolio system created for the University of Brighton, which further reduced its value as a pedagogic device. Other small but important losses included the means to identify the access settings of particular posts, reducing faith and trust in the system, and a far less effective search tool, reducing the ability to find things across the site. Unwittingly, the new design also more clearly emphasized the institutional role of the system, with a large banner showing announcements and a feed widget displaying institutional announcements. It also began to lose its champions.
Though author Dron remained employed in a part-time capacity at the University of Brighton, he left its full-time employment in 2007 and his involvement, including his strong promotion of the system, diminished from then on. By the end of the first decade of 2010, a financial crisis was beginning to hit UK academia and resources that were at the best of times thin on the ground were increasingly channelled into other projects at the expense of the community@ brighton site. An enthusiastic and skilled learning technologies group still managed to continue with a small amount of development but, on the whole, the site entered maintenance mode.
Community@brighton persists today, but its future is in jeopardy, and currently it is in visible decline. For the past couple of years its main roles have been to provide an advertising bulletin board for students sharing or seeking accommodation, institutional announcements, and a diminishing amount of course-related use, typically involving student blogging—usually only engaged in under duress for course grading. As we write this, of 98,766 users, only three are logged in and a widget displaying “hot topics” is completely devoid of content. The Wire, its microblog (the equivalent of Twitter on an Elgg system) has not been used for 27 days and most posts to it are classified advertisements or requests to meet similar people in the area. We sincerely hope that the system may yet be saved, but the signs are ominous.1
Problems with community@brighton
There are many complex factors behind the slow demise of community@ brighton. We will identify some of the more salient issues.
Interaction Design
Elgg has never been noted for its innate usability. The modularity that gives it great flexibility can, without very complex theming, also lead to a fragmented and often confusing user experience. This is, to some extent, inevitable in a rich toolset without a clear center or focus, but it is also not helped by unintuitive metaphors, too much click-distance between related items, and inconsistent navigation and action tools. Use of terminology and tools like dashboards, profiles, and widgets confused people, even those familiar with the earlier version of the site, and without a compelling need to stay, drove them away
Change Management Concerns
We have already noted some of the problems that occurred when moving from one version of Elgg (0.9) to another (1.0). The enormous discontinuity between the two versions came at a time when the site was still finding its feet, and for many, the loss of data and formatting reduced trust and commitment to the site. Had the new version been a compelling improvement things might have settled down quickly, but the loss of functionality that its users had come to depend on, including lecturers who had incorporated it into their courses and those who had simply provided a little content, as well as large changes in terminology and implementation, made the move painful and abrupt. The then-developers of Elgg were widely criticized for the lack of support for existing users, and there was much ill-feeling in the community, despite recognition of the underpinning design’s excellence and acknowledgement of the value of the new direction the software had taken.
The old version of Elgg was poorly engineered but very well evolved, while the new version was very well engineered but untried, untested, and lacking in features. None of the many plugins that had been developed for the old version worked in the new one, disenfranchising many in the buoyant and distributed open source developer community so much that some who had invested large amounts of time and effort in developing for the platform felt betrayed. It was like the shift between piston engines and jet engines in the aircraft industry: for nearly twenty years, piston engines outperformed jet engines in nearly every measurable way, until jet engines became sophisticated enough to surpass their predecessors (Arthur, 2009). The new Elgg had immense promise, but in its first iterations, failed to deliver and moreover failed to facilitate a smooth transition from old to new.
Mismatched Social Forms
Elgg supports groups and nets well, and offers a few set-oriented tools like the Wire (its Twitter-like microblog) and tagging. However, this flexibility is a double edged sword. At any given moment, all of these social forms might be visible and only a click away. One might be in a group context and click a blog link, only to find oneself in a network context. Similarly, one might click a tag to find oneself in the context of a set. This fluidity is a strength in many ways, but also means that it is very hard to get a sense of place on an Elgg site. Furthermore, support for sets is not strong: many of the groups that were created on community@ brighton were actually more set-like than group-like. For example, author Dron’s particular favorite, “Grumpy Old Gits”—a group for people to complain about modern life—required users to become members in order to post a complaint, even though what drew them together was only a shared interest in whining about life. For such a set-oriented interest, there is no need for the trappings of group membership—the hierarchies, rules, and norms simply got in the way, and when the group owner lost interest, it became unsustainable.
Another mismatch in forms arose from the fact that academia is a highly discontinuous and hierarchical group form. Students are members of course groups they are periodically engaged with, but the groups have sharply delineated start dates, end dates, and demarcation lines between one course and the next. Furthermore, students and staff are members of faculties and schools that are largely separate from one another, with loose networks connecting them. There are strong boundaries between year groups, with little overlap among networks within them. These and other discontinuities mean that the fluid engagement found in a public social network like LinkedIn, Facebook, or MySpace takes on a more clustered form in academia. Students and staff frequently move between different networks, groups, and sets, often in predictable ways. While Elgg’s finegrained access controls are very useful for keeping these separate, it remains a single space viewed through different filters, and what is suitable for one context may not be suitable for all (Dron et al., 2011).
Lack of Ownership
Partially to compensate for its lack of center, community@brighton’s role as an institutional organ was made too prominent: announcements, banners, and embedding with the institutional LMS fill the main real estate of the site, and conspire to detract from a sense of individual ownership. Because a major point of the site is to provide personal control, anything detracting from that reduces the chances it will be enthusiastically used. A user-owned community site must embody a much different look and feel, and contain different content than the “official” website of an institution. Many users lost trust in the site after content, formatting, and functionality were taken away when Elgg was upgraded, further reducing their sense of control. When changes were made, it seemed that they were being inflicted from above, rather than emerging from the needs and interests of the site’s users.
Competition and Overlap on Many Sides
On the one hand, the institutional Blackboard LMS system has added tools such as wikis and blogs that, in limited group contexts, compete favourably with Elgg’s tools. If the purpose of an educational innovation is solely to share user-generated content within a closed group context, there are no great benefits from using a system that supports network- and set-oriented modes of engagement. On the other hand, the fact that the vast majority of students have Facebook or other social network accounts makes the need for social networking within the institution less compelling. This reality was compounded by the increased insularity introduced in the newer version of Elgg installed on the site. Another competitor in the form of Microsoft SharePoint, a staff-oriented tool that performs some similar social functions, has reduced the need for a tool that enhances sharing and social cohesion among staff.
Lack of Champions
Less than 5% of the site’s population contributed significant content and, among those, many were forced to do so because of course demands. This was a site with a very long tail. Over half of the 30,000 or so blog posts were created by author Dron, or more accurately, by a very buggy RSS tool provided with the earlier version of the site that imported the same posts repeatedly. Even so, Dron contributed some hundreds of unique posts over a period of several years. The loss of a single prolific poster, especially one with a strong evangelical mission to promote the site, was therefore a significant loss. While there were still a few champions after he left, there remained insufficient numbers of people with critical passion to sustain a sense of liveliness and topicality on the site.
Lack of Diversity
The flip side of the very long tail was that a small number of people appeared far more visible than the rest, thus establishing a culture and themes that would not interest everyone. We encounter this issue again later in this chapter when we discuss a site developed to deliberately address the problems raised here. Author Dron over-promoted the site as an educational tool for use in courses, which led to a stronger focus on educational issues and a consequent lack of emphasis on social and support uses. A number of students realized that the site could be a useful bulletin board to advertise rooms wanted and for lease, as it provided a free channel that would be seen by sufficient others to make it successful. The Matthew Effect took hold, driving greater and greater concentration of such uses, eventually leading the development team to design a plugin to support this main use.
Meanwhile, site administrators spotted value to be gained from being able to quickly and easily disseminate information, deliberately promoting such news to the most visible top corner of the site’s front page. Although many groups were created for a wide range of interests, clubs, societies, religions, and hobbies, they were overwhelmed by the dominant uses. In order for a generalized social system catering to a set of people to thrive, there must be sufficient reasons for users to be there, otherwise they are like the areas in cities that Jane Jacobs (1961) identifies as dangerously monocultural, such as city centers where people go to work and then leave when the day is done, making them dead and dangerous at night or on weekends.
Periodicity
Students come and go with predictable regularity, typically for three or four years at a time. Champions who created groups and sustained and nurtured them while they were students of the university left, and with their departure the groups they created faded away. Even though many group members and new students might still have had an interest in their topics, the fact that their owners were no longer present meant that newcomers were faced with the choice of joining a moribund group, or trying to start a new, competing one with a similar purpose. This was particularly problematic when the “groups” were really sets—collections of people with shared interests. The mismatch between the group form imposed by Elgg and the social form of the set it was trying to cater to led to fragmentation and dissolution.
Critical Mass
A social networking system only has value if it has many users. This circumstance creates a “cold start” problem, where users do not participate in a new networking system until a significant number of people are present. While enforced enrollment on the site provided a large population at the start, this served to highlight the limited amount of participation relative to the number of users. As user interest waned, it became self-reinforcing. It is not only important for there to be a lot of content, but on a social site, there must be visible and recent activity: the network effects of Metcalfe’s Law (1995) also works in reverse, with value decreasing proportionally to the square of the number of nodes in the network when nodes are removed, as MySpace found to its misfortune as its users left for Facebook in droves. The punctuated and time-limited nature of academic life, with ephemeral courses and fixed terms of engagement, meant that groups and networks experienced massive and catastrophic drops in membership every year, every semester, and sometimes in between, reinitializing the cold start problem once again. Only sets and groups, often devoid of active members and sometimes lacking owners, persisted. With ever-reduced resources being put in place to sustain and build these afresh, the site waned.
Me2u
At roughly the same time as community@brighton was being rolled out, author Anderson instigated another Elgg site at Athabasca University (AU) in Canada, named me2u. The reasons for installing the system were broadly similar to those informing community@brighton, though me2u’s ambitions were focused on a smaller community. While it did gain members from across AU over time, the site was mainly intended to encourage in-course, beyond-the-course, and open learning within a single academic centre, the Centre for Distance Education. At its peak, it had around 600 users. This smaller and more focused community developed into both a group-based support space and a means to support personal learning through portfolios and social networking within the community, including with its alumni. Its relatively small size meant that it was a mix of groups and tightly knit networks, and activity on the site remained fairly high because its use was required for a significant portion of its users at any one time as a coursework element. With a shared and cohesive vocabulary and purposes, the site appeared to be thriving, but it gained little from the benefits of network- and set-oriented modes of learning, and mostly kept a distinct disciplinary focus.
With far fewer resources than those available at the University of Brighton and without institutional backing, me2u remained a backwater research project but gained some avid users and supporters, driven particularly by Anderson’s enthusiastic endorsement of the system, bolstered by Dron on his arrival at AU in 2007. This was shortly before the new and ultimately improved version of Elgg that had caused so much disruption at the University of Brighton was released. Together, the authors of this book combined to build on me2u to achieve broader, more sweeping goals. The changes we planned were to encompass the whole university and beyond, to become a social learning space for formal and informal learning.
Athabasca Landing
The authors’ home institution, Athabasca University (AU), is unusual in many ways. It is an open university that accepts anyone regardless of qualifications, though a few senior and many graduate courses do require prerequisite knowledge or skills. It is almost entirely a distance institution, apart from a handful of courses, mainly at graduate level, with a small residential requirement, and another handful of courses that may be taken at partnered face-to-face colleges. One of its most distinctive features is that almost all of its undergraduate courses are self-paced: students can start a course in any month of the year and have six months to complete it, or up to twelve months with paid-for extensions. They can study and submit assignments and write exams at any time they wish. This provides great freedom of time, place, and pace, but traditionally does so at the cost of limited social interaction and virtually no opportunities for collaboration. Because the chances are very slim of two students with coincident timetables being at exactly the same point in the course at the same time, most interactions that occur in courses are limited to dialogue with tutors, or sporadic questions and answers on shared forums. This means that, though much high-quality learning goes on, the student experience can be lonely, disjointed, and lacking in some of the benefits of learning with others on a shared campus, where serendipitous encounters and the rich interactions of a community of scholars offers benefits beyond those of the formally taught classes. More than that, the focused nature of the dialogues that do occur ensures that it is very easy for gaps to emerge where one hard system does not perfectly interlock with another. Some students fill those gaps by asking questions of others and their tutors, but others see them as gulfs that are disincentives to continue. Dropout rates once a student has leapt the biggest gap of starting to submit work are quite low and compare very favorably with those of conventional universities, but before they ever submit a piece of work or start their course of study, these rates are very high.
The distance nature of the institution is not only limited to students. AU has traditionally followed a production model for most of its courses that evolved in the print and correspondence age of distance learning, with production teams including editors, learning designers, multimedia specialists, subject-matter experts, and a host of supporting roles developing well-engineered courses that are designed to be delivered more than taught. When courses are running, they are supported by teams of mostly part-time tutors and managed by a course coordinator who is often a member of permanent faculty. Faculty themselves are widely distributed geographically, most working from home and living in places spread across Canada, with concentrations in Edmonton and Calgary, and a very few at AU’s central headquarters, in the town of Athabasca, which is two hours’ drive from the nearest city. Not quite the middle of nowhere, but you can definitely see nowhere from there.
This means that the majority of interaction within the university is at a distance, and despite a plethora of communication technologies used to connect its staff, this makes it a victim of Moore’s theory of transactional distance (1993). There are many forms, processes, and procedures required to offset the relatively limited opportunities for dialogue when compared to a traditional institution. Manifold computer-based systems are used to disseminate information, and communicate to and between staff, but in the process, things fall between the gaps. However, communication tools can fill many of the gaps when used effectively. Email, Skype, telephone/teleconference, Adobe Connect, Moodle discussions, Zimbra groupware, and video conferencing facilities help to some extent, but each has limitations. Email is a powerful and effective technology than can be bent to almost any communication and information sharing task with sufficient effort, especially in conjunction with listserv technologies, but it takes a great deal of individual effort to manage effectively. It can be a scheduling system, a content sharing tool, an archive facility, a coursework submission tool, a voting tool, a personal networking tool, and a million other things, including its primary purpose as a communication tool, but each of these uses requires effort as well as organizational and interpretive skill on the part of sender and recipient. Email is also prone to error, inefficiency, and lack of reliability.
Moreover, email is a technology with the individual at its center, a tool that almost completely blurs boundaries between multiple groups, networks, and sets. Moodle has facilities for discussion and sharing, but its hierarchical, role-based approach and the fact that it mirrors the organizational structures of traditional courses and classrooms makes it inappropriate for more diverse uses. Furthermore, it provides limited personal control over disclosure and connection, especially in set and net social forms. Various forms of synchronous interaction are provided through Adobe Connect web-meeting software, Skype, and dedicated video conference facilities between AU sites, with consequent limited cooperative freedoms of time, pace and, in some cases, place. Zimbra provides a wide range of tools such as scheduling, chat, file sharing, and collaboration, but it is highly oriented toward group forms of interaction, and because of AU’s unusually transient and self-directed student population, is not available for students.
None of the tools that were available provided the kind of variegated, connected social space where many people could co-reside, selectively share, and experience a sense of what others were interested in and doing outside the restricted social roles in which they encountered them. In short, there was very limited support for networks and sets. This was especially problematic for interactions with students, who were at the bottom of the control chain in almost every kind of engagement.
Development of the Landing
In late 2009, with institutional, provincial, and federal funds, the authors helped to create a social site, a kind of virtual campus or learning commons for Athabasca University that was christened Athabasca Landing. Athabasca Landing was named after the original name of the town (a nineteenth-century landing on the Athabasca river) in which Athabasca University is based, but the site has, from the start, been commonly referred to as “The Landing,” which is not only shorter but also reflects both its role as a place to land and gather, and a space between other spaces.
The Landing was designed from the start as a place to connect, share, and communicate, to reflect and inform the ideas that we have expounded in this book and in our earlier work, building upon our earlier experiences and benefiting from what we had learned about advantages and pitfalls in Me2U and community@brighton. We intended the Landing to be a place that filled the gaps, both in social engagement and in process, left between our well-engineered, hard, and purpose-driven tools. There were several principles that we formulated early on and that continue to inform its development:
• Ownership and control: the site should be by and for the people that use it, who should have complete control of what they create, who they engage with, and who they share with, without significant hierarchies or top-down control. This made Elgg one of a small range of possible candidates as a platform for the site, as the vast majority of other systems embedded roles, access hierarchies, and top-down control in their design.
• Diversity: the site should be designed to cater to every need, avoiding an excessive emphasis on teaching activities.
• Sociability: social engagement and the ability to connect should be embedded everywhere throughout the site. Related to this was the notion that it should be a trustworthy and safe site, free from commercial motives, hidden agendas, advertising, or manipulation. Once again, Elgg presented itself as one of only a few alternatives that embedded social engagement everywhere, not just in confined spaces.
We discuss more fully some of the concerns, rationales, and discussion we had on these features in the following subsections.
Ownership and Control
We believe one of the reasons that community@brighton failed to reach its potential was that it was perceived as an extension of the institutional system. This perception was significantly reinforced by its most prominent use as a teaching tool: in effect, it became an extension of the classroom for many students, or was viewed as a communications tool for university administration despite its many social networking features and tools to create personal learning environments and bottom-up engagements. This perception was further reinforced by its tight integration with the university’s learning management system, a design that emphasized announcements rather than community-created content on its front page, and students’ forced membership in course-related groups. Furthermore, all students were automatically enrolled in the system when they registered with the university; they were not given a choice as to whether they were members or not. This immediately took away some of the benefits of deliberate group joining noted by Kittur, Pendleton, and Kraut (2009), and may have reduced motivation to participate as a result.
All of our design decisions about the Landing were based on the principle that its users are its owners. Before even starting to design the site, we enlisted a diverse group of over 50 AU staff and students to choose the tools and technologies to use, and to define its purpose. When the site was opened, we invited these people to join a set of individuals to guide the development of the site: they formed a group we christened “Friends of the Landing.” This group has thrived—at the time of writing, it had 97 members: we will report on some of the learning that has occurred within it later in this chapter.
Elgg was not the only possible choice of infrastructure for this new site. When choosing a technology from 50 possible systems that provided the kind of tools we needed such as blogs, bookmarks, wikis, and file sharing, once we had weeded out commercial systems (we needed the flexibility of open source), and those that were hosted elsewhere (there was a need for privacy, in addition to flexibility and long-term ownership), the choice was narrowed down to two: Elgg and Mahara. We were very impressed with a number of content management and blogging systems, such as Drupal, Plone, Wordpress, Joomla, and LifeRay, and many involved in the project argued for extending the existing Moodle learning management system to meet our needs. However, all of these candidates embedded role-based or access hierarchies that meant end users would not be in complete control of their content, or if they were, ensuring they could exercise the rights we wished to give them without impinging on those of others would be an unsustainable management burden.
Mahara is a tool explicitly based on Elgg that specializes in the production of e-portfolios. While it is very good in this role, incorporating social networking and several tools such as blogs, file sharing, and wikis, and it was a highly polished product, its other features were decidedly lacking when compared to Elgg, and the effort required to add new features would be considerably greater. Both were extendible, but Elgg was vastly superior at that time: Mahara had a small handful of plugins compared to many hundreds available for Elgg. Elgg’s architecture had been completely reworked shortly before we were choosing systems in order to make it more of a social software construction kit than an extendible system, and so, as it was our intention to mold the system as closely as possible to the social forms we had identified and principles of design we had established, the final choice of the Elgg system was almost unanimous. We note, though, that the psychological lock-in to a system we were familiar with through development of Me2u may have influenced our decision. There were practical benefits to leveraging existing knowledge and skill sets, even though Elgg itself had undergone major revision.
Context Switching
Academic life for both students and faculty is a disjointed affair, with frequent and abrupt shifts between different social contexts: classes, courses, research areas, departments, terms, and so on, demarcate borders between areas of interest and sets, networks, and groups of people. Access permissions and the functionality of groups, networks, and collections allow users to both selectively reveal different things to different people and filter what they see according to various needs. We have built a number of tools that make switching between contexts more explicit and intentional by allowing people to place highly configurable widgets on different tabbed spaces for different purposes:
• Super-widgets: Widgets are small objects that can be placed on the screen to display (but usually not add to) different kinds of content—for instance, to view blog posts, files, recent activity, groups we belong to, and so on. Users of widgets can also access different social sites and services such as Twitter, news feeds from other sites, et cetera. Widgets can be placed in groups, on personal profiles, and on the user dashboard (a learning space used to organize and personalize an individual’s view of the site), and serve to alert users about fresh content, upcoming events, or important addresses. We have made extensive modifications to the widget functionality provided by Elgg so that users have far greater control over what they show, allowing filtering according to group, network, or set (through tags), date range, individually selected posts, and more. We added sorting and display options that make it easy to configure a group, profile, or dashboard according to individual needs and contexts.
• • Tabbed profiles and dashboards: to support the super-widgets, we have extended the single-page views of individual and group profiles as well as dashboards to allow multiple panels for different contexts (see Figure 8.2 below). People can create tabs for particular courses, interests, and intentions, each filled with different widgets showing different content. This allows individuals to both switch between contexts—for example, to separate social from academic interests—and present different facets of themselves or their groups to others. Because each tab has the same sets of permissions applied to it as all other objects on the site, people can display one aspect of themselves to their friends, another to their teachers, and a third to the world at large. Similarly, research groups can have a tab that supports internal working processes and another to display their outputs to the world.
Figure 8.2 Profile page on the AU Landing, showing widgets, tabs, and the “Explore the Landing” menu.
A Soft Space Made of Hard Pieces
The Landing is highly componentized, both in architectural terms (Elgg has a very small core and gains almost all of its functionality from plugins) and in interaction design. For an end user, Elgg provides a set of tools that can be assembled, aggregated, reassembled, and integrated in an infinite number of ways. Creating a different use for the Landing is simply a question of assembling and configuring components to suit specific needs. The intention is to escape the prescriptiveness of a role-based hierarchical system such as an LMS, but to reduce the difficulties of building a system from the ground up. Widgets, tools such as blogs, wikis, bookmarks, and files, groups, tabbed groups, and individual profiles can be combined in many ways to meet diverse needs. The balance between ease of use and flexibility is difficult to achieve, and we are still some way from getting the balance right for everyone; indeed, this may be a quixotic search. One of the most frequently voiced complaints about the Landing is that it is complex, confusing, and hard to navigate. To deal with this, we are currently adapting a range of strategies, including story-sharing, social menu organization, and community-led design.
Sharing of Stories and Ideas
The help system of the Landing is constructed using the wiki tools available on the site, and we have attempted to encourage users to share their stories and suggestions within this context. However, few have done so, perhaps because the wiki is available in a Help group context, whereas it is more clearly and obviously a set-oriented activity: it would be unusual to feel a sense of membership for a help system unless one was explicitly recruited to it. Unwittingly, we have made use of the wrong social form to provide help within the system. However, a few Landing members have independently begun to share their stories and insights. A student, for instance, started “the Unofficial Landing Podcast” and interviewed other students, Landing founders, and even the AU president on topics of interest to AU members. A member of staff created a podcasting group in which he and a few others present ideas, links, and tutorials on podcasting though, once again, the group form acts as a barrier to entry, and means it remains primarily the domain of a single enthusiastic user. Another student started a video cast series that explored similar themes to the Unofficial Landing Podcast, but has since left the university. As we saw with the University of Brighton, individuals’ sporadic and time-limited involvement in the community causes problems of continuity and acts as a barrier to ongoing engagement.
Social Organization of Menus
Instead of the default tools-oriented menus natively provided by Elgg, we have reorganized the structure of the site in accordance with our model of sets, nets, and groups. The menus we provide are:
• You: profiles, dashboard, settings, options to view one’s own activity and content, and to post new content.
• Your network: options to see what people one is following are doing, as well as to discover and connect with new people.
• Groups: options to see one’s groups, the activity within them, and to join new groups.
• Explore the Landing: options to focus on specific tags and keywords relating to topics of interest (sets).
These explicit perspectives help to control the kind of interactions people have with others on the site. Those who wish only to engage in group contexts should be less distracted by network interactions, those who are interested in their connections with others should find them more easily, and those with specific interests should find it simple to discover and explore subjects and topics that matter to them. However, once users follow a link, they may soon find themselves in different contexts from those where they began, and this reality limits the extent to which the social organization of menus achieve the desired goals.
For example, when exploring the site-wide categories, as soon as an individual clicks on a specific post, they are immediately flung into whatever social context it was created in, often a group or a network, which requires a subtle transformation of perspective to understand the relationships between what they are viewing and what else it relates to. This remains an ongoing design problem.
Community-led Design
We are also engaged in a constant cycle of refinement that incorporates feedback and suggestions from the Friends of the Landing and others on the site. We have added an instant feedback link on every page using AJAX, so individuals can make comments without leaving the context in which their issue arose, or instigated discussions to which many have contributed. The ideas we have gathered as a result are beyond our technical capacity to deal with in a timely manner, but we are making progress all the same.
We also realize that even within closed communities, users may purposively or inadvertently post content that others find objectionable or unlawful. Thus, we have a link on the footer of each page where users can report content that they feel violates norms or laws. Fortunately as administrators we have yet to see use of this link, but there has been controversy and discussion about a number of posts (we will discuss this later in the chapter).
The Friends of the Landing have monthly or bi-monthly meetings via webmeeting tools, and we have evolved a process of round-robin discussion where people share their experiences, concerns, and interests. This is not only a useful source of feedback for design purposes but also a means of sharing stories and ideas that spread through the community.
Diversity
Both Me2U and community@brighton became, for different reasons, monocultures. Me2U’s limited user base, largely drawn from a single, highly focused academic centre and just a few courses, was never evolved into a general purpose environment. The combination of academic focus, lack of ownership, and the exigencies of being a face-to-face university where, though campuses were distributed, most people who needed to meet in person did so, led to community@ brighton eventually serving only three main purposes: teaching, announcements, and advertisements attempting to fill shared rooms in houses. While many other communities were created and some flourished for a little while, there were few reasons to visit the site outside of those specific needs, and so visits tended to be brief and task-focused.
As a starting point, we expended a fair amount of effort on migrating as much of the content and users from the older Me2U site as possible because it was being actively used in teaching and we could not sustain two social sites at once. This had a number of repercussions, not the least of which was an extremely strong emphasis on distance learning interests right from the start. As we observed in the last chapter, the impact of path dependencies and the Matthew Effect meant that we were starting in a weak position from which to encourage diversity. We adopted a number of mitigating strategies in an attempt to swing the balance away from this focus, actively recruiting our assorted group of Landing Friends to contribute from their diverse fields of interest, running events and giving talks to encourage people from across the university to engage, and deliberately shaping the environment—for example, we removed a tag cloud at the start that showed virtually nothing but education-related tags. Despite this positive discrimination and much work to encourage diversity over the past three years, the effects of this early bias continue to be felt. On the bright side, because AU is a distance university, many people who are not actually studying distance education do take an interest in and benefit from the rather large amount of content and interaction on this subject.
Sociability
One of the reasons for choosing Elgg over alternatives such as the institutional Moodle site was that sociability was built into every part of the system. Unless people choose otherwise, the default behavior for every object created—be it a file, a photo, a blog post, a wiki, a bookmark or a calendar event—is to enable comments and discussion to evolve around it. Whenever such commentary does occur, the individual who made it is shown in avatar form, with a hyperlink that allows people to follow them.
We deliberately changed the default Elgg vocabulary of “friends” to “followers,” partly because that is a more accurate description of the one-way relationships enabled by Elgg. I do not necessarily “follow you” if you follow me, unlike the reciprocal relationship of Facebook friends. We mainly did this because we did not wish to suggest a specific kind of relationship when one person connected with another. In many cases, we knew that people would be following the activity of teachers, for example, and using the relationship as a means of sharing work with them. We also recognized that many people would be sharing work with and following the work of colleagues, co-researchers, and others who may not accurately be described as “friends.” Elgg supports a feature known internally as “collections” that allows one to group those one is following into sets. One can create collections labeled with anything, such as “friends,” “co-workers,” “COMP602,” and so on. We improved this functionality to make it easy to create such sets at the time of following, in a manner almost identical to that which was later used by Google+ when it introduced Circles. Because of the subsequent popularity of Google+, we renamed “collections” as “circles” in order to make them easier to recognize.
We built a tool to enable comments on public posts from people who were not logged in, to support beyond-the-campus interactions, and extend the site beyond a closed, group-like community to broader sets and nets around the world. To make it easier to find people, we provided a tool that identifies followers of people one follows and fellow group members.
The Social Shape of the Landing
The Landing supports social networking functionality, but is not exclusively a social network like Facebook, LinkedIn, or Bebo. As Chris Anderson puts it, social networking is a feature, not a destination (2007). Many of the uses of the Landing are group-oriented, but the fact that the technical form of a group has been employed does not always mean that the social form is appropriate: many groups are simply used to collect a set of resources around a single topic. For example, several students have created groups to amalgamate individual portfolios or research findings, while other groups have been created as a focus for areas of interest, such as the “Zombie Research Group” or “First World Problems”; a staff member has created a site to share photos of convocation events. Because of this, we have built a plugin explicitly intended to support sets that we call the Pinboard. Pinboards are technically similar to groups in the functions they provide but do not have any notion of explicit membership: essentially, they are containers for objects akin to boards on Pinterest or Learni.st. Unfortunately, though our Pinboard is a powerful plugin that has been taken up by many other Elgg-based sites around the world, it is far from easy to use and has not been as widely adopted within our own community as we had hoped.
Default Access
The capability of Elgg to provide fine-grained access control has worked well. However, thanks to the power of the default (Shah & Sandvig, 2005) permission setting has proven to be a powerful determinant of user choice. In the very early days, we hoped to attract outside readers and thus left the default permission to “public.” However, we soon found that many users had left this as their default, and a few were not pleased with the exposure on Google search engines that resulted. We thus changed to default to “logged in users” for general posts and to the members of a group, for content posted within groups, leaving it open for the user to set more or less restrictive permissions if desired.
A second useful feature of Elgg is the capacity to open or close membership to the site. We have chosen to allow login by any member of the university community (teachers, students, staff, and alumni) and have integrated the single sign-on used for other university systems. Although we have manually added a few guests working on research projects and so on, this has meant that potential contributors from outside the Athabasca network and set have been denied the opportunity to participate. We did, however, build in a moderated comment tool for outsiders to add comments to posts that are explicitly made public after appropriate moderation by the poster, to prevent spam comments. Thus we have described our Elgg installation as a “walled garden with windows.” Membership in the site is restricted, but any member can open a window through which their contributions can be viewed and commented on from outside.
Using Athabasca Landing
At the time of writing, the Landing has more than 5,000 users who have, between them, created over 20,000 resources, including around 8,000 blog posts, over 6,000 file uploads, and thousands of other objects like bookmarks, wikis, photos, polls and events, along with countless comments and annotations of other posts. There are nearly 400 groups. It is hard to analyze the precise purpose of all of these without interviewing the individuals who create and use them, and groups have a tendency to evade neat categorization: for example, groups that are purportedly related to a course may turn out to support a specific research student or project or, in a couple of cases, students may have set up their own versions of official course groups. Such is the bottom-up nature of the Landing. Bearing this in mind, we have attempted to classify the kinds of uses, using an iterative coding process. Relying on the descriptions provided and some informed guesswork, for instance, by identifying course names and numbers or recognizing specific organizational groups, we see the following breakdown:
Research-related: 16%
Personal: 5%
AU business (e.g., committees and working groups): 15%
Academic center or faculty: 5%
Non-formal learning (e.g., support groups for computing or hobbies): 9%
Course-related (e.g., study groups, project groups): 21%
Course administration (e.g., development or tutor groups): 2%
Course (formal): 18%
Social (e.g., local meetups): 2%
Subject area: 2%
Landing-related (groups supporting research, operations, etc. in the Landing): 4%
Experimental (set up and forgotten): 1%
While there is still plenty of room for increased diversity and an understandably large emphasis on things that are related to teaching and learning, we have achieved some success in making the site sufficiently diverse so that there is more than one reason for someone to visit the site. Among the biggest of these is in formal course use. In the following section, we provide a few examples of the way that the Landing is used to support and enhance formal courses.
The Landing in Paced Courses
Information Technology (COMP 607)
Ethical, Legal, and Social Issues in Information Technology (COMP 607) is a graduate- level course provided to students in a distance-taught MSc in Information Systems at AU. The previous iteration of the course was based around a book, with weekly discussion forums centered on different chapters. It was a classic group-based course, with tutor-guided discussions enabled on Moodle, shared study of a single text, a set of short essays, marks given for participation, and a final examination, taken at home. Because study was paced, the group form was an appropriate approach but, as all students were working in the IT industry and had rich experiences to share, there were opportunities to draw more broadly from their own knowledge and gain from “teachback” (Pask, 1976) in a more networked manner. Furthermore, each iteration of the course had started with a blank slate, a newly replicated version of the original Moodle course, so none of the learning and knowledge building of previous iterations carried forward to new cohorts.
For the new revision of the course, a Moodle course was created with a broad and flexible course outline and a few selected readings, and the Landing was used as the platform where all course activities occurred. A group (defined in Elgg as a container for content and interaction with members) for the course was created. This automatically opened up the opportunity for a persistent record of student activity that would remain for the next cohorts to draw upon. The group would therefore naturally draw in more of the set, and open up opportunities for a network to develop, if previous group members remained in the group (membership after the course being voluntary).
The course was structured around a variety of social processes, a mix of debate formats such as fishbowls, team debates, Oxford-style debates, and small group discussions, and combinatorial cooperative strategies such as sharing bookmarks and contributing to an “encyclopedia.” Each week revolved around a topic that, after some introductory exercises in ethical and moral debate, explicitly focused on topics in the news. This emphasis on events within a few weeks of the course beginning ensures that students learn from previous cohorts but do not copy them. Basic arguments and viewpoints can and do repeat from one cohort to the next, but the content is always different and draws from a broader network.
Having run through two iterations, the course has been successful from the point of view of the experience and outcomes. Comments from students were positive: “I was impressed by the level of intellectually stimulating debate. It certainly twisted my brain in a new direction and I am among a great group of folks!” and “I’ve enjoyed the discussions and the debates, and have learned a lot from people with different viewpoints,” and “it’s nice to see how many people have contributed to the discussions, almost everybody answering a different question.” However, the positive benefits were largely the result of pedagogical design that could have been achieved within a Moodle course using conventional group tools. The set and net benefits were thin on the ground, but some were seen. The second iteration of the course benefited notably from access to the work done by the previous cohort, especially when it came to the ongoing development of the “encyclopedia,” and there were two interjections from previous course members, which suggests value in ongoing networked connection with a course. Some benefits were seen from references to other posts by people from other faculties on the site, and one staff member from a different department contributed a couple of comments on open posts. However, the fact that the group was closed militated against deep involvement from across the set/net of the rest of the site, despite many of the students posting their work for all logged-in users and, in a couple of cases, public viewing.
All of this is, in retrospect, an inevitable consequence of following a traditional, closed-group process and the highly task-oriented instrumental approach used by most students accustomed to this mode of teaching. A major benefit of using the Landing, however, is that it is within the power of the teacher to implement change. In the next iteration of the course, it will no longer be a closed group. While assessment will, as ever, be limited to the paid-up members of the course, the group on the Landing will be open to anyone wishing to join. We hope that this will bring about a more interesting dynamic and encourage engagement from others beyond the course.
Planning and Management in Distance Education and Training (MDE605)
This semester-length course operates in paced mode and is compulsory for students in a distance Masters of Education program. The course has run for a number of years in Moodle, and the major assignments revolve around iterative development of extensive business and evaluation plans. The Moodle environment was used to store content and for the assignment dropbox, but all interaction took place in a closed Landing group limited to registered students in the course, though additional students were added each year. Students could choose to remain in the group and receive notifications of activities in subsequent years and add comments, or resign from the group upon completion. Thus, unlike typical LMS systems, students were able to review contributions, blogs, comments, wiki pages, and most importantly postings of assignments—draft iterations of business plans from former students. In addition, students were encouraged to post links to useful resources they found on the Net, and were required to post a summary blog in which they reflected on their contributions and experience in the Landing context. The course ran for three years, and thus a considerable “archive” accumulated. Students could choose to share their assignments with or without the marks and audio marking annotations inserted by the instructor (Terry Anderson). Interestingly, some chose to address deficiencies identified before posting assignments, while others chose to leave them.
Almost all the students expressed enthusiastic appreciation for the archive, especially the submitted and marked assignments. In a follow-up research study, students made comments, such as, “I had no idea how to approach this assignment until I saw what other students had done—it was great!” However, a minority were uncomfortable with this exposure to others’ work, and stated, “I came to learn this material myself, looking at the work of others would be cheating.” It strikes us that the latter attitude inhibits the great affordance of the Net: to search for and build upon the contributions of others, a process which has defined scientific publication and knowledge growth for centuries.
Also of interest was the decision made by the next teacher of MDE605, after Anderson moved to other teaching assignments, to discontinue using the Landing and revert to the standard Moodle presentation. This may illustrate the challenges of implementing change and the conservative nature of many academic institutions. Or perhaps it only illustrates the need for enthusiastic early adopters to propel exploratory use of new technologies.
The Landing in a Self-paced Course
Athabasca University’s undergraduate courses are all based on individual study. Students enroll any month of the year, are assigned tutors, and then have six months to complete the course as it suits them. While catering well to many of the cooperative freedoms, it has historically been almost impossible to gain the benefits of group processes (collaborative or cooperative learning) in this self paced context. Most people take courses in isolation, with occasional contact with tutors via email or telephone, and formal points of contact established for feedback on regular assignments.
Over the past decade or so, course designers and instructors have increasingly used learning management systems, particularly the centrally supported Moodle system, and many courses have incorporated group forums as an attempt to increase a sense of social presence and reduce the loneliness of the long distance learner. To some extent this has worked, inasmuch as forums have become places where students can ask questions about the course, and on the whole, get answers, sometimes from tutors and sometimes from other students. However, the group discussion forums are, as the name implies, designed for groups, whereas these independent learners are, in most respects a set, only bound together by the characteristic of taking the same course at the same time. Typically the forums are little used and often not effectively moderated by tutors, who are not paid for this “extra” work. Unlike a group, there is no shared collaborative purpose: everyone is doing his or her own thing at his or her own time, without dependencies on other people. Most of the time, beyond a name or occasional shared profile (optional, of course), the rest of the group remains anonymous, part of an undifferentiated crowd.
COMP 266
Introduction to Web Programming (COMP 266) is a course in HTML, JavaScript, and related technologies that had been running for a number of years as a textbook wraparound course. A study guide, available on a Moodle site, provided guidance on readings and exercises in the textbook. Moodle was used to provide a set of self-assessment multiple choice questions, a means of submitting the four assessment exercises for the course, and a threaded forum. The forum was almost exclusively used to get answers to specific questions and, as a result, over a period of years became a poorly organized but well-used repository of knowledge for students seeking information. Most students contributed nothing to the forum however, and for many, their only human interaction was with the tutor in the form of feedback on assignments. At the end of the course, students sat an exam at one of many exam centers around the world either run by AU or franchised out to other institutions. The course appealed to a few, but there were many complaints and many who registered but failed to complete the course.
In the course’s revision, author Dron applied many of the ideas and principles expounded in this book. While there were clearly few, if any, opportunities to make use of group-based learning, the natural set orientation of the self-paced course mode of delivery suggested a range of possible approaches. There were also opportunities to foster the formation of networks and, at least in principle, to use collectives to help harness the wisdom of the crowd.
Figure 8.3 COMP 266 group profile page on the Landing.
The course makes use of Moodle to provide fixed content, a place for students to submit work, and self-assessment exercises. Students are required to follow a guided and scaffolded process to build a single website that gains in sophistication as the course progresses, starting with a design unit the rest of the work is based upon, then working through HTML, CSS, JavaScript, library re-use, and AJAX integration. Students choose what the site is about, what functions it will have, and everything else about it. There are stop-points throughout where tutors give feedback but no grades, to ensure that students stay engaged and do not take on too much or too little to succeed. The only assessment for the course is a single portfolio: students are given a grade for each intended learning outcome rather than on work performed for particular units. Throughout, students are required to submit all the work they do via a closed group on the Landing (see Figure 8.3 below) in a learning diary that contains reflections, design artifacts, code, and so on, as well as links to their publicly visible sites. Students are permitted to set any permissions that they like for this work, as long as the tutor can access it as well. Many limit access to the group (the default), almost as many allow access to all logged-in users (members of the larger Athabasca community), and a few provide access to the whole world. A very limited number restrict access to only their tutor. Because grades are given for learning outcomes rather than specific pieces of work, students may submit any evidence they like of having met them, including annotations of links shared with others, help given to others, and general commentary in their learning diaries. This helps to align marks with incentives to participate in the set, without enforcing sociability on those who do not wish it. Those who do not want to engage or who wish to remain peripheral participants can be successful simply by creating a good website and set of reflections, but there are still dividends to be had from sharing.
From the point of view of the students, the course has been a huge success by allowing a full range of cooperative freedoms. However, this is again the result of pedagogical design, although unlike our previous examples, such a pedagogy would have been difficult or impossible without the ability to selectively share anything with anyone. Many students comment on the value of being able to see what others are doing and thinking, and benefit from the amplification effects of tutor and student feedback on work posted. Notably, students explicitly mention that they are inspired by what others have done, and are motivated to excel by the fact that others are not just looking at their work but displaying an active interest in it. There is a great deal of camaraderie in the course, with some students referring to their fellow students as a “cohort,” despite the fact that it is nothing like one from an organizational perspective—by and large, this is a set, with cooperative sharing and mostly unsustained dialogue forming the bulk of social interaction.
Nevertheless, the overwhelming amount of communication has caused problems for many—we do not yet have powerful collective tools to provide the necessary filtering for this, though a collaborative filter is in testing as we write this book. This is exacerbated by a poor design choice to require students to share a single course blog. Although this does have benefits in making everything visible, which was the intention, there is simply too much to pay attention to. In other courses author Dron has created on the Landing, students either share their own personal blogs or make use of hierarchically structured wikis so that their work is still visible but separate from the rest. The wiki approach is more successful in a group context and still provides the benefits of visibility, but the personal blog approach has value in extending the course into the broader network. Beyond these issues, the interface is often seen as unintuitive, at least partly because of the ongoing confusion, despite our best efforts, of group, net, and set social forms, as we described in an earlier paper on a similar course run at the University of Brighton (Dron & Anderson, 2009).
Informal Learning on the Landing
While course-based uses of the Landing have shown the potential for social tools to expand the number of pedagogies we use and improve the motivation and engagement of learners, even beyond the course period itself, one of our biggest hopes for the site was that it would support learning outside of courses, to help build a richer learning community and foster forms of engagement that navigated the formal boundaries.
The Friends of the Landing
In the formal constitution of the Landing, we deliberately avoided the usual nomenclature of “steering committee” to describe the people who would help guide its development; opting instead for a “steering network” we christened it “Friends of the Landing.” We wanted it to be an informal and inclusive collection of people who engaged as based on interest and propinquity rather than as a result of the formal group edicts and processes that guide a typical closed-group committee. Anyone who uses the Landing can be a Friend of the Landing, and like most friends, the commitment does not require them to follow schedules, meet quorum requirements, or adhere to established rules of conduct. Though we described it as a network and implemented it in an Elgg group, this is in fact more akin to a set, bound together by a shared interest in how the Landing develops. The Elgg group is simply a container where social objects of interest like discussions, links, minutes, wiki pages, and blog posts are shared. Almost all activity that goes on within the group is provided by people who set access rights to all logged-in users rather than to the group alone, demonstrating and reflecting its set-like nature. Similarly, though the vast majority of set members have chipped in from time to time, the fact that engagement is driven by current interest rather than commitment to a group, its members, and norms, strongly suggests that the set mode of engagement dominates this collection of people.
However, there are more complex patterns of social engagement overlaid on the group that make it a far from equal set. While some are unknown to others, many are networked with one another in different contexts, sharing the same groups or working together as staff members, friends, and fellow students. As in any formal education institution, there are power relationships in which AU staff are recognized as a different set from AU students, as well as more complex divisions within the staff that contribute both on academic and organizational lines: faculty, learning designers, administrators, and others exist in formal and informal juxtapositions within an institutional context. Notably, the core development team has a particularly strong role in an informal hierarchy, as the majority of decisions and suggestions made within the set are channelled through them, interpreted, and filtered, before being implemented on the site. Adding to those inequalities, people have to actually join the group to fully engage with all the tools: while most allow anyone with the rights to see them to comment, wikis and discussion forums are, at the time of writing, a peculiar subset of Elgg technologies that can be seen but not engaged with by people outside a group. We will be changing this in our new “set” developments, but unwanted mismatches between the set and various group and network social forms occur, since people deliberately have to become members because of the group’s technological form, and a group has owners.
There are synchronous meetings held monthly via an Adobe Connect webmeeting system to which all Friends of the Landing and, emphasizing the set-based nature of the engagement, anyone using the Landing are invited, but a lot of involvement takes place via the Elgg group itself. In addition to members of the core development team, the group contains faculty, support staff, administrators, graduate and undergraduate students, interested bystanders, alumni, and executives of the university. The proportions are not determined by any formal constitution—those who are members are those who have self-selected to join. Sometimes, people contribute from beyond the group of members, as virtually all communications within the group are shared with all logged-in users of the site and a few are available to the whole world.
The other collection of people intimately involved with Landing development is the Landing Operations Group. Unlike the Friends, this is a true group in most senses of the word. It has a distinct mission and purpose, is closed to non-members, involves strong social ties, and is hierarchically organized: there are three co-leaders, the authors of this book and George Siemens. Most of its online activities take place in its closed Elgg group but it has regular weekly meetings in person/via teleconference, and also uses other tools such as the Bugzilla software management tool to manage interactions.
Bridges and Isthmuses
As well as the relatively formal uses of the Landing that we have reported on so far, threads of knowledge weave back and forth across the site, breaking out of course boundaries, sets, and formal groups and spreading across the network. To help foster this diversity, we have designed the site with a number of tools that make it likely for one to encounter the posts of others, with related content displayed in several places, a configurable activity river that shows posts from across the site and for specified groups and circles of people one follows, a random content widget that displays posts from across the site, and more. The Landing is a thriving community where comments are very common, including those that come from beyond the walled garden, and there is diverse activity and a strong sense of awareness of what others are doing. Although we have (yet) to implement realtime chat capabilities, several people have commented on the sense of reassurance and value gained from seeing that others are online, displayed via a counter on the site’s front page. There are typically 20–30 people logged in and identified as active (i.e., having loaded a page within the past two minutes) at any one time during the day. As I write this at 2 a.m. on a Sunday morning, even allowing for time zones (from 1 to 4 a.m. across most of Canada), I see that there are seven other people logged into the system. I have no idea who they are, but the collective thus plays a role in giving me a sense of relatedness with others.
Issues, Concerns, and Wicked Problems
The Landing is a work in progress, and there is a long way to go before we can trumpet its success. We outline some of the remaining issues in the following subsections.
Punctuated and Time-limited Engagement
Some of the problems that beset community@brighton remain on the Landing, and in some cases are magnified in the Athabasca University context. In particular, the fact that many students are visitors taking a single course who are not even enrolled in a program means that the punctuated nature of engagement that played a strong role in community@brighton’s dynamics is an even greater problem at AU. It is very hard for a student who is taking a single course for less than six months, with little social engagement in most cases, to feel any sense of ownership or belonging in a transient community. While we make it clear that, unlike access to most other university services, Landing accounts persist for the foreseeable future even when students have left, for transient students there are few compelling reasons to join or remain in the community at AU. Given the rarity of in-course communication between students apart from those created on and for the Landing, there are seldom networks of people to make it worthwhile to remain on the site; even when they form, close personal friendships or professional relationships are more likely to be maintained on a purpose-built and heavily populated site like Facebook or LinkedIn.
Lack of Diversity
For all our efforts to foster diversity, the strong Matthew Effect caused by an early influx of distance education students, combined with the fact that the most persistent users of the site are staff who have an inevitable interest in distance education, means that distance and online learning is by far the most prominent area of interest on the site. Because we have a strong policy of technological non-interference, all we can do to ameliorate this problem is evangelize about alternative uses, nurture these when they occur, and make explicit our celebration of diversity. But, though we are among the most prolific posters to the site, and we do run workshops and presentations, particularly to encourage staff members to participate, our views are just two among many.
Sets, not Groups
As we have already mentioned, the Friends of the Landing should be a set but is embedded in a group tool and is comprised of multiple overlapping groups and networks, all playing a significant role in its formation. We illustrate this with an example of a discussion that occurred around a problem users had commented on both in the group and across the site: that people sometimes post things others find distasteful, offensive, or boring. On the whole, the access control facilities on the site prevent such things from occurring, as people usually recognize there is a limited audience and deliberately post sensitive materials so that only those with an interest will see them. However, that is not always the case, and on some occasions, there are very good reasons to make controversial or sensitive posts public. Yet because of the diverse sets of people on the site, some of whom are engaging due to coursework requirements, and all of who have diverse tastes and ethical or religious stances, this caused problems for some users. A couple users commented that they usually accessed the site from the workplace, where some content is forbidden or disapproved of. The discussion was started with a message outlining the problem and providing three solutions that Friends of the Landing had suggested, none ideal:
1. . To provide the means to filter out/ignore specific individuals
2. To (optionally) censor specific words
3. To encourage people posting potentially sensitive content as “not safe for work”
For the purposes of this story and in the interests of preserving the right to privacy of the participants, we do not examine the discussion in detail, but will observe some of the outcomes that occurred to illustrate how the discussion was a valuable learning experience, how it failed to achieve the initial goals of its initiator, and how it resulted in further conversations that highlighted the complex interplay of group, set, and net modes of interaction on the Landing.
The discussion was a rich learning dialogue, in which many diverse points of view were brought to the table, with distinct camps in computing, social sciences, education, and support/administration staff. The discussion often revolved around the complex issue that the participants were not all equal, some being students of the staff involved, others being recognized researchers in their different fields, bringing expertise and vocabularies that required a great deal of unpacking and explanation. These explanations and clarifications provided benefits for many participants, several of whom commented on the enormous value they were getting from it as a transformative learning experience. Many difficulties were caused because some treated the Friends of the Landing group as a community, while others saw the whole Landing (the tribal set) as the community under discussion, and some were interacting with people they knew from other contexts. The fact that this was not, technically speaking, a typical hierarchical group, but more of a set, made it very difficult to come to conclusions.
Suggestions that problems should be resolved by establishing a cooperatively designed social contract, for instance, were difficult to bring to fruition because the discussants recognized that the Landing is not a single community but many, with different social forms including groups and sets and networks. Each of these cross-cutting cleavages has different, sometimes overlapping but often divergent needs and interests. By the time the discussion fizzled out, after branching into two further sub-discussions, over 180 messages had been exchanged, many of them lengthy and filled with references and links to further readings, and the discussion continued for some time in ensuing reflective blog posts. As we write this, no solution has been found that satisfies everyone, and it remains an ongoing wicked problem.
Ownership
We have noted the central importance of ownership and commitment, but it takes a huge leap of faith for an individual to commit posts and effort to build a network when the future of the site itself is unknown. One the many things that was done right at the University of Brighton was to make a commitment for the long haul (Stanier, 2010). It was recognized from the start that a large-scale social system needs time to grow, and growth cannot occur unless the people occupying the space feel it is more than an experimental campsite that may disappear at a moment’s notice.
To date, the Landing has been funded and supported as a research project, championed by this text’s two authors. However, the site’s creators have always intended for it to be an integral and (we hope) essential component of our distributed university’s infrastructure. Thus, we wish to migrate it to a place of permanence and continuity and to normalization within the university’s administration and budgeting cycle. As financial background, the project was initially funded by \$150,000 (CAD) of research development funding and has since received about \$80,000 a year from a variety of internal and external research funds. Almost all of this funding is used to support a full time PHP programmer with part-time support provided for systems administration from the university research centre.
We have had a number of discussions with our computer services (CS) department, and find that much of our development process parallels the ones used by the university to support its open source and proprietary administrative systems— including Moodle. However, we have evolved the Landing at much faster speed than a typical CS project, and do much of our development and testing following the Web 2.0 mantra: “release early and release often.” This results in a culture clash and occasional misunderstandings as we negotiate a future long-term home for the Landing. We are currently negotiating with the Library, which is attempting to reinvent itself, and we hope that the Landing will become an appropriate feature of this “library of the future.”
The Perils of “Release Early, Release Often”
Our preferred development process has been successful, inasmuch as the site has not failed, had its security breached, or been brought down (apart from once due to hardware issues we resolved using mirrored recovery systems), since it was first installed. However, some people have complained that the site changes too much. For those who are trying to use it as part of formal courses, instructions provided elsewhere go out of date very quickly: the Landing is not friendly to top-down group processes. For others, however, the Landing represents a constant learning challenge as new features and improvements provide new challenges. We do not have an easy solution to this problem. Our goal is to provide an ever richer, more valuable toolset, not a fixed single-purpose tool, but the price to be paid for increased functionality is increased complexity.
Achieving the right balance is difficult, especially as we are in the thrall of path dependencies whereby, if someone is using a tool, we cannot remove or change it so that data are lost. The Friends of the Landing have only approved the removal of one tool in the course of three years, a marketplace plugin that was hardly used, thanks to the distributed nature of the university.
Conclusion
In this chapter we have attempted to illustrate how the model and methods presented in earlier chapters play out in a complex, institutional setting. An overarching theme that emerges from this is the complex interplay between different components of the institutional machine and the social software embedded in it. Technologies, including institutional methods, procedures and techniques, pedagogies, tools, and information systems are assemblies, constituted in relation to one another, together creating a complex adaptive system where each plays a part in the whole. However, the role different parts play is not equally influential. Like most complex systems, the large and slow moving affect the small and fast moving more than vice versa (Brand, 1997). The pre-existing structures of institutional life, including the course form, punctuated engagement, formal requirements, existing software tools, as well as the external environment of competing systems and differing contexts of distance learners have played a major role in constraining the activities and methods used on the social systems we have described. We return to these and other concerns in the next chapter, where we discuss the darker side of social media for learning and some of the problems, solved and as yet unsolved, that emerge. | textbooks/socialsci/Education_and_Professional_Development/Teaching_Crowds_-_Learning_and_Social_Media_(Dron_and_Anderson)/08%3A_Stories_From_the_Field.txt |
ISSUES AND CHALLENGES IN EDUCATIONAL USES OF SOCIAL SOFTWARE
Turning and turning in the widening gyre The falcon cannot hear the falconer; Things fall apart; the center cannot hold; Mere anarchy is loosed upon the world,
W. B. Yeats, “The Second Coming”
In this chapter we explore some of the risks and dangers of using social software. We have touched on some of these already in our discussion of each of the social forms, and in our stories—out-of-control feedback loops, privacy, identity, safety, reliability, access, usability, and a host of other issues have emerged in the context of the tools, methods, and systems we use in social learning. In this chapter we focus on issues that arise within an institutional education context, rather than in purely informal and non-formal learning, because many problems are a result of the clash between novel adjacent possibles and the baked-in norms, methods, and behaviors that have evolved in a different evolutionary landscape. The fact that you are probably reading this as a book rather than a more socially mediated form demonstrates that we are in a period of transition, where old ways of thinking and learning are overlaid on and co-exist with the new.
Disruption and Change
Institutions seldom accept with relish major changes to practice, especially those that impact long-held norms and beliefs, and resistance is common. C. Christensen observes that disruptive innovation, of the sort we are observing here, is almost never successfully developed and adopted within existing systems (1997; Christensen et al., 2008). This is not surprising, because disruptive innovations are nearly always initially worse than the existing systems with which they compete. Most technologies evolve primarily by assembly, slowly gaining in complexity and sophistication. Only rarely do novel innovations come along, and when they do, they are nearly always less compelling, functional, or useful than what they replace at first. As Arthur (2010) explained, it was around twenty years before jet engines were able to compete with their piston-driven propeller forebears, and at first, they did so in separate, non-competing niches. Educational systems may be viewed as complex adaptive systems, and like ecologies, novelty rarely survives unless the evolutionary landscape changes or they are introduced from a different ecosystem.
Disruptive innovations can therefore only take root where they are allowed to incubate without direct competition with existing technologies. Christensen cites the growth of microcomputers which, he claims, initially targeted children and gaming systems in order to establish a market where they could evolve without clashing head-on with the monolithic mini- and mainframe computers that already had the adult market well sewn up. Net, set, and collective technologies used in learning have evolved outside the educational system in social networks, Q&A sites, blogs, and wikis, filling niches not already taken. Some, at first, crept into the educational system unbidden and are hardly noticed as they sow seeds for change: Wikipedia, the Khan Academy, and other social systems present faces sufficiently similar to existing models that are the thin end of a wedge to prise open educational systems to new technologies.
Net, set, and collective-oriented social technologies for learning, as we have seen, demand a different way of thinking about the learning process than those built for groups. The whole apparatus of institutional learning, including the processes and methods used in schools, universities, and colleges, is a highly evolved set of technologies that does what it aims to do very well. Social technologies designed to support net and set modes of interaction, when placed in direct competition with other tools such as purpose-built learning management systems built to fit with the other technologies of education, will likely fare poorly. In particular, there is a mismatch between the technologies of institutions and those of network, set, and collective-centered social systems. Technologies such as classes, timetables, hierarchical management, assessments, lesson plans, and teacher-oriented pedagogies are unlikely to be well catered for by tools that center on individuals and networks. This puts a brake on change and progress. It is exacerbated because existing systems such as Moodle, Blackboard, and Desire2Learn are highly evolved monoliths that perform a wide variety of functions and are purposely incorporating a growing number of tools that, superficially, look like network tools: wikis, blogs and similar features are increasingly included in such systems. However, though the tools may look and act in a similar manner to their counterparts in the wild, the group-based teaching that they are intended to support changes them. They use the same tools, but they are different technologies with different purposes, utilizing different phenomena with subtly different functionalities. Moreover, they are combined with different assemblies, and it is the assemblies that matter more than the parts of which they are comprised. Wheels appear in many different technologies, but it is the cars, watches, boats, cookers, and doors that matter to us, not the fact that they all contain wheels of some sort. It is the same for blogs and wikis: simply providing a tool as part of an assembly does not necessarily make that assembly into a different kind of social technology.
If we are to make effective use of networks, sets, and collectives within an institutional setting, then the greatest impact will be achieved by supporting needs and interests not already catered to by a well-evolved and entrenched set of tools. Potential niches within a formal setting include:
• Inter-/cross-disciplinary learning (e.g., support for using common research tools, cross-course projects, etc.)
• Learner-driven (as opposed to syllabus-driven) pedagogies
• Beyond-the-campus learning (incorporating others beyond the institution, whether formally or informally)
• Beyond-the-course learning, supporting disciplinary activity and interest across cohorts
• Self-guided research
• Self-organizing groups (e.g., study groups)
• Just-in-time learning
• Enduring committees, clubs, and student organizations
• Peer support (e.g., for learning to use research tools, computers, etc.)
Institutional Cross-cutting Cleavages
One of the first issues typically raised when a social software system is proposed that empowers students to share with others relates to dealing with posts that are critical, abusive, illegal, or objectionable—especially if the system allows public viewing beyond institutional boundaries. However, we have rarely experienced anything like this, either at Athabasca University or the University of Brighton.
Tens of thousands of posts have been created, almost none of which caused problems for others or threatened the institution, and none, so far as we know, included intentionally malicious or harmful material. Far from it: for the most part, public posts have served as an advertisement and invitation, something to be proud of, not hidden. We do, however, acknowledge the harm caused by bullying, especially in school systems.
Perfectly legitimate posts, taken out of context, can be offensive or disturbing for others using the system. Most university courses in the arts and humanities actively encourage students to explore complex adult issues and, in many cases, be provocative. In the comfort and safety of a role-controlled, group-based LMS, such posts are read by others with an understanding of the context, course requirements, expectations and norms. When this moves into a network, or worse, a set mode of engagement, posts that are made visible beyond the group might be seen out of context and may not be understood or may be deemed offensive by others. Discipline boundaries may make this more difficult to address. For instance, a religious student who is using a social media system as part of her course and treats it as an extension of the classroom—a safe space, a functional tool—especially if she objects to, say, swearing, may not appreciate a work of art posted by a student of fine arts deliberately constructed with profanities and blasphemies to challenge sensibilities. Some respond to this kind of problem with a knee-jerk reaction of censorship, asking for tools to hide such things, while others suggest self-censorship or tutor regulation of activities, but that denies the point of the provocative piece in the first place. Such anomalies are rare but important, affecting the beliefs, opinions, feelings, and relationships of individuals within a social system. This relates closely to the problem of contextual ambiguity.
Contextual Ambiguity
Within an institutional setting, learners are constantly switching between different groups, networks, and sets, in a far more diverse and discontinuous manner than, say, when engaging with a social network of friends or people in similar businesses. A single tool that supports group, network, and set modes of interaction can soon become an unwieldy and confusing space unless it has been carefully designed to take these discontinuities into consideration. Traditional learning management systems, being group-oriented, carefully divide spaces into well-defined, course-oriented segments. Social networks base their design models on the assumption that a single individual has a single network, a single persona, a single facet that is displayed, with more or less filtering, to others. Some systems, such as Elgg, Google+ or, in an inelegant way, Facebook lists, explicitly recognize the discontinuous nature of networks and offer support for filtering different content to and from different people, but these are simply filters: the underlying presentation of content does not vary, it’s just that some people see more than others, and some content is preferentially displayed depending on its originator. One very common way to get around this problem is to use different tools for different groups, nets, and sets. However, this raises important issues: it becomes significantly harder to maintain and for users to master, especially given the fact that groups, nets, and sets often overlap with one another in multiple dimensions, so similar lists of the same people may often recur in different systems. It also raises the specter of duplicate functionality.
We have created a range of solutions in Elgg for the problem of contextual ambiguity clustered under the umbrella term of “context switching”(Dron et al., 2011). The tools allow anyone to switch between different social and personal learning contexts, and to show different things to different people in different ways. Tools include tabbed profiles, dashboards, and group profiles, which allow an individual or a group to organize their learning life into separate spaces, each built with highly configurable widgets. These spaces may have different appearances and display quite different content and, crucially, may be visible to different people. The circle-like collections that allow people to create sets of networks make this highly configurable: people can reveal what they want to reveal, how they want to reveal it, and to whom they want to reveal it too easily and fluidly. Different dashboards can also be configured to make navigation and retrieval easier as a user switches from task to task. We have added many different widgets that make it possible to show fine-grained results not just from personal content but also from networks, groups, and sets that are of interest. We have also created a “set” tool that enables people to group collections of related content together so that they can more easily represent different interests and identities to different people.
“Duplicate” Functionality
One of the largest problems that we have faced in encouraging uptake of the Landing at Athabasca University is that it is perceived to offer little that is different from other systems in use at the institution. This is a valid concern. It is, for example, possible to use email to replicate almost anything that can be done with social software, from a discussion forum to an LMS or social networking system. However, the complexities of doing so for anything that departs from one-to-one or one-to-many messaging are immense, requiring a great deal of effort, interpretation, and coordination by people involved in the dialogue, and slowing the pace to the extent that, for many uses, would be highly impractical. For email to be a shared repository, for example, every recipient would have to keep a copy and organize it in a way that would make it easily discoverable when others refer to it; in contrast to the simplicity of sharing a web page or link to an online repository, this is clearly a poor approach. The same is true of many tools, especially when they provide rich toolsets. For example, an LMS may offer messaging (like email), chat (like an instant messenger), wikis, blogs, discussion forums, bookmark sharing, file sharing, and many other tools duplicated in social systems. Conversely, Facebook may provide many tools that are similar to or improve on tools provided by an institutional LMS. The toolsets that we use for different networks such as LinkedIn, Google+, Facebook, Bebo, Hi5, or MySpace may offer very similar functions to one another, or subsume others. Most systems have Twitter-like microblog variants, for instance. However, quite apart from the different networks and sets that inhabit these spaces, there are very few cases where systems are drop-in replacements for other systems. All have some differentiating value in terms of access control, role systems, aesthetics, usability, price, manageability, tools, long-term prospects, support communities, capabilities for integration, and so on.
Faced with a potential infinitude of alternatives, it makes no sense to choose them all. This is especially true of social systems, where the fact that someone is using one system may act as a disincentive to use another, and make it pointless to do so: if everyone in the world were using a different, non-interoperable social system, then they would not be social at all. In an ideal world, tools would be interoperable so that one could be integrated with any other, and any community could extend its tool use in any way appropriate to the social form. Where possible, such interoperable, mashable, and connectable tools should be used. However, real-world decisions seldom provide this luxury. Apart from the ability to use tools together, there are few general rules for making decisions about which to choose. We have found our framework of social forms very useful in establishing criteria and heuristics for selecting appropriate technologies. For example, our selection of an Elgg system was due to the lack of support within Moodle for set and net modes of engagement. However, this left us with many further choices to make. We list a number of weighted criteria here that may be useful to others faced with similar decisions, but it should be borne in mind that the context of every decision of this nature will strongly determine important factors, and this is far from an exhaustive list:
• Cost
• Support (internal and community/company)
• Potential longevity
• Control (personal, and at group level)
• Usability
• Accessibility
• Import capacity
• Export capacity
• Interoperability with other systems
• Device support
• Learnability
• Diversity of tools
• Scalability
• Hosting (local, cloud)
• Access control and role models
• Network/set/group features
We encourage those who are trying to decide whether to implement social tools in their learning, and which to choose, to extend and amend this list to fit their own constraints, interests, and contextual concerns. When selecting the technologies for the Landing, we gathered stakeholders together and asked them to contribute the things they wished to see and what they wished to avoid in the new system: our list was many times longer than the one presented here. Every sociotechnical context will be different and should be dealt with on its own terms.
Privacy and Social Software
Many a parent has been shocked by the personal disclosure exhibited by their children on networked social software sites. Do they really want the whole world to see the pictures or read about their antics at last weekend’s beach party? Will they want those images retrieved in ten years, when the not-so-young person applies for a new position or runs for public office? The affordance of cyberspace to provide and in some senses become a personal newspaper, radio, and television station broadcasting 24 hours a day to a global audience raises very profound questions about privacy, openness, and identity. The persistence of digital data on a network, and the fact that it may be seen in a very different context from its original posting, makes this a pressing concern. For many of us, the Net forces a profound rethinking of privacy and public identity. Privacy issues have likely been of interest since prehistoric times, when we shared our caves with others. The advent of both mass and personal communications has served only to speed up and magnify these concerns.
In his ground-breaking work, Altman (1976) noted the interest in privacy from many discipline perspectives shown by citizens, social institutions, and governments. He lists three ways in which privacy is defined and understood. To some, privacy revolves around exclusion, the avoidance of others, and keeping certain types of knowledge away from others. A second definition focuses on control, individuals’ abilities to open and close themselves to others, and the freedom to decide what aspects of themselves are made accessible to others. Paradoxically, privacy is not defined merely by the presence or absence of others, as is implied in the sense of being anonymous or “lost in the crowd.” Likewise, privacy is not valued in and of itself: it is relative to changing needs. An ultimately private life might look like a sentence of solitary confinement in jail, or being shipwrecked on a desert island. Finally, privacy is not static: each of us has moments when we desire both more and less of the presence of others, and similarly, there are times when we want to share more or less of ourselves and our ideas. Thus, Altman’s second definition, with its focus on privacy as choice and control, suggests we need mechanisms that allow us to control the boundaries in time, space, perception, and communication so we may selectively open and close ourselves to both general and particular sets of “others.”
Altman also describes the systems, tools, and behaviors we use to create, defend, and appropriately modify our sense of privacy to align with our everchanging needs. He notes three types of boundary tools. The first use verbal and non-verbal behaviors: we invite others to enter or to leave our individual spaces. The second is built upon on environmental constraints we build and inhabit such as doors, fences, and speaking platforms. Finally, Altman notes cultural constraints, such as the type of questions that are appropriate to ask, the loudness of voice, and the amount of physical touch we use to build and reinforce interpersonal boundaries that culturally define privacy spaces and practices. Each of these boundary behaviors has evolved over millennia and been finely honed by evolutionary selection. The Internet, however, has evolved with breakneck speed, and has created privacy concerns with which we have had little experience, nor enough time for us to evolve appropriate boundary tools and systems.
Palen and Dourish (2003) invite us to unpack our concepts of privacy for a networked context . They note that “with information technology, our ability to rely on these same physical, psychological and social mechanisms for regulating privacy is changed and often reduced” (p. 130). If we return to Altman’s three sets of boundary tools, we see that each is fundamentally altered by network affordances. Verbal and nonverbal behaviors certainly change in networked contexts, and their diversity, from text messages to immersive interaction with avatars, makes generalizations challenging. Most notably, networked behaviors span boundaries of time. A Google search reveals not only the comments I made this week or last, but reveals my comments from years past. Given that the boundaries I use to protect and define my privacy comfort zone are ever-changing and context-dependent, it is important that I know who threatens these barriers, so that I can raise the appropriate level of boundary protection. Unfortunately, such awareness of others is often not possible on the Net. The searcher of my name can easily be a trusted colleague, a potential new friend, an aggressive salesman, or an identity thief. Furthermore, the audience changes over time. Trusted colleagues one year may become aggressive competitors the next, and information that I may be proud to share this year may prove highly embarrassing in the years to come. Worse still, the place where I left private information may change its privacy rules and technologies without me being aware of this. Many users of Facebook, in particular, have suffered because of the network’s ever-shifting privacy controls that have often revealed more than they originally intended to different people.
Environmental boundaries also are morphed in cyberspace. All but the most tightly encrypted activity in cyberspace leaves traces. Many Net users use multiple email addresses and maintain multiple identities in immersive environments and open social software sites so that they can contain these traces. Passwords, access to members and friends, and other security tools replace locks and keys from the physical world but fill similar functions. And just like in the real world, locks, doors, and barriers require active maintenance and attention to adequately serve as boundary defenders.
The cultural boundaries are perhaps most profoundly altered in networked contexts. There are as yet only emerging standards and social norms that are acknowledge and adhered to by Net citizens. For example, many of us have different standards with regard to email functions such as use of blind copies, forwarding messages with or without approval, and the release of our own email addresses or those of others. In even newer contexts such as SecondLife, World of Warcraft, and other immersive environments, social and cultural practices are constantly evolving and altering, and currently these customs change while millions of new users are exploring these environments.
We see that the maintenance of privacy and the boundary tools that we use in the networked world are in many ways markedly dissimilar to those we encounter in real-life contexts. Thus, it should come as no surprise that privacy issues are a major concern for all who use the Net, and perhaps especially so for those using social software tools for both formal and informal learning.
Many social software suites allow users to set privacy controls on personal information, permitting them to effectively select the amount of disclosure they allow and to what audience this information is revealed. However, studies are showing that the majority of users do not alter these privacy settings, leaving the default settings of the system (Govani & Pashley, 2005). In a 2005 study at Carnegie Mellon University of over 4,000 students registered on Facebook, Gross and Acquisti (2005) found “only a small number of members change the default privacy preferences, which are set to maximize the visibility of users profiles” (p. 79). Govani and Pashley (2005) found that over 30% of university students in the US had given permission for people they had never met to be their “friends” on the popular social networking site, allowing these strangers access to their entire profile, containing contact information, photos, and other personal details. As awareness of the dangers increases, however, users are becoming more careful. A US-based Pew Internet Study in 2011 revealed that 58% of adult users of social networking sites limited access to only friends, 26% of them adding further access controls, and another 19% making them partially private (Rainie & Wellman, 2012). Even so, this still means that 23% of users make no effort at all to control their privacy.
It is interesting to speculate on the reasons why users are not more actively constraining the visibility of private content. This is likely not because of a lack of awareness about the problem, given the coverage in the popular press on issues related to identity theft and cyber-stalking. In a 2007 qualitative study of Facebook users, Strater and Richter found that “while users do not underestimate the privacy threats of online disclosures, they do misjudge the extent, activity, and accessibility of their social networks.” (2007, p. 158 ). The participants in this study did realize that posting personal information could have negative repercussions, but they assumed (often incorrectly) that such data was only accessible to a selected group of trusted friends. One might also wonder about the user-friendliness and design of social software tools. It may not be clear to users exactly who has access, and perceived as difficult to restrict access further. But what is more likely is that those users realize the value of social software increases in proportion to their support for connections with new and current friends and acquaintances. The balance is always a trade-off: many social software systems provide their services in return for information about individuals.
Taken to its logical conclusion, those most concerned with privacy would not participate in social networks at all, and indeed, this does happen—we have relatives who avoid all but personal communication online. Thus, we can assume users need very flexible systems that allow them to hide and reveal information at a low level of granularity, both in regard to the nature of the information and the membership of the various audiences who are allowed access to it. These decisions are very personal, and defy generalizations based on socio-demographic details. For example, the authors release their cellphone numbers only to a small group of very close friends and family. Yet for others, their mobile number is very public knowledge and is listed in many places on the Web just as many home phone numbers appear in paper-based telephone directories even today. We also provide information to select and changeable audiences. For example, we might share our calendars with associates at our workplace, but would withdraw this if either we or our colleagues left our current place of work.
Privacy and Teachers
The mismatch between the social forms of classroom groups, with their formalized hierarchies and social networks and sets, has led to many difficulties for teachers, especially in schools. The formal relationship between teacher and student causes difficulties for some when teachers disclose information about their personal lives, reveal preferences and interests outside the professional context of the classroom, and engage in social chat with students. Indeed, recognizing this mismatch, the makers of Facebook provide explicit advice on separating the formal context of the teacher from the networks of their students (Dwyer, 2009). We understand that the formality of teacher–student relationships can lead to difficulties in a network context that, in extreme cases, lead to teachers losing their jobs, or at least their credibility in the classroom. Many teachers deliberately refuse to accept “friend” requests from students and former students for this reason. On Athabasca Landing, we deliberately renamed “friend” as “follower” in order to address the fact that there are complex ethical and practical issues for some teachers and students treating one another as friends. However, the corollary to this issue is that a blurring between student and teacher networks can allow richer, longer-lasting, valuable relationships. By enabling students to see their teachers as human beings, warts and all, they can gain a clearer idea of what it means to be a lifelong learner, to see that education is not divorced from life but is an integral part of it.
The notion that teachers should be role models is deeply embedded in the way the profession is viewed in society, but we question the value of a role model who demonstrates secrecy, and by implication, hypocrisy. We believe that teachers should present themselves as they are, not as they should be. Institutional values need to be seen in a human context, not as aspirational rules but as lived behaviors. This is not to suggest that teachers should reveal every aspect of their private lives. Context matters, and some things are rightfully kept private from some people. But the notion that the solution to the problem is to keep everything secret to the extent that we reject personal connection with those we teach is taking secrecy too far, and represents a failure to embrace an adjacent possible that can greatly enrich the learner experience.
Why do People Disclose?
“Several objects motivated blogging in our sample. Bloggers blogged in order to:
1. Update others on activities and whereabouts.
2. Express opinions to influence others.
3. Seek others’ opinions and feedback.
4. ‘Think by writing.’
5. Release emotional tension.” (Nardi, Schiano, & Gumbrecht, 2004, p. 225)
The previous sections reveal that the control of privacy is a challenging and ongoing task. Effective management must work at a number of levels and entails a partnership of software designers, ethical and attentive systems managers, and knowledgeable and empowered users.
The design constraints of this context focus on three challenging propositions:
1. There is no single control setting that meets both the privacy and dissemination needs of all users.
2. There is no single control setting that effectively both secures and exposes all of the components of my personal profile and contributions or postings I wish to share.
3. There is no single setting or control that effectively both secures and exposes information over an extended period of time, since users’ needs are subject to change.
The first constraint leads naturally to the solution that each individual should be able to easily set the privacy controls over personal information. While such a solution works for informed adults, it presents further challenges when educational social software systems are used by children who require either institutional or parental guidance.
Linked with concerns about privacy, and in some sense predicating them is the notion of online identity. Increasingly, we establish a range of online identities across social networks, on the websites that we visit, in our email systems, and in the online group tools we use. Despite efforts to consolidate identities through systems such as OpenID, Facebook Connect, Twitter, or Google+, those who choose to engage with cyberspace have to deal with multiple ways of revealing identity across different contexts. Our own context-switching approaches are one way to deal with this (Dron et al., 2011), but the bulk of solutions involve using different social systems and tools for different purposes.
Trust
Beyond issues of privacy and identity, networks and sets (in particular) raise issues of trust and security. We have already observed that one of the most significant issues driving the use of collectives in networks is to establish faith in the credentials of those with whom, and from whom, we learn online. The popular press is full of examples of ways that trust can be broken online, notably in the behavior of some pedophiles and other stalkers in cyberspace, who take advantage of the many-to-many strengths of the Internet combined with the potential for anonymity to achieve nefarious ends. While we hope such problems are rarely present in learning communities, it is vital to their success that learners feel safe and secure when learning. Learning outcomes are far more easily achieved if, in particular:
• One trusts the skills and capabilities of a teacher, both in subject matter and in pedagogical abilities
• One feels safe from attack or lesser antagonism by one’s peers
Learning is, by definition, a leap into the unknown, and the unknown is scary. While we may justifiably be scared by what we know is harmful, what we don’t know is often scarier. It is a sensible evolutionary adaptation that makes us fearful or wary of dark places and novel situations: until we gain awareness of the potential risks, it is safer to assume that danger may be lurking than that there is no danger at all. This is only true up to a point, of course—risk avoidance also means opportunity avoidance, so it is more an issue of being wary than of not doing anything that might be dangerous. It is also true that many of us positively relish the tingle of fear that comes when starting a new learning trajectory, the thrill of uncertainty that comes with learning something new, but again, only up to a point. This is perhaps itself a learned behaviour, something we have come to recognize as a result of previous successful experiences, probably with the kind of assistance and safety that a teacher provided, even if we have now learned to teach ourselves.
One of the many roles played by teachers and teaching institutions is to provide reassurance and a measure of safety. This is an essential process: if the only way we had to learn how to swim, perform surgery, ride a bicycle, or hunt a wild animal were to actually do so in real life, then far fewer would survive the process. Any child who has learnt to swim by being thrown in at the deep end is unlikely to have a very comfortable memory of the process, even though it might have been tempered by an underlying trust in the one doing the throwing. While learning about medieval history, how to be a teacher, literary criticism, or how to play the piano may lack the risks and dangers of the previous examples, there can still be fear involved, if only of failure to achieve our learning goals.
Whatever the risk factor of our learning is, nevertheless, it is helpful to be led by one who we believe knows the paths. We need teachers not just because we can achieve more with the aid of an expert—remember Alan Kay’s warnings about the danger of a “chopsticks culture,” when learners are provided with technologies but no examples from which to learn (1996)—but because the expert reduces uncertainty and/or reassures us about what we do not know, and offers us the security of knowing someone will be there to catch us when we fall. Similarly, if there are fellow travelers, we usually want them to, at the very least, not wish us harm in achieving our goals. We need supportive fellow learners not just because they help us to explore perspectives, including our own, but because they reduce the danger. We generally feel more comfortable when entering an unknown place or situation if there is someone else we know and trust with us.
All of this leads to some interesting problems in networked learning. We have seen that collective approaches can help in establishing trust, but when learning and engaging with others, it is the purely human and social processes of communication that we fall back on. Different cues in what people say can help: it is usually obvious, for example, when someone is being provocative, flaming, or trolling. Equally, it is generally clear when someone is using dialogue to be supportive and helpful. Unfortunately, when the former has occurred, it may poison us against a particular community or network, reducing our willingness to participate. We, the authors, have experienced some responses to our thoughts and discussions on the subject of this book in a networked environment that were discouraging, infuriating, or just plain useless or irrelevant. Partly we are supported by each other, partly by a belief that the medium is worth persisting in, and partly we have been inured to such things over many years of participation, but it is easy to see how such experiences might dispirit someone feeling uncertain and insecure. Indeed, if it happens often enough, it may prevent them from wanting to participate at all in any network.
This is a larger problem than it might be in a closed group context because our networks are typically joined and borderless, so withdrawal from one network may mean withdrawal from others. To make matters worse, there are subtler problems than simple antagonism. People may use a network as a platform to discuss things that do not interest us, get sidetracked by things we consider irrelevant, or simply talk at a level that is either beneath or above us, leaving us feeling alienated or bored. The very diversity that gives networks much of their strength also, potentially, contains the seeds of their demise. Much of the work that we have performed in the area of context-switching and context awareness has been an attempt to reduce such dangers by allowing people greater control of how and what is disclosed, and with whom it is shared.
Access Issues and the Digital Divide
Although access to cyberspace is fast becoming the norm in both highly and less well-developed countries, the majority of people in the world still do not have access to an Internet-connected computer. This proportion becomes significantly smaller when we take into account those with mobile phones but, despite over 2 billion broadband-connected mobile devices, most cellphones used at the time of writing have limited access to the totality of cyberspace, and that still leaves billions with limited means to access even a small part of it, let alone the Internet whether for economic or political reasons. This remains the case despite the growth of services like U2opia (www.u2opiamobile.com) that bridge the gap by allowing Facebook or other service access through traditional “dumbphones.” The topic of mobile telephones raises a further concern that there is much inequality in access speeds and the capabilities of machines used to gain access to cyberspace.
What can be reached and how fast it can be accessed with a basic cellphone is far less than what can be achieved with a top-of-the-line laptop or tablet with a high-speed connection. The massive growth in such technologies seems set to continue for some time to come, but inequalities will still remain even when, by 2017, it is projected that a broadband connection will be available for almost everyone on the planet (Broadband Commission, 2013, p. 14). In the authors’ own country (Canada), the majority of the population is at least able, if they wish, to gain high-speed access to the Internet, but even in this highly developed country, there are huge areas where dial-up or, surprisingly often, satellite access is still the only option available. This immediately discounts a wide range of the technologies we have written about, including VoIP telephony, videoconferencing, live web meetings, immersive 3D environments, and more, as well as making even common websites, especially those using rich media, Flash, or AJAX technologies, unbearably slow to access. Having said that, access to more basic technologies like books, desks, and even pencils remains an issue in many parts of the world, so the problem is not new. Moreover, while the costs of initial access remain relatively high and still beyond the reach of some poorer families, once a connection is made into cyberspace, the cost of networked information is typically much lower than that of traditional books (Renner, 2009).
At Athabasca University we are making the transition from paper to electronic books and have calculated that, even given publishers’ often exorbitant textbook prices (whether electronic or on paper), the cost of a good e-reading device, whether a tablet or dedicated reader, will be offset after the purchase of two or three textbooks for an average course, while the cheapest tablets now cost significantly less than a typical textbook, and come with access to tens of thousands of free books from sites such as Project Gutenberg. Such devices offer more than just an alternative means of reading: they also provide access to the Web, email, and many other facilities of cyberspace. While many issues remain, such as the cost of network access, the availability of infrastructure, and the complexity of calculating environmental impact relative to the cost of paper, storage, and transport needed for books, the accelerating move to ever greater cyberspace access for an ever-increasing diversity of people seems inevitable for economic reasons alone. There are large economic and gender inequalities that must be overcome but we are already at the point where access to the Internet is more widespread than to a decent traditional education, especially at higher levels, and so we are optimistic about the future. We hope that the ideas we promote in this book, particularly as they apply to networks, sets and collectives, may suggest ways that learning can happen without a formal educational process, enabled by the massive growth in socially-enabled technologies that is bound to occur.
Mobile Learning
Mobile technologies offer many affordances. A modern smartphone is far richer than the average personal computer in its input capabilities (e.g., voice, video, velocity, direction, text, geo-location, Bluetooth, Wi-Fi, cellular networks, and more); even the simplest cellphones offer text and speech capabilities. Cellphones are typically with us all the time and smartphones allow us constant, uninterrupted access to cyberspace. At the same time, they have at least as many limitations as affordances, such as small screens, deeply incompatible standards, limited processing power, limited battery lives, expensive tariffs and overly diverse interfaces. With some exceptions, content developed for the Web needs to be re-presented for use on cellphones. Indeed, content and applications developed for one make and model of cellphone may fail to work on another, even from the same manufacturer. Despite the growth of popular platforms like iPhone, Blackberry, Windows Mobile, and Android, most applications will fail to work across even two of them, let alone all.
There are pedagogical challenges too. It would be wrong to suggest that the migration from traditional media to the Web was unproblematic, but it was a far simpler transition process than it now is from the Web to mobile platforms. Partly this was due to the fact that most uses of digital technology in education, as in other industries, do not show an imaginative leap when presented through a new medium: the typical LMS is a classic example of the “horseless carriage phenomenon,” a mirror of existing face-to-face processes in an online environment, with little heed for the affordances of the medium. The small-screened, incompatible devices with awkward systems at best for text input do not succumb so neatly to mimicry, apart from some limited contexts such as language learning. As technologies such as Twitter Bootstrap (twitter.github.com/bootstrap/) that allow multiple representations of content for different devices become more prevalent, awareness of these issues is increasing and the tools to address them are more widely available.
Legal and Ethical Issues in Networked Environments
The global nature of networked environments poses a range of challenges to many of our legal systems in different countries, states, and provinces. We have seen in recent years a sharp reaction from governments to the increase in network freedom new technologies allow. From the extreme black hole of North Korea and the censorship activities of China, to the subtler scrutiny and control of the US (as evidenced by the provisions of the Patriot Act), governments are becoming more active in controlling the use of the Internet by their populations. Even in relatively libertarian countries such as Canada and the UK, service providers are required to keep records of activities that may be scrutinized with, some would argue, insufficient concern for the rights of citizens.
Copyright (Cross Country/State/Province Concerns)
The increasing economic value of videos, blockbuster novels, and sound recordings has provoked governments to respond to pressures from their media and cultural industries to increase the length and enforcement of copyright protection for intellectual property. This has resulted in extensions of the exclusive but temporary monopoly granted to creators to market their intellectual products in many parts of the world. As a result, educators have had to wage extensive battles with copyright owners, who are often major for-profit publishers, rather than content creators, to assert their right to Fair Use of content for education and research purposes. Recently in Canada and elsewhere, the tide seems to be turning, and courts and legislatures are realizing that allowing dissemination, review, and critique in the education system actually enhances and stimulates the development of cultural and intellectual content, which was the original aim of copyright legislation.
Also of increasing importance is the capacity to lawfully share intellectual products while retaining some or all copyright, typically through various Creative Commons licensing schemes. It is a tragedy that so much potentially valuable educational content lies unused and unusable, not because educators or other creators want the product to be restricted from educational use, but because, prior to the Net and Creative Commons licenses, there was no cost-effective way to share it while retaining rights such as attribution, restriction from others commercially exploiting the product, or changing parts and then redistributing product.
Openness, Interoperability, and Integration
We should disclose a personal bias at this point: the authors of this book are strong advocates of open sharing of knowledge, and chose AU Press at least partly because it is committed to making its books available freely for education and non-commercial download. In a social learning context, a lack of openness can cause difficulties. For example, if a wiki has been worked upon by multiple authors, then ownership is hard to ascertain and the solution in a non-open context is often to default to that of the service provider—a university, publisher, or closed company. This situation both reduces motivation to contribute, because contributors do not have control of distribution, and prevents the free flow of knowledge. The issue becomes more complex when data are aggregated and re-presented, as may often occur when, for example, pulling in and redisplaying an RSS feed. There is a tension between personal ownership, the social capital that accrues as a result, and the sharing of knowledge that is essential for learning to occur.
The issue goes beyond simple questions of ownership, however. It is not quite enough that we own and share the data we produce: we also have to be able to re-use it, integrate it, and re-present it. For this, protocols and standards such as TinCan, OpenDD, Europass, RSS, and Atom are required to enable the easy movement of data from one system to another. Unfortunately, many proprietary systems are deliberately designed to make such transfers difficult. As is often the case, the dominant social software provider at the time of writing, Facebook, is one of the worst offenders: although user pressure has forced the company to allow people to export their own data, it is in a form that cannot easily be re-used in a different and potentially competitive social system. Facebook, Twitter, and other commercial systems often assert some degree of ownership over the content produced by their millions of users, and their business models are based on analysis and sale of “their” content. This is one of the reasons that boutique systems such as the Landing are valuable, because they make it possible to return ownership to users. However, efforts to do this on a larger scale, such as Diaspora, have failed to gain momentum so far.
Cultural Considerations
Despite a widespread feeling that we inhabit McLuhan’s global village, cultural identities remain strong. As with personal identity, we are typically not just part of a single culture, but engage in many cultures in many contexts. One of the most popular means of distinguishing differences in cultures comes from Hofstede (2001), whose study of a multinational corporation across 40 different countries revealed five distinct dimensions of culture. Of these, the dimension that showed most variation and has been frequently verified and observed in other studies (Church, 2000; Triandis, 2004) is the collectivist/individualist dimension. In individualist cultures, people see themselves as separate individuals and prioritize their personal goals over those of others, motivated by personal needs, goals, and rights—culture in the US, though diverse, provides a classic example of this set of behaviors, but it may be found in most Western cultures. In collectivist cultures, on the other hand, people see themselves as parts of “collectives” (note that this is not in the technical sense that we have used the term, but rather used in a more general social sense) such as families, organizations, tribes, and nations. Their motivations are more closely aligned with those of their social aggregations, and are driven by norms, duties, and expectations of these groups, nets, sets, and collectives.
Indian culture, though arguably even more diverse than the US, provides a good example of a more socially oriented set of attitudes. Given the great differences between cultures in this dimension, one would expect significant differences in uses of social networks, and this is indeed what we find (Kim, Sohn, & Choi, 2011; Vasalou, Joinson, & Courvoisier, 2010). Even more significant, from a learning perspective, is what happens when people with divergent cultural attitudes inhabit the same virtual spaces. Many of these differences are masked because social groupings need to share a common language. But, increasingly, as English competencies are developed by citizens of all nations in the world, we expect to see more confrontations and misunderstandings resulting from differences in this collectivist/individualist dimension. This is particularly significant inasmuch as, to a greater extent than when meeting face to face, obvious signals that a person belongs to a particular culture may be less prominent or not be observable at all.
Social software is only part of a learning system: content, behaviors, norms, existing social forms, and many other factors play strong roles in determining the shape it will take. Because of the way that structure can determine or influence behavior, there is a risk that a social software system designed with one set of cultural expectations in mind may work against the dominant (or conversely, dominated) culture that uses it. Conversely, where a strong culture exists, it may undermine the effectiveness of software built to support different needs. Where, for example, as in India it is the cultural norm for teachers to be treated with a particular kind of respect (Jadhav, 1999), a system that deliberately equalizes participants in a learning transaction may cause discomfort to some or all participants.
Author Dron experienced this firsthand when working in a cross-cultural collaboration between English and Indian computing students in the early 2000s, where different norms posed a major threat to effective collaboration (Singh & Dron, 2002).
After trying and failing to encourage discussion through closed forums, at least partly because such exchanges were not the norm in India, a large part of the solution to this problem was to use a set-based, topic-oriented collective bookmarking application, CoFIND, that largely anonymized interaction and required little direct contact beyond cooperative sharing. By focusing on a shared topic of interest to both groups of students, many of the social differences and imbalances could be safely ignored, while both groups benefited from the process. This topic-oriented sharing was a common denominator that reflected common practice among students in India, where sharing of notes was common but challenging the wisdom of elders, including those within the student body, was frowned upon or caused discomfort. This was in almost total opposition to the more constructivist, guide-on-the-side approaches taken with the UK group, where argument and conflict were seen as part of the process. As a result, what little dialogue there was when these cultures were blended was stilted and strange. On a smaller scale, we have observed that cultural expectations of teachers by learners trained to stand up and bow to their professors can make for a similarly strange and stilted dialogue in an open learning environment like the Landing. The fact that some students, especially those from collectivist cultures, feel uncomfortable addressing us as anything other than “Dr. Dron” or “Professor Anderson” overlays a different kind of ethos to that of the casual, first-name culture we typically encourage and that students from more individualist cultures more often find easier to adopt. This tendency is exacerbated by the formal context of institutional learning that reinforces and sustains roles and hierarchies, regardless of the equality we deliberately encourage on the Landing. Like all cultural differences, there is huge variety to be found among individuals and a great deal of blurring between cultures, but the propensity of groups to converge on norms and develop groupthink behaviors means that such behaviors can spread in both directions. Whether this is a good or a bad thing depends on one’s perspective and the context of the group. On a good day, it can help to provide a sense of membership and commonality. On a bad day, it can clash with the pedagogies and processes intended to bring about learning, either by preventing easy sharing or by causing discomfort to those for whom such sharing may feel unnatural.
Lost Souls
Sherry Turkle’s book, Alone Together (2011), is a tightly argued warning against the alienation and increasing separation between people that cyberspace technologies can create. As we increasingly cease to engage in physical spaces, often preferring the convenience and controllability of SMS, email, messaging, social networks, and other forms of electronically mediated interaction, the breadth of our social interaction increases while becoming shallower, less engaged, less human. Our knowledge of others becomes what they choose to represent with avatars and profiles, abbreviated and edited, essentially a narcissistic performance where friendship is measured in quantity rather than quality. This is indeed a worrying trend, though Turkle’s arguments are diminished somewhat by studies that show those who engage more online and through mobile devices also spend more time in face-to-face interaction (Rainie & Wellman, 2012). There are also notable benefits for those who have found communities and engagement with others who would otherwise have found it difficult to do so (Wei & Lo, 2006) and huge benefits that Turkle acknowledges in sustaining relationships at a distance (T. H. Christensen, 2009). However, even when active users of social media have extensive contact with others in person, that face-to-face time may not be full engagement. We have all sat in public spaces surrounded by others who are at once with us but also texting, messaging, and talking to people at a distance on cellphones and tablets. Whether or not we find this disturbing, for distance learners something is usually better than nothing. Without such technologies, many distance learners would be far more isolated than they are.
Information Overload
The ease with which information can be shared is both a blessing and a curse. In a formal course setting, students with who tutors might have had sporadic and formal contact in the past may now require or at least expect far more attention. One of author Dron’s students, studying the “benefits” of social media in online learning, proudly proclaimed the effectiveness of her intervention by pointing to increased satisfaction levels, greatly improved grades, and deeper learning outcomes. On further investigation, the interview responses quickly revealed the reason for this. For instance, instant messaging was seen as especially useful because, according to one responder, “It was wonderful to be able to contact your teacher any time, even after midnight.
One of the greatest benefits of social media lies in their potential to create richer channels that let great teachers do what they do best. However, dedicated online teachers are rapidly drowning in a torrent of interaction where there are no longer quiet times of the day, no longer holidays or conference times when they cannot be contacted. Some have taken email sabbaticals, or specified online hours during which they will attempt to reply, but the torrent continues for most of us regardless of good intentions to constrain our availability to others. This is an unfortunate result of the combination of network and group forms.
The group form typically includes, as part of the implicit or explicit rules that govern it, the requirement for a teacher to be responsive and demonstrably caring. That expectation has, however, arisen in a controllable environment in which caring need only be evidenced during class and office hours. More network-oriented social media such as social networking tools, blog comments, and email increase both the volume of traffic and the expectations of a response: the many-to-one nature of the engagement can quickly overwhelm a teacher. It is essential for the network-engaged teacher to make response time expectations clear at the outset and, in designing learning experiences that incorporate the crowd, to ensure that there are opportunities (and expectations) for others to answer questions and discuss issues.
A similar problem afflicts the online learner. A popular connectivist MOOC can generate hundreds of posts a day, and sorting the wheat from the chaff can be a major problem. Collectives can help a great deal in this case, however, providing assistance in filtering and searching for dialogue.
Filter Bubbles and Echo Chambers
We have already written of some of the ways that the Matthew Effect and preferential attachment can lead to mob stupidity rather than wise crowds. The perils of groupthink, echo chambers in which we only hear what we choose to, and the blind leading the blind are particularly problematic in a learning context (Pariser, 2011). Network and set forms of engagement remove the comfortable assurance of accredited sages telling us what to learn and how, replacing it not only with the difficulties of deciding who and what to trust but also a set of dynamics that may actually make things worse by their very nature. In a formal learning context, it is therefore of vital importance that teachers challenge and refocus students who are led to low fitness peaks and into filter bubbles.
Conclusion
We have solutions to some of the risks of a networked learning environment, but many risks and uncertainties still remain. The greatest risks all come back to difficulties in understanding the nature of social engagement in social media. Excessive content is often a direct consequence of superimposing a network or set form on that of the group, without adjusting the processes and methods used by the group. Privacy concerns often occur as a result of misplaced assumptions in a closed group, when in fact the social environment is net-like, or worse, set-like. Alienation and separation occur when people mistake Net-enabled interaction for relationships in meat-space (i.e., the non-cyberspace “real world”). Shifting contexts become hidden in simplistic, one-dimensional models of identity provided by many networked social environments. Collectives, used uncritically, are as likely to lead to stupid mobs as they are to wise crowds, perhaps more so, and the dangers of filter bubbles creating echo chambers where vision becomes narrow are great. We hope that the clearer understanding of social forms we have provided in this book will help networked learners and teachers to at least be aware of the risks and be more mindful of the ways that they engage. These issues will continue to emerge as technologies develop in years to come. With that in mind, in our final chapter we move on to discuss current and projected innovations that are currently emerging, providing new challenges as well as exciting opportunities. | textbooks/socialsci/Education_and_Professional_Development/Teaching_Crowds_-_Learning_and_Social_Media_(Dron_and_Anderson)/09%3A_Issues_and_Challenges_in_Educational_Uses_of_Social_Software.txt |
THE SHAPE OF THINGS AND OF THINGS TO COME
It is little short of a miracle that modern methods of instruction have not already completely strangled the holy curiosity of inquiry. . . . I believe that one could even deprive a healthy beast of prey of its voraciousness if one could force it with a whip to eat continuously whether it were hungry or not. Albert Einstein, Autobiographical Notes
In this chapter, we identify current trends in learning, make some tentative predictions about what will happen next, and proffer some wild speculations about what might happen if the world were a less complex place and there were fewer constraints on the effects and affordances of social systems on education.
We head toward the end of this book with some observations and speculations that probably reveal as much about us and our philosophical stances as they do about the future. It is fair to say that many generations have felt their educational systems were failing them. Near the beginning of the twenty-first century, this is as true as ever. However, not to be deterred, we would like to suggest that there are some significant differences between the current era and earlier times, and that a significant number of them relate to the growth of cyberspace, both in terms of opportunities and threats.
The Problem with Institutional Learning
We are in the midst of an ongoing revolution. Whether it is a continuation of the industrial revolution, the start of the knowledge revolution, the green revolution, some blend of these, or something else entirely, what we can say with assurance is that in these first decades of the twenty-first century, the rate of technological change is greater than ever before (S. Johnson, 2010; Kelly, 2010). This is an inevitable result of the increase in the adjacent possible that our technologies bring, which engender more technologies that change how we connect, perceive, and value people and things in the world.
As a direct result of technological change, the world is getting better, and it is getting better faster than ever before (S. Johnson, 2012; Ridley, 2010). By almost any measurement—wealth, health, life expectancy, pollution, crime, violence, education, accessibility, discrimination, population growth, exploitation, inequality—many societies in the world shows significant, and in several cases, exponential improvement when viewed over a period of decades. However, this improvement is not evenly spread. There are huge local fluctuations, and it would be misleading to suggest that everyone in the world has experienced every benefit. But, on average, the world is getting better and better at a faster and faster rate.
The learning revolution is a part of this improvement, both benefiting from and driving change. Increasingly, learning is being separated from the formal institutions that we have created to facilitate it, not just through visible and hyped technologies such as MOOCs. Knowledge (or at least information), once centrally held in libraries and universities, corporations, and isolated individual groups, is more available than ever before. As it has always been—but at a far greater scale thanks to cyberspace—knowledge is held within the network of people and the artifacts that they create. More importantly, that knowledge is accessible on demand. We can offload the need to know facts and details to the networked totality of cyberspace because we know we can access it when we need to. Rather than being the result of lengthy study, we can learn things we need to know in a short period, often only seconds from identifying that need. Whether we need to know who has written what about networked learning (and, through collectives, whose thoughts are most valued by the crowd), how to fix a leaking tap, or how to produce overtones on a saxophone, we can turn to the crowd and its reified knowledge for answers. This does not mean that the need for lengthy study has gone away, and we need to hone our skills in both discovering and evaluating the knowledge that we find in cyberspace, but it does mean that knowledge is more easily attained that it has ever been, and it is getting easier by the second. And yet, for all this massive increase in learning and the ability and opportunity to learn, we continue to run institutions as though it had never happened.
This is not just a problem about learning. It is also a problem about the purpose and structure of learning. We are less likely than ever to stay in the same place, highly unlikely to stay in the same career, and many of the “careers” that we embark on would be unrecognizable to our parents, let alone our grandparents. Children born today will have career paths we can barely imagine at the moment. What marks this trend is an increasing need for creativity, flexibility, analysis, and synthesis skills in the use of information. Yet our educational systems have been phenomenally slow to change their approach in response to these issues. Indeed, many changes are extremely regressive, as governments try to prove they are doing something to deal with the gaping holes in education visible to all by measuring the measurable (e.g., SAT scores, or the number of hours spent on centrally specified tasks) and controlling what should not be controlled (e.g., setting standardized lessons and outcomes for curricula). There is, and has always been, a tension between the role of education as a means of reproducing cultural norms for stability and as an instrument of change.
There is a pervasive, if sometimes fuzzily formulated, recognition of the value of education to society. This leads to top-down and bottom-up demands for an increase in the numbers of people entering higher education that makes their traditional processes and infrastructures creak at the seams. It is not surprising, therefore, that institutions turn increasingly to mass-production methods in an attempt to cope with the demand. However, we are seeing a neo-liberal reluctance to fund formal education systems from public revenues. Thus, as universities become more expensive for students to attend, and these institutions fail to meet their bloating needs, they adopt a particularly retrograde form of instructivist learning: industrial-sized lectures, mass media use, and MOOC (massive open online course) formats, with regulated outcomes and fixed modes of delivery. But this approach is, if more than a century of research in constructivist learning has taught us anything, fundamentally wrong. An industrialized methodology is exactly the opposite of what is needed if we want to nurture the skills of new generations, infusing them with a love of learning. They must have the ability to be self-directed and self-motivated learners in order to cope with ever-more rapidly changing (and perhaps more dangerous) times.
Saving Institutions from Irrelevance
Before the twelfth century, people used to visit scholars in order to learn (Norton, 1909). They sat around while the great masters (who were always men), shared their wisdom, wherever they happened to be located. These students were, of course, quite rich—going to spend a few years at the feet of scholars was not something the average peasant ever dreamt of, and grants were few and far between. At around the same time, city burghers in Bologna and Paris saw the benefit of having many rich students populating their streets for years at a time, and helped to establish Europe’s first universities. At first, there were two distinct models of university: the university of masters, with Paris as the prototype, which set teachers up as arbiters of all things; and the university of students, stemming from the processes used in Bologna, where students determined what was taught and who taught it. Over the centuries, the Parisian model came to dominate. A concentration of self-moderating scholars soon led to things like
• The housing and collection of books into libraries;
• Buildings to house and teach students and faculty;
• Administrative procedures to manage ever more complex processes;
• Formal awards and testing methods to validate both institutions and their learners,
• “Efficient” methods of teaching like lectures (and the infrastructure to match);
• Restrictive subject ranges born of economic and physical necessity (communities of scholars needed critical mass);
• Large, complex bureaucratic infrastructures to maintain and organize the educational machine to handle timetabling, student registration, award-giving, hiring, and firing;
• Overseeing bodies (often governmental) to ensure quality, consistency, and so on;
• Restrictions on entry to ensure students’ capability, class, and finances to succeed.
A few centuries later, in the late eighteenth century, the written exam was born in the form of the Cambridge mathematical Tripos, which came to supplement or replace the traditional vive voce oral presentation and defense of a thesis. This innovation spread fairly slowly over the next century, driven largely by economic and standardization benefits: written exams were cheaper than oral tests to mark and administer. Beyond that, there were few major innovations. Except for minor technological innovations such as slates and quills, and later ballpoint pens and whiteboards, the occasional restructuring (e.g., Humboldtian universities) and the incorporation of subjects other than the original three of theology, law, and philosophy (including, after some hundreds of years of being treated as a manual trade, medicine), there was little change. The teaching methods and organizational structures used in most institutions today would be instantly recognizable to Abelard, one of the early medieval education pioneers. Nearly every technological innovation in education since medieval times has been an attempt to overcome some of the unwanted consequences of the basic technologies that remain unchanged.
Even modern open and distance universities that should not have to conform to patterns that emerged out of their physical and historical context, replicate structures designed to fit scholastic life in medieval Europe. And so we continue to see the dominance of a group-mode model, including the evolved trappings such as courses, semesters, libraries, deans, faculties, convocation ceremonies, medieval gowns, classes, grades, exams, scholarly covens, doctors and master’s degrees, and an incipient hidden curriculum of class and gender (Margolis & Romero, 1998).
Higher Education has spawned a wealth of industries: copy houses, essay mills, textbook publishers, gown makers, schools that “prepare” students for university, companies that filter based on qualifications, government departments dedicated to grant awards, professional societies to defend their disciplines, tourist industries to employ the mass of students every summer, student unions, faculty associations, institutional furniture suppliers, whiteboard and computer manufacturers, and so on. It is very well integrated into our social and economic lives. More than that, the central credentialing role continues to serve as a filter for many jobs in academia, government and industry.
But sometimes, technologies can do more than repair the damage done by others. Sometimes they open up new adjacent possibles that allow us to replace the whole system, because the paths they clear ahead of them lead somewhere better. C. Christensen has called such innovations “disruptive” (2008; C. Christensen et al., 2008). The Internet is one of those technologies. Right now, we in academia are mostly using it to shore up the old technologies and entrench them deeper with tools that automate medieval ways, like LMSs and web analytics to drive performance according to limited criteria.
Sets, nets, and collectives do not fit comfortably in this medieval model of teaching. If we are to reap their benefits on a large scale, then institutions must adapt, and in many cases, radically change. We propose a number of changes to help break this cycle.
Variable-length Courses
This book has shown how courses are far from the be-all and end-all of intentional learning. They are, however, so central to the design of educational systems that it is easy to forget the enormous effects they have on the shape of institutions. Courses are the main temporal unit that determine the ebb and flow of activity within a university. They are units of work allocation to teachers, administrative units for payment of fees, assessment determinants, constituents of a final award, and dictate class sizes and structures, among other things.
Courses are, for the most part, fixed denomination currencies that, for reasons of organizational efficiency, are divided into a very limited range of unit sizes. In Europe, especially after Bologna Accord (Sanders & Dunn, 2010), and much of the rest of the world, there are credit transfer points that make it relatively easy to compare one course with another by considering the expected study time involved, including teaching activities, personal study, and assessment activities. Typically, such credit points relate to a notional 10 hours of study, so a typical 10-credit course would, with some notable regional variations, normally equate to around 100 hours of study for an average student expecting an average grade. This is, in principle, a flexible approach that might allow a course to be created of any size. However, in most cases, this does not happen. Courses are normally divided into chunks that fit traditional term lengths: a single-term course usually accounts for 10 or 15 credits, a double-length course accounts for 20 or 25, and so on. It is extremely rare for courses to provide less than 5 credits and unusual to find courses worth more than 30 or 40. Smaller chunks are much harder to administer for a group-oriented model: it leads to complexities of timetabling, credit transfer, and difficulties identifying appropriate prerequisites. In short, smaller chunks make the bureaucratic technology of educational institutions creak at the seams, massively increasing costs. At the other end of the spectrum, courses that are too large make things more difficult for students because failure is far more devastating and transferability is more difficult because of the increased risk of parts of a course overlapping with others. Much of the reasoning behind the sizes that are chosen relate to traditional academic term lengths, which are determined on the one hand by religious holidays (in historically Christian cultures, Christmas and Easter) and on the other by the expectation that students need to return to their homes to help with the harvest during summer months. This has little to do with pedagogic, disciplinary, social, or psychological needs in modern societies. Educational systems contribute in a large way to the continuation of such seasonal breaks, accounting for rhythms of work and vacation that reverberate through entire societies.
In North America, for historical reasons, things are much worse. North American institutions use credit points relating broadly to the amount of teaching rather than the amount of learning: this very bizarre inversion means that two apparently very similar 3-credit courses, the norm for a single-term course, may equate to anywhere between 100 and 200 hours of study, depending on subject. A single credit in an American institution thus equates to anything from around 30 to 60 hours of study. This equates to a more standardized 39 hours of teacher instruction which, of course, is irrelevant in an asynchronous online environment, and gives no clue as to the amount of time spent learning. The combination of fuzzy and inconsistent expectations and coarser granularity makes the system even more bureaucratically dense and less flexible.
Whichever system is used, its value is not for the student but for the bureaucratic machinery of higher education, with lengths determined not by any pedagogical or organizational rationale, but by a pattern of holidays relevant to medieval times. Among the biggest problems that arise from this kind of chunking is that, from the perspective of acquiring any given competence, there are no fixed limits on how long it might take. For most people, a skill such as learning to tie a shoelace can probably be acquired in minutes, but for some it remains a challenge for years. For some people, becoming a proficient programmer may take years, while others may become productive in days. Literacy in many arts or sciences may take a lifetime to acquire, but different levels of literacy can be reached in minutes or hours.
Competence-based Assessment
For over a century, the most popular approach to assessing competence in university courses has been the previously unseen written examination. The popularity of this form of assessment has much to do with the fact that they are perceived to reliably ensure the person who claims to have learned something has actually done so, and they are relatively cheap and easy to mark in small numbers, or at scale. Unfortunately, they achieve neither goal. Exams are expensive because they do not contribute to the central goal of learning. In fact, it is considerably worse than that: they actively reduce motivation to learn because they impose extrinsic rewards and punishments, thus massively reducing intrinsic motivation (Deci & Ryan, 2002; Kohn, 1999).
Given their strong extrinsic role of punishment and reward, it is unsurprising that over 70% of high school students admit to cheating in exams (McCabe & Trevino, 1996). Measures to reduce this level of cheating are extremely expensive, and it is a never-ending arms race that cannot be won by educators. If exams were accurate discriminators of skills then this would be less of a problem but, except in some very limited contexts, they are not. With the exception of a few trades such as journalism, the competence of writing or problem-solving using a pen or pencil, with no access to the Internet or to other people, without a computer, in silence, with extreme time constraints and under extreme stress to perform, is seldom if ever again required. Exams reward those who work well under such pressures and punish those who do not, even though these pressures are almost never going to exist in any real-world application of skills and competence. At best, they lead to the development of gaming skills that students use strategically to pass examinations, not to gain scholarly competence.
What is required is accreditation that shows what you can actually do, not whether you can pass a test on fixed-length courses; accreditation that is transferable to wherever you need to go next, that is precise, that does not bind you to one institution, and that allows you to receive recognition for what you are provably able to do, whether the context is academic, professional, or personal (Berlanga et al., 2008; Koper & Specht, 2006). Partly due to the unreliability of university assessments in identifying the skills and qualities of candidates, and partly because it is easy, an increasing number of employers are either ignoring or reducing the weighting of formal qualifications when hiring new employees. Hiring managers consult sites such as LinkedIn and even Facebook, especially where skilled professional work is needed, leveraging networks and associated collective tools (such as reputation tagging) to identify those with appropriate and appropriately verified skills.
Badges
To partly formalize learning achieved in sets and nets as well as groups, increasing attention is being paid to the use of badges. Badges are symbols or indicators received for demonstrating some competence, skill, quality, or interest. The Scouting movement and other organizations of its ilk have used the physical variant of the idea for many years. The modern update of badges involves the use of images that indicate one’s accomplishments: these are as simple as participation in a forum or as complex as receiving a doctorate. Each is certified by an issuer (the “badger”), so they cannot be easily faked, and tied to a person’s identity so that they cannot be reissued to someone else. Badges may be set to expire after a certain time for volatile skills. While anyone can issue them, some issuers will have higher reputations than others. They have many benefits beyond simply signaling achievement. The Open Badge project (openbadges.org) identifies a range of uses, observing that badges can:
• Signal achievement
• Recognize informal learning
• Transfer learning across spaces and contexts
• Capture more specific skills than traditional degrees
• Support greater specialization and innovation
• Allow greater diversity
• Motivate participation and learning outcomes
• Allow multiple pathways to learning
• Open doors
• Unlock privileges
• Enhance your identity and reputation
• Build community and social capital
• Capture the learning path and history
• Recognize new skills and literacies
• Provide a more complete picture of the learner
• Provide branding opportunities for institutions, organizations, and learning communities
(Adapted from the Open Badges FAQ (n.d.) at https://wiki.mozilla.org/ Badges/FAQs#What_are_the_benefits_of_badges.3F)
There are many ways that badges provide a way out of the institutional course stranglehold without necessitating a massive change to traditional ways of doing things in one fell swoop: a badge can represent accomplishment of a course as easily as it can any other competence. It is notable that many of the benefits are of great potential value in groups (e.g., allowing faster establishment of norms, expectations, and trust based on past accomplishments and known skill levels), nets (e.g., providing social capital, enriching projections of identity, and easing entry into different networks), sets (e.g., providing attributes to identify sets and subsets, and assisting in trust management), and collectives (e.g., discovering trends, identifying patterns of reputation and clusters of related skills). Badges are thus not just signals of accomplishment but act as mediating objects for social engagement outside group contexts. They offer a potential means of enabling networked and group learning to move beyond formal educational boundaries and enter into mainstream and lifelong learning.
However, on a cautionary note, there is a risk that badges may be seen, like traditional assessments, as extrinsic rewards. The wealth of evidence that such rewards are almost always deeply demotivating, especially when related to complex skills or creativity (Kohn, 1999), means that it should always be made clear that badges are simply credentials, evidence of achievement, not things to be striven for in and of themselves. We have some concern, especially when they are used as motivating objects, that there are big risks they could lead to unintended and unfortunate systemic consequences, much as the use of grades, gold stars, and awards in classrooms and the workplace have demotivated and hobbled generations of learners. We hope that they will eventually be seen as nothing more than evidence of ability, not as a substitute for success. Unfortunately, their prominent use in large-scale teaching systems like the Khan Academy and large MOOCs suggests otherwise.
At the time of writing, the specifications for badges are still in flux and, though used in a number of formal institutions and organizations, it remains to be seen whether they will become ubiquitous. However, they or something like them represent the technological means to enable the revolution in assessment and accreditation that is necessary if education as a formal process is to survive by moving beyond the rigid course format. Badges provide the means to transition between top-down accreditation and bottom-up recommendation. In principle, they can be aggregated and reassembled to fit different needs and purposes, signal specific competences rather than broad disciplinary knowledge, and equally used to describe still-broader facets of individual accomplishments. It is possible to envisage uses beyond the purely academic that may be of great value to potential employers, such as, for example, recognition of creativity, stickability, stoicism, or sociability. We can already see instances of such broad recognition having value in, for example, LinkedIn endorsements, which do not only show subject skills but also personal qualities. It is easy to imagine a PageRank-like collective process that uses networks to judge the reliability of such assessments and, just as Google currently provides greater weight to academic pages than to commercial pages, so we might see greater weight given to certain badgers relative to others. This may, over time, lead to a self-organizing system of accreditation in which universities carry no innately greater weight than individual academics, employers, social networks, or sets of people with relevant skills.
Changing Patterns of Publication and Distribution
Libraries provided a strong rationale for establishing an institution before the advent of the Internet, and were often central to the institution and its functioning. Books, journals, and other resources were too expensive for individuals to buy for themselves, and it made sense to centralize them. The word “lecturer” derives from the fact that a single individual would read texts to classes of scholars in the Middle Ages because books were too rare to share. This is no longer such a strong imperative. In the course of writing this book, we have barely touched a piece of paper. While some books (particularly those published more than a decade ago), are still only available in paper form, the vast majority of the papers and books we have referred to existed on our computers as electrons and patterns on a screen. Libraries are still valuable, largely as a means to negotiate terms with closed publishers to gain access to electronic versions of papers and e-books, and we have used our own institutional libraries extensively in researching this book. However, the papers and books themselves are, for the most part, held by the publishers or freely available on websites. The library has become a junction in a network, not a repository of knowledge.
Beyond the library, in several cases we have been able to make use of our networks to contact original authors to receive not only their work but also engage in dialogue about it. This is the thin end of a large wedge. In many cases, work is published in blog form, and we can engage in and benefit from discussions with many others about it. We see this as an increasing trend that may eventually transform or even oust the traditional processes of peer review. Literature, especially academic literature, is enlivened by the dialogues that develop around it. Like medieval glosses, scholarly works are explained, illuminated, criticized, and extended by the conversations around them and these may provide equal or greater value to that of the original work that is being annotated.
Blog posts are typically seen as a less worthy form of academic publication because they lack peer review. However, the truth is sometimes almost exactly the opposite. The problem with this point of view is that it assumes that a blog post is simply a new way of presenting information that is like a newspaper or journal article. It is not. A “publication” is not just the blog post, but also the diverse dialogue that is associated with it: a blog post is the work of a crowd, not an individual. A post from a popular blogger in academia is not a standalone work like a traditional academic paper but an extended process, in which the comments are often as important as the post itself, where errors are examined, implications observed, and contrary views expounded. Often, through trackbacks, the blog becomes part of a network of shared knowledge that explores an issue in depth. The article that spawned such reified dialogue may itself be part of a larger network of connected posts. For writers of books such as this one, targeted at a largely academic audience, this presents a problem. How can one cite such a connected jumble, whose character is constantly changing and whose essence is discursive, where good and bad is mixed with sublime and awful? This is not the same kind of publication as an academic paper to which references have been made in other papers, despite apparent morphological and topological similarities.
There are two main reasons for this. Firstly, the pace is different: the slow rate of reply through academic publication of the traditional kind, where it is not uncommon for an article to take two years or more to reach publication, means that the dialogue is cumbersome and the original author may well have moved on to another topic by the time he or she might have replied to a response that appears in a follow-up peer-reviewed journal paper. Moreover, on many occasions the nature of academic rewards suggests that there is little motivation to respond: academics may not wish to tread old ground, and will have moved on to other considerations. Secondly, the conversation through academic journals is spatially discontinuous. A blog forms a centerpiece around which discussion and critique evolves in situ, whereas academic papers engender responses in different journals, conferences, workshops and presentations across the world, with few easy ways to link them together as a coherent dialogue. There are few places where the chasm between traditional modes of communication and the new forms that social software enables are so starkly highlighted.
Beyond simple blogs, collectives can provide powerful means of filtering and shaping these kinds of dialogue to provide a meta-review of the reviewers. On sites such as Slashdot, the use of the collective, through technologies such as rich metadata and karma points, can shape a large dialogue to reveal posts that are highly valued by the community for different needs, creating more reliable, richer, and more diverse co-authored resources than the best traditionally authored texts. An early system for computer-supported collaborative argumentation, D3E, formed the basis of JIME (Journal of Interactive Media in Education) in which conversation and disputes around papers provided rich peer review that was often as valuable as the articles under review.
While the existing author model persists, such systems are unlikely to see persistent use. It is notable that the JIME experiment was eventually abandoned, though recently revived in a different and less adventurous form, and efforts to make an educational equivalent of Slashdot have foundered, largely due to its geek-friendly design that appears arcane and complex to people of a less technical orientation. Less geek-oriented approaches such as that used by the StackOverflow family of sites have been far more successful, but have yet to see much transfer to academic environments. However, even more formal processes, such as those that sustain PLoS One, are increasingly open and inclusive: PLoS One has a panel of over 3,000 expert reviewers, and the reviews generated are aids to understanding for not only the writer but the reader of the article as well.
Flattening Organizational Hierarchies
Institutional hierarchies and associated bureaucracies were once thought to be a necessary evil that had to be tolerated so large groups could work together efficiently. On the whole, they still work moderately well when the world does not change too fast. They are highly evolved social species, usually formalized group forms that have solved many of the problems of coordination on a scale necessary to support large populations. Without such technologies, we would be limited to the hunter-gatherer demes humans are so well adapted to live in (Caporael, 1997). However, they come at a cost in time, effort, and space. One big reason for this is transactional distance. Each level of a hierarchy separates one sub-community from another. This limits the capacity for dialogue between those in different organizational units and requires dialogue to be replaced with structure—formal reports, memos, announcements, and the like—that condense and impose structure upon what may have been less formal dialogues, with the truly informal being lost or diluted in committee meetings and other formal channels of information exchange. This channeling and condensation up and down the hierarchical structure is a necessary feature that makes such hierarchies possible. Those at the trunk ends of the tree would not be able to cope with the mass of detail from the branches without such methods, and those at the branches would be overwhelmed if they had to pay attention to everyone else in the organization.
However, it does not have to be that way. The capacity of cyberspace to support larger set-like tribes as well as groups and nets, especially with the aid of collectives that can provide the filtering and channeling formerly delivered by formal condensations of reports and top-down edicts, creates opportunities to rethink how and whether such hierarchical technologies are needed. Just as individual learners can learn effectively in nets and sets, so can a whole organization. In a tumultuous world, there is a need for structures that are flat, distributed, and agile, adaptable to changing needs, interests, and groups, yet still capable of effective and efficient coordination. Large, hierarchical organizations inevitably introduce rigid and slow-moving structural elements that preclude rapid change.
Breaking Disciplinary Boundaries
Part of the hierarchical structure of educational systems is based on subject and disciplinary divisions. These academic tribes and territories are deeply embedded (Becher & Trowler, 2001). They start in earliest schooling, with lessons, classes, and teachers becoming more and more specialized as academic careers progress. To an extent, this is inevitable. There are natural path dependencies that mean when we take one path we cannot take another, and so we become more and more focused in the direction of our interests. As we take such paths, we develop cognitive toolsets that are appropriate to different ways of seeing the world: the toolsets that we need for the appreciation of literature are quite different than those we need for physics (S. E. Page, 2008).
It is not a surprise, therefore, that communities of interest form around more and more refined disciplinary areas, where cognitive toolsets are similar enough to enable richer communication about a subject. These disciplinary divisions are reinforced by hierarchical group structuring: the schools, divisions, faculties, and similar expressions of difference with which we are all familiar. Because these are constructed as groups, and because groups thrive on exclusion and difference, it is equally unsurprising that the systemic effects of disciplinary clustering reinforce that clustering. It is embedded at such a deep level in everything from research funding to teaching practice that it is hard to imagine it could ever be otherwise. It is hard to be a renaissance person in a system that is fundamentally divided at its most basic architectural roots. Unfortunately, the world of real problems does not respect disciplinary boundaries.
A world of constant change demands ever-increasing creativity. Creativity thrives at the boundaries and borders (Wenger, 1998) and is driven by diversity (Florida, 2005; S. Johnson, 2010; S. E. Page, 2008; Vaill, 1996). If we create boundaries that are hard to cross, the potential for timely evolution, at least of the individual, is thwarted. This is a more complex issue than individual growth, however. It can be argued that the parcellation caused by such divisions allows for greater system-level diversity and so, if there are opportunities for those from different disciplinary foci to work together, they will bring richer cognitive toolboxes to the problem. As S. E. Page (2008) demonstrates, a diverse group will usually outperform a less diverse one, even when the less diverse group is composed of experts, for most problem-solving and creative activities. So, while disciplinary areas reduce individual cognitive flexibility, they can increase it for society as a whole. The problem is one of balance: it makes no sense to completely demolish subject boundaries, because that flattening would reduce overall capacity and creativity, and anyway, would be impossible: people do have diverse and incommensurate interests in areas of study, and that is as it should be. Nevertheless, it makes no sense to sustain subject boundaries to the extent that crossing borders is too difficult for individuals. The solution lies in recognizing that these are not groups, but sets of people with shared interests. People will always focus on what interests them, and path dependencies mean they will always cluster in particular sets. If we are to make greater progress toward creative and agile educational institutions, then deliberate flattening is required, which means getting rid of inappropriate group forms.
If people wish to form groups for particular purposes, for instance to perform some substantial research or to further the study of teaching in their set(s) of interest, then that should be possible. Those groups may be composed of people with similar cognitive toolboxes, but they may not. However, groups should not be created out of sets simply through tradition or for bureaucratic convenience. Is there a case for groups of mathematicians who work together on problems or as teachers? Yes, absolutely. Is there a case for departments of mathematics? There may be far less compelling reasons for this, almost all of which revolve around a circular assumption that they exist within a hierarchical bureaucratic structure where such a department is needed (for administration, funding, research recognition, and so on). But, as we have already suggested, such structures no longer make the sense that they used to make. As a result of this disaggregation of boundaries, new organizational models that recognize and facilitate knowledge production within cross- and multidisciplinary sets of interest and focus (e.g., environmental issues, urban construction, education) may be created as needed, when needed.
Changing the Patterns of Teacher Rewards
Legend has it that the open sleeves of gowns worn by professors are pocket-like because students would drop money into them if they were satisfied with a lecture. Had universities developed along the lines of the student-led Bologna model, a variation on this approach might persist today. However, for most of those in academia, payment comes in the form of a predictable pay packet. In North American systems, though not commonly elsewhere in the world, those wishing to become full-time permanent professors must endure a curious trial by fire known as attaining tenure. This requires them to jump through a series of hoops to show that they are well-rounded (and conventional) academics who can teach, research, and participate in the university and broader community. Once they’ve achieved tenure, all can and some do rest on their laurels. As a process, it leaves much to be desired. However, the world over, it is the norm for a Humboldtian model of research, teaching, and community service to be fossilized into the structure and organization of an institution. This has some unfortunate consequences, such as the fact that students are often taught by researchers who cannot teach, and that research is often performed by teachers who are not great researchers.
The notion that every academic should be an all-rounder accounts for much of the dissatisfaction expressed by those in the profession—high workloads, low teaching standards, and mundane or pointless research. It is one of the structural forces that propel academia along its well-trodden furrows and away from potential change. It is particularly strange in an institution like Athabasca University, the authors’ academy, which distributes the teaching role among many such as learning designers, editors, graphic designers, and technologists, and employs people in roles such as coordinator and tutor that are primarily concerned with teaching and its coordination. Individual academics still need to support the three pillars: teaching, research, and community engagement, despite the fact that they are no longer individual academics in the traditional academic sense. As we move more fully into the sets and networks where learning happens, these restrictive roles will seem stranger for everyone.
For some, and we are among them, the three pillars of academic life are fulfilling: all of the roles are interesting, valuable, and enjoyable. For others, this is not the case, and as a result, many who would play one or two of the roles well are deterred from engaging in the profession, or leave it early. In the US, the mean length of an academic career is less than 11 years (Kaminski & Geisler, 2012). Some institutions have dedicated themselves to research or teaching at the exclusion of the other, but this too has dangers. Research informs and motivates learners, and teaching at a high level is difficult without a passionate and ongoing interest in the subject being taught, stimulated by active research. Some forms of research can appear pointless if they are not disseminated and explored through teaching.
No research has value without a community context, where work is grounded in, driven by, or meets the needs and wants of society. Once again, the way out of this dilemma lies in sets and nets. Problems arise because of the group-oriented view of a university, with fixed roles and rigid organizational demarcation. Academics are nearly always involved in cross-cutting cleavages, their sets intersecting with others across the world, their networks extending far beyond a single institution, and these connections are not only encouraged, but facilitated through institutional formalisms like conferences, journals, and workshops. However, within institutions themselves, the lines are often more distinctly drawn. Author Dron, for example, only found out that a colleague in the next office shared a research interest because he was a member of the same globally distributed set, a subject-oriented mailing list.
With greater organizational flattening, those with different interests and skills, whether in research, teaching, or community engagement can connect more easily. Our own Athabasca Landing demonstrates the value of this, connecting people in sets and nets who would otherwise have no knowledge or interest in what others are doing, and allowing good practice in research and teaching to spread organically throughout what is otherwise a hierarchy. Once this step is taken, it becomes easier to balance strengths and weaknesses. If some of one’s learning is mediated by those who teach well, some is inspired by those who research interesting things, and some is embedded in the social and business life of the community, then classes and subject divisions are simply obstacles that prevent the best use of resources.
This brings us back to how academics are paid and rewarded. While we do not have a quick and easy solution to the problem, it seems worthwhile to consider not the breadth of skill in an academic, but the diversity of skills across a networked institution, including the people, the technologies, and the structures that enable that knowledge to be spread and organized. As long as we retain isolated groups connected hierarchically, then well-rounded individuals are a necessity. However, if we assume a network and sets, supported by collectives, then it is the collective intelligence of the system that matters, not the skills of a single individual. To some extent, of course, this is already the case. Anyone who hires a team will make a point of choosing a diverse range of people knowing that they will contribute differently. Yet, a team is a group, and an institution, though inevitably carrying some of the trappings of a group, veers more toward the set or net in its social form.
Adapting to Learners
In an ideal world, we would provide methods of learning that are fitted to the subject and people learning them, not the needs and capabilities of institutions teaching them. This is what learning in sets and nets, with the aid of collectives, allows. It opens possibilities for people to learn differently. The role of the institution becomes more like that of the modern networked library, a hub to connect people with other people and resources that will help them to learn.
The Monkey’s Paw
“The Monkey’s Paw” is a story by W. W. Jacobs about a talisman that grants wishes which always come true with horrific consequences. This resonates deeply as a metaphor for technological change. While we have observed many systemic and path dependencies in the current system of education, there is no doubt that widespread changes would lead to equally unforeseen and potentially negative consequences. If we made these changes across the board, then the monkey’s paw would no doubt work its usual mischief. For example, breakdowns in disciplinary boundaries might lead to increasingly shallow insights, albeit with greater breadth. The loss of examinations would impact a range of businesses and social structures that depend on them, and make it easier for some types of incompetence to be enabled that were previously restrained. But this particular set of wishes has held sway for too long, and it is no longer fit for its purpose.
Beyond the Institution
For some years now we have been asking academic audiences at education and online learning conferences and venues where they turn first when seeking to learn something new. With almost no exceptions, the answer is a search engine (nearly always Google) and/or Wikipedia. Such audiences are, perhaps atypical, and at this time these remain starting points, not for most, the end-point in their search for knowledge, but it does help to demonstrate the massive penetration of social software, especially that which supports sets, networks, and collectives, in the service of learning. We are not speculating about the future when we talk about educational uses of social software in this book, but describing the present. In the past, such an audience would have turned first to libraries, books, reference works, and so on, and perhaps to courses and programs for more ambitious learning activities. Such things still have a place, but even here cyberspace is making massive inroads. In the course of writing this book, we have barely contributed to the destruction of a single tree, let alone the small forests that we both consumed when writing our Ph.D. theses. These exemplars of set, net, and collective applications show the enormous existing impact of learning with others beyond the traditional groups of formal education.
MOOCs and Self-study Resources
MOOC (massive open online course) is an acronym coined by Dave Cormier to describe an open-to-enroll free course with many participants. Current popular examples of platforms for MOOCs include Udemy, Udacity, edX, and Coursera, but the market is shifting rapidly, and we are seeing a proliferation of competitors as this book goes to press, such as Open2Study, WorldWideLearning, and FutureLearn. How many of these will stand the test of time remains to be seen, but there is clearly a growing demand for MOOCs. Coursera alone has grown faster than Facebook or Instagram, garnering more than 1.8 million students in just over a year (Cadwalladr, 2012). These represent only the visible edge of a massive movement to self-directed and institution-free learning.
There are two distinctive forms of MOOC emerging. One, the original bearer of the name that is championed by people such as George Siemens and Stephen Downes, is based in a connectivist model of learning, and the other takes a more industrial and instructivist approach, using behaviorist/cognitivist models of teaching. These have been referred to, respectively, as cMOOCs and xMOOCs (Siemens, 2012). Both xMOOCs and cMOOCs typically, though not universally, follow a paced model of learning: courses have start and end dates.
In xMOOCs, it is normal for those wishing to take a course to sign up and engage in many individual learning activities and some group discussions (usually with an instructor) that are closed to non-members. The cMOOCs typically also ask for enrolment, but this is mainly for coordinating a looser network. They seldom have formal groups of any kind: clusters of learners connect, form their own networks, and link up to the broader network, typically through a hub that aggregates networked content explicitly linked or tagged. This does not mean that there are no groups involved, as they may be used with or in formal classes. When creating the first MOOC to bear the name, for instance, George Siemens and Stephen Downes used a closed course run within an institution so that others could participate, offering accreditation to paid-for students and open participation to anyone and everyone else (Downes, 2008b). David Wiley had done this a year or so previously, but on a smaller scale.
A further subdivision of the genre that sometimes gets lumped with the others is the more flexible, bite-sized tutorial approach exemplified most prominently by the Khan Academy, that may also be found in many places such as Instructables (www.instructables.com), eHow (www.ehow.com), HowStuffWorks (www. howstuffworks.com), LifeHacker (www.lifehacker.com), Ted Talks, and countless others. We christen these kMOOCs (Khan-style MOOCs). They are almost entirely instructivist in approach, but their small size makes them more easily assembled by different learners and, unlike most xMOOCs and cMOOCs, they do not follow a paced model that requires learners to move in lock-step with one another. The Khan Academy alone has helped over 10 million students (Cadwalladr, 2012). There are similarities between kMOOCs and the goals of proponents of re-usable learning objects (RLOs), but unlike the RLO, these “courselets” are inherently social, with commentary, remashability, and engagement built in from the ground up. Interestingly, these courselets are aggregable, appearing in set-oriented categories and including both top-down and collective-generated recommendations of what to learn next. The combination of fine granularity, social engagement, and collective guidance suggests that such methods may have a great future.
While much discussion is currently taking place about appropriate models and the different virtues or vices of these approaches, we observe that the reality for many learners differs surprisingly little between the three models. Large and small networks, sets, and both face-to-face and online groups have emerged around all of these courses, supplementing and enriching the learning experience provided by the course itself, whether or not this was intended in their original design. This is a benefit of scale: with enough people learning at the same time, the traditional group form of course-based approaches becomes at best tribal in nature, filled with multiple networks, smaller groups, sets, and clusters. In the case of cMOOCs, a rich network is an essential element of the experience, but in the rest, it has happened as networks coalesce and form into study groups, online and face-to-face, or sets that form around topics, posts, or themes in the larger MOOC. Given the scale, even in a paced MOOC such as those developed for Coursera there are always people (often strangers) who form tribal sets to help one another. As Koller, cofounder of Coursera, puts it,
We built in the opportunity for students to interact with each other in meaningful ways and have one student help another through the hard bits so they could work together to achieve a better outcome for everyone. There was a real community built up where students felt incredibly motivated to help each other and answer each other’s questions to the point that in the Fall quarter of 2011, the median response time for a question posted on the forum was 22 minutes. Because there was such a broad worldwide community of students all working together, even if someone was working at 3:00 a.m., chances are that somewhere around the world, there would be somebody else who was awake and thinking about the same problem. (Severance, 2012, p. 9)
For the unpaced, small-chunk kMOOCs, the set that gathers around an individual tutorial, often instantiated in asynchronous comments, can be rich and pedagogically valuable, exploring and explaining the skills or concepts of the static tutorial, much like a blog post. In some cases, MOOCs have formed a structural backbone and content for traditionally taught classroom-based or online courses. The reason this can happen is that, despite intent in the case of some xMOOCs, without the binding group form of the institution, a single social form no longer formally binds learners.
Much has been made in the popular press of the relatively high attrition rates in MOOCs of all descriptions, but we think this is a not much of a problem. Relatively low completion rates are only a failing from the point of view of the purveyors of MOOCs, not from that of their participants, who often sign up on a whim, and may have little interest, time, or commitment to sustain their ongoing participation, at least when compared to the large commitment made in a traditional paid-for course. Freed from the coercion in conventional institutional courses, it is no surprise that MOOCs may be treated much like any other free resource on the Web. People get what they need, if the timing is right, and leave if they do not get what they want or if their curiosity is satisfied in the first week or two. There is one major benefit of this attrition rate, however. In part as a result of what are perceived to be high non-completion rates, the average length of xMOOCs appears to be getting shorter. This increasing focus and consequent diminution of group-like character means that they are becoming more and more aggregable, enabling learners to take ever more control over the learning process and integrate them into other social forms for learning. As course lengths become shorter, it would not be surprising to see xMOOCs becoming part of the “content” of network-oriented cMOOCs as well as formal closed-group classes, just another resource for learning specific skills or competences on a broader learning journey. This further emphasizes their set-like nature.
Personal Learning Environments and e-portfolios
Central to cMOOCs and widely used in many other situations is the concept of a personal learning environment, or PLE (Attwell, 2007). The PLE can take any technical form, from a collection of documents and links in Evernote to a purposebuilt space in an environment like Elgg, which provides a dashboard designed for this role. Echoing Rainie and Wellman’s concept of networked individualism (2012), this personal space acts as a hub to a world of connected people and objects that are of value in a learning context. We have built our own extension of the concept, the context-switcher used on the Landing (Dron et al., 2011), in order to allow for the variegated, discontinuous, and multifaceted nature of learning. Within any tab of an Elgg dashboard people can store files, link to blog posts, show RSS feeds, posts from particular groups, Twitter searches, and items tagged with metadata that may be of interest, supporting sets, nets, and groups in equal measure. However, the same functionality can be achieved in many alternative ways, even using something as simple as a paper notebook, though such tools make it considerably harder to aggregate and organize the dynamic flow of information from the network.
Related to the personal learning environment and often combined in the same toolset is the e-portfolio. Like PLEs, e-portfolios can be used to aggregate learning resources, and though the typical use case is to present these aggregations to others, they may equally be used in the learning process as tools for organizing and sense-making, as well as social networking. Elgg and Mahara are good examples of the genre, both straddling the PLE/e-portfolio border due to their capacity to selectively reveal things to different people in different ways, including entirely privately. As we move creakily toward an open and interoperable future, standards such as TinCan (scorm.com/tincan/) will enable us to assemble evidence of learning from diverse sources, probably augmented by badges of proficiency, which we may use to make sense of our own diverse learning and assemble it in different ways for different needs. In the language of TinCan, learning management systems become learning record stores (scorm.com/tincanoverview/), repositories of evidence and tools to manage learning journeys rather than tools for teaching.
What will the Future of Formal Learning look like?
The time has come to move on from the present and into the near or not-so-near future. It is difficult to predict if, let alone when the kinds of things we talk about in the next section may happen. This is not just because we do not have enough facts (and we don’t) nor because we cannot anticipate disruptive new technologies that have not yet been imagined (we can’t), but because this is an increasingly networked world, a complex adaptive system encompassing much of the planet in which cascades of change can happen very suddenly and with little warning (like the appearance of a black swan; Taleb, 2007), at least until viewed in retrospect.
We think that a tipping point is on the near horizon, but it may be decades away. Like all good prophets, we hedge our bets and tread with caution. What happens may bear no resemblance at all to what we predict, and we will definitely be wrong in places. Most notably, the momentum of medieval values in universities is huge and heavy: though the format may change here or there, there are massive organized forces that have, for centuries, proudly sustained equilibrium. A fundamental change to how we learn and accredit learning will certainly be resisted by the varied interconnections between educational institutions and the rest of society: from governments to tourist industries, banks to small businesses, schools to old-boy’s-club networks, our institutional forms are attached throughout the system. Academia will defend its position for the best possible reasons, and the worst. It is interesting that, whenever such issues are discussed within institutions, the default position is always “how will we deal with this threat?” or “how will we survive in this new environment?” without ever considering whether “we” the group should survive. Groups want to survive. The group forms that have sustained academia this far will not give up easily. With those provisos, we present our projections for what may be coming next.
Just-in-Time
As we already see for the small things of life, learning will happen more and more when it is needed, enabled by mobile technologies and beyond these on to forms of social learning that will increase as we become more trusting of and dependent upon the crowd and its productions. The focus will increasingly be on connecting the dots, sense-making, and taming the torrential stream of knowledge that is available to us.
Situated
Learning will occur in context—place, organization, project, and so on. Places to gather for specialist and large tools will still be necessary, though increasing use will be made of simulacra, immersive environments, and remotely controlled devices and experiments, and the tools of many trades are becoming smaller, cheaper, and more affordable. Genetic sequencing, for example, that a mere ten years ago took weeks or months and required massive and expensive equipment, can now be done with a chip and carried in a briefcase, with a turnaround measurable in hours. For many things that do require physical presence, learning will be carried out in situ, at the place where it has value.
Personalized
We already engage in personalized learning every time we do a Google search (your results will likely not be the same as mine) or look something up on Wikipedia, or find a lesson in the Khan Academy. In the future, collectives and curated sites will allow us to learn more easily what we want to learn, and to gain appropriate accreditation for it. Learners will be in control of how, what, and when they learn.
Disaggregated and Re-aggregated
The course, for which we will perhaps retain the term if not its denotation, may be anything from five minutes to five years in length. Accreditation will be through badges or similar certification systems. It is likely that the badgers themselves will be badged, perhaps using a collective that filters reputation rankings from multiple sources in order to identify the value of the provider, or that uses a PageRank-like algorithm to provide a weighted rating of value derived from the crowd’s opinions and actions. Interestingly, some of those achieving high rank will be individuals, some companies, some institutions, and perhaps, some collectives: karma ranking in Slashdot or endorsements from LinkedIn may well become a more important currency than certification by institutions or learned bodies.
Some providers will be individuals, some will be companies, and some may be universities. The collective may rank some individuals more highly than all the universities combined. Universities will compete to gain attention from such superstar accreditors, who may be employed part time or on a contractual basis by them. Institutions whose credibility rests on a path dependency stretching back to medieval times will no longer dominate the formal learning space. There will be diversity of provision. Publishers and libraries, pushing into markets to replace those lost as a result of the non-rival nature of their wares, will become providers and accreditors that compete directly with universities and colleges. This is already happening—Pearson University, for example, follows just such a model. Indeed, even individuals will begin offering credentials certified only by their individual reputations as David Wiley, one of the main instigators behind open badges, has already done. All will be swamped by the wealth of freely available, paid-for by advertising or sale of associated products, and app-based learning tools.
Teachers may or may not be employed by single institutions. For many, their particular skills may allow them to work in many places, paid according to the work they do. Others may prefer the security and benefits of a single institution: there will be scope for diversity. Physical location will seldom play a strong role, though some researchers and teachers may still be drawn to physical facilities and toolsets offered by institutions.
Distributed
No longer will institutions be virtual monopolies that lock individuals in to a limited set of fixed-length courses for the duration of a program. If institutions like universities do exist, they will be both hubs for other services and service providers for individuals and other hubs. Learners may choose institutions much as they choose cable network providers, for the range of channels they provide, though unlike these, there may be other more social and academic benefits, especially the presence of an academic community, the opportunity to engage in organized groups around topics and, at least in some cases, to provide expensive, dangerous, or complex facilities like laboratories, meeting areas, or large-scale computing devices. Face-to-face institutions will ubiquitously provide something similar to flipped classrooms, where learners engaged in learning from the distributed web of cyberspace may gather and explore what they have learned, perhaps using approaches like action learning sets (Revans, 1982) to provide motivation, depth, and diversity to their learning.
Disciplinarily Agnostic
Universities and colleges have, in the past, deliberately prepared students for particular occupations. While it is true that many subjects are non-vocational and have broad application, this is often because of their coarse chunking, which is a good thing if you are seeking generality. With the disaggregation of courses, people will acquire far more diverse skillsets, and continually build on them as needs emerge. The use of badges that relate to specific competences will allow a much more nuanced and realistic perspective on the skills that have been attained, and will make it simpler to cross disciplinary boundaries, as accreditation will no longer be bound to a single school or college.
Old School Tie-less
Because most individuals will no longer be directly affiliated with institutions, there will be little opportunity for groupthink and the lack of diversity often entailed by, for example, a Harvard or an Oxford education. While there are benefits for alumni of institutions, especially in terms of social networks and elite status, it is precisely the shared culture of thinking that gives academic value. The lack of diversity may, however, reduce the potential for acquiring rich cognitive toolboxes. Because formal learning will be occurring in a patchwork of sets, nets, and groups, learners will be exposed to a greater range and diversity of perspectives, heuristics, and ways of understanding the world. This will be beneficial to adopting a creative and multi-layered understanding of the world.
Open Research
When we, as researchers, publish a paper, a blog post, a research finding, or a comment on a blog, our readers will be able to award us badges. We will be awarded social capital for what we do, not by citations (that may frequently be critiques of our points of view) but by actual commendations. A PageRank-like algorithm will drive a collective that gives weightings to our commenders and thus calculates the value of our commendation. We see the potential beginnings of this operating already in the much wider base of citations used to calculate impact in Google Scholar, as opposed to more traditional World of Science citation rankings, albeit without the use of explicit commendation (Harzing, 2010). There are already crowdfunded research projects and education initiatives. This will become more common, allowing for a greater diversity of projects, including those that fail to attract funding at present because of their lack of obvious application—the long tail of the crowd (C. Anderson, 2004) has many interests. It will also benefit those that fall between research councils and cross broad disciplinary boundaries.
Wilder Speculations
There are many technologies on the horizon whose growth is influenced by increased communication and connectivity and whose repercussions are difficult to imagine. Genetic engineering, medicines, and increasing knowledge of health and safety may make us smarter and able to live longer. This is a trend that has continued unabated for over 100 years and shows no sign of stopping. A job for life when that active life may continue for 100 years is not a likely outcome. We will work longer, in more rewarding and varied ways, and we will take longer togrow up, have children later, and be exposed to ever richer and more challenging stimuli that make us smarter still (S. Johnson, 2006). Lifelong learning, formal, augmented, and informal, will be a way of life for all.
The primitive augmented reality tools like Google Glass or location-aware apps on our cellphones will become lighter, smarter, more responsive to our context and eventually disappear, becoming contact lenses, implants, or less invasive augmentations to our own bodies (Waterfield, 2012). More than ever, we will know about the world without having to keep that knowledge in our heads. These technologies will be networked. We will have instant access to the crowd, bringing new and powerful challenges to our sense of identity, our privacy, and how we deal with massive cognitive overload, but also remarkable opportunities to know one another better than ever before, to tap into the knowledge of the crowd, to learn from and with one another. Collectives will play a large role in helping us to cope with this, along with smarter AI that will understand context, language, and perhaps what we think. Man–machine interfaces already allow us to control machines, exchange thoughts and ideas, and even to know what others are thinking and dreaming, though not, at least for a while, as spookily as the popular media would have us believe. It is already an anachronism to learn by rote things that we can know in seconds by looking them up. As our tools for searching become integrated with everything we do and see, the ability to remember passages from Shakespeare or to know how to service the engine of our vehicle will seem quaint: they won’t go away, because we love to learn and love to explore, but they will become unnecessary, as much as the ability to operate a horse-drawn plough is unnecessary but, for some, rewarding still. What we will need to know is how to use this immensity of knowledge, how it fits together, what is useful and what is harmful, what is valuable and what is dross.
We think it highly unlikely that the pointless arms race with exam cheating in large-scale written examinations will continue under these conditions, and we confidently predict the end of this steam-age barbaric anomaly. It is not that the cheaters will win, but simply that everyone will realize, as they should already, that there is less than no point. The means of demonstrating competence will be authentic, targeted, and embedded in the social networks and traces that we leave as we learn. The skill of assembling such traces to demonstrate our competence to others will be crucial, and no doubt augmented by the crowd. Reflection and the skills of analysis and synthesis will be pre-eminent capacities in this not-sodistant future. Similarly, if there are still teachers of children, which we think may in some capacity exist, then they will not be the primary sources of information: children will have access to that as easily as they do. Instead, teachers will become not so much guides as co-travellers on the learning journey, helping children to accommodate their vastly enriched and interconnected worlds. If they run into difficulties, help will be just a thought away.
Most universities will not, ultimately, survive in their current form, though some will almost certainly be kept alive as we keep alive old farming traditions and hand-weaving. We will probably look at them wistfully and think that life was so much easier, so much finer, so much more refined in those days. And we will be wrong. The arguments between advocates of online and face-to-face learning will be largely forgotten, much as we have mostly forgotten the arguments between proponents of scrolls and supporters of bound books. All learning will be both online and situated in an ever-shifting context.
Though we have great hopes for technologies that enhance and augment our cognitive abilities, we do not hold out the hopes of Kurzweil (1990) and others that the singularity, the point at which machines become smarter than us in every way and start to create still-smarter machines, will allow us to transfer ourselves into machines, nor vice versa, at least not using any conceivable technology at the moment. However, the potential for change at that point, however it may play out, is unknowable and vast. We recommend the reader to the vast body of speculative fiction on that topic for better ideas than we can come up with, almost all of which are wrong—if only we knew which ones! With that, we have reached the end of what we can reasonably extrapolate from current trends and inventions.
Conclusion
We have traced social learning from the dim past, dwelt long in the present, and ended in the future. It has been a long story, but it is one that will continue at an exhilarating rate, branching in diverse ways that will continue to challenge and ennoble us, while humbling us. As crusty old academics writing skeuomorphically within the system we suggest is fading, in a format designed for a technology whose sun is setting, we will enjoy what we can of the ride, but will view it perhaps as outsiders, like the dinosaurs watching the asteroid streak across complacent skies. | textbooks/socialsci/Education_and_Professional_Development/Teaching_Crowds_-_Learning_and_Social_Media_(Dron_and_Anderson)/10%3A_The_Shape_of_Things_and_of_Things_to_Come.txt |
Figure 1.1.1 Learning in a digital age
Image: © CC Duncan Campbell, 2012
1.1.1 The digital age
In a digital age, we are surrounded, indeed, immersed, in technology. Furthermore, the rate of technological change shows no sign of slowing down. Technology is leading to massive changes in the economy, in the way we communicate and relate to each other, and increasingly in the way we learn. Yet our educational institutions were built largely for another age, based around an industrial rather than a digital era.
Thus teachers and instructors are faced with a massive challenge of change. How can we ensure that we are developing the kinds of graduates from our courses and programs that are fit for an increasingly volatile, uncertain, complex and ambiguous future? What should we continue to protect in our teaching methods (and institutions), and what needs to change?
To answer these questions, this book:
• discusses the main changes that are leading to a re-examination of teaching and learning;
• identifies different understandings of knowledge and the different teaching methods associated with these understandings;
• analyses the key characteristics of technologies with regard to teaching and learning;
• recommends strategies for choosing between media and technologies;
• recommends strategies for high quality teaching in a digital age.
In this chapter I set out some of the main developments that are forcing a reconsideration of how we should be teaching.
1.1.2 The changing nature of work
Of the many challenges that institutions face, one is in essence a good one, and that is increased demand, particularly for post-secondary education. Figure 1.1.2 below represents the extent to which knowledge has become an increasingly important element of economic development, and above all in job creation.
Figure 1.1.2: The knowledge component in the workforce
The figure is symbolic rather than literal. The pale blue circles representing the whole work force in each employment sector may be larger or smaller, depending on the country, as too will be the proportion of knowledge workers in that industry, but at least in developed countries and also increasingly in economically emerging countries, the knowledge component is growing rapidly: more brains and less brawn are required (see OECD, . Economically, competitive advantage goes increasingly to those companies and industries that can leverage gains in knowledge (. Indeed, knowledge workers often create their own jobs, starting up companies to provide new services or products that did not exist before they graduated.
From a teaching perspective the biggest impact is likely to be on technical and vocational instructors and students, where the knowledge component of formerly mainly manual skills is expanding rapidly. Particularly in the trades areas, plumbers, welders, electricians, car mechanics and other trade-related workers are needing to be problem-solvers, IT specialists and increasingly self-employed business people, as well as having the manual skills associated with their profession.
Artificial intelligence (AI) is another development that is already affecting the workforce. Routine work, whether clerical or manual, is being increasingly replaced by automation. Although all kinds of jobs are likely to be affected by increased automation and applications of AI, those in the workforce with lower levels of education are likely to be the most impacted. Those with higher levels of education are likely to have a better chance of finding work that machines cannot do as well – or even creating new work for themselves.
1.1.3 Knowledge-based workers
There are certain common features of knowledge-based workers in a digital age:
• they usually work in small companies (less than 10 people);
• they sometimes own their own business, or are their own boss; sometimes they have created their own job, which didn’t exist until they worked out there was a need and they could meet that need;
• they often work on contract or are self-employed, so they move around from one job to another fairly frequently (the gig economy);
• the nature of their work tends to change over time, in response to market and technological developments and thus the knowledge base of their work tends to change rapidly;
• they are digitally smart or at least competent digitally; digital technology is often a key component of their work;
• because they often work for themselves or in small companies, they play many roles: marketer, designer, salesperson, accountant/business manager, technical support, for example;
• they depend heavily on informal social networks to bring in business and to keep up to date with current trends in their area of work;
• they need to keep on learning to stay on top in their work, and they need to manage that learning for themselves;
• above all, they need to be flexible, to adapt to rapidly changing conditions around them.
It can be seen then that it is difficult to predict with any accuracy what many graduates will actually be doing ten or so years after graduation, except in very broad terms. Even in areas where there are clear professional tracks, such as medicine, nursing or engineering, the knowledge base and even the working conditions are likely to undergo rapid change and transformation over that period of time. However, we shall see in Chapter 1 Section 2 that it is possible to predict many of the skills they will need to survive and prosper in such an environment.
This is good news for the higher or post-secondary education sector overall (universities and colleges) as the knowledge and skill levels needed in the workforce increases. It has resulted in a major expansion of post-secondary education to meet the demand for knowledge-based work and higher levels of skill. The post-secondary enrolment rate of 19-year-olds across all Canadian provinces increased steadily from 53% in 2001 to 64% in 2014, equivalent to a 21% rise over the 13-year period (Frenette, 2017). This means more students for universities and colleges, even where population trends are flat or even declining.
Figure 1.1.3 A video animator: a typical knowledge worker. Photograph: Elaine Thompson/Associated Press, 2007.
References
OECD (2013a) OECD Skills Outlook: First Results from the Survey of Adult Skills Paris: OECD
OECD (2013b) Competition Policy and Knowledge-Based Capital Paris: OECD
Frenette, M. (2017) Postsecondary Enrolment by Parental Income: Recent National and Provincial Trends Ottawa: Statistics Canada
Activity 1.1 Thinking about skills
1. What kind of jobs are graduates in your subject discipline likely to get? Can you describe the kinds of skills they are likely to need in such a job? To what extent has the knowledge and skills component of such work changed over the last 20 years?
2. Look at the family members and friends outside your academic or educational field. What kind of knowledge and skills do they need now that they didn’t need when they left school or college? (You may need to ask them this!)
3. Exactly how are you assisting your students develop such skills through your teaching? Is this centre or peripheral to your work? Is this part of your job – or someone else’s?
There is no feedback on this activity. | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/01%3A_Fundamental_Change_in_Education/01.1%3A_Structural_changes_in_the_economy%3A_the_growth_of_a_knowledge_society.txt |
Figure 1.2.1 Using social media for communication is an essential skill for a digital age
1.2.1 The growing importance of skills development
Knowledge involves two strongly inter-linked but different components: content and skills. Content includes facts, ideas, principles, evidence, and descriptions of processes or procedures. Most instructors, at least in universities, are well trained in content and have a deep understanding of the subject areas in which they are teaching. Expertise in skills development though is another matter. The issue here is not so much that instructors do not help students develop skills – they do – but whether these intellectual skills match the needs of knowledge-based workers, and whether enough emphasis is given to skills development within the curriculum.
1.2.2 The needs of a digital society
Prediction is always risky, but usually the big trends in the future can already be seen in the present. The future will merely magnify these current conditions, or current conditions will result in a transformation that we can see coming but is not here yet. Examples are many:
• the Internet of Things where almost everything is digitally connected
• autonomous vehicles and transportation
• massive amounts of data about our personal lives being collected and analysed to anticipate/predict/influence our future behaviour
• automation replacing and/or transforming human work and leisure
• state agencies and/or commercial oligopolies controlling access to and use of data
• lack of transparency, corruption of messaging, and magnification of these distortions, in digital communications.
One thing is clear. We can either as individuals throw up our hands and leave all these developments to either state or commercial entities to manage in their own interests, or we can try to prepare ourselves so that we can influence or even control how these developments are managed, for the greater good.
This is what I mean when I talk about developing 21st century skills, or preparing for a digital society. We have a responsibility for ensuring our students are educated sufficiently so that they understand these issues and have the means by which to address them. This is a responsibility of every educator, because it affects all areas of knowledge.
For instance the science professor needs to instill in her students an ability to identify reliable and unreliable sources of scientific data, and an ability to apply that knowledge in ethical ways that benefit mankind. This is a particularly important responsibility for those teaching computer sciences. We need to teach about the dangers of unintended or unknown consequences of artificial intelligence applications and of automated analyses of mass data, potential biases in algorithms, and the need to audit and adjust automated procedures to avoid unforeseen but harmful consequences before they do damage.
Digital (rather than purely online) learning has a critical role to play, because in order to develop these skills our students’ learning itself needs to be digitally embedded. Only by mastering technology can we control it.
1.2.3 What skills?
The skills required in a knowledge society include the following (adapted from Conference Board of Canada, 2014):
• communications skills: as well as the traditional communication skills of reading, speaking and writing coherently and clearly, we need to add social media communication skills. These might include the ability to create a short YouTube video to capture the demonstration of a process or to make a sales pitch, the ability to reach out through the Internet to a wide community of people with one’s ideas, to receive and incorporate feedback, to share information appropriately, to identify trends and ideas from elsewhere;
• the ability to learn independently: this means taking responsibility for working out what you need to know, and where to find that knowledge. This is an ongoing process in knowledge-based work, because the knowledge base is constantly changing. Incidentally I am not talking here necessarily of academic knowledge, although that too is changing; it could be learning about new equipment, new ways of doing things, or learning who are the people you need to know to get the job done;
• ethics and responsibility: this is required to build trust (particularly important in informal social networks), but also because generally ethical and responsible behaviour is in the long run more effective in a world where there are many different players, and a greater degree of reliance on others to accomplish one’s own goals;
• teamwork and flexibility: although many knowledge workers work independently or in very small companies, they depend heavily on collaboration and the sharing of knowledge with others in related but independent organizations. In small companies, it is essential that all employees work closely together, share the same vision for a company and help each other out. In particular, knowledge workers need to know how to work collaboratively, virtually and at a distance, with colleagues, clients and partners. The ‘pooling’ of collective knowledge, problem-solving and implementation requires good teamwork and flexibility in taking on tasks or solving problems that may be outside a narrow job definition but necessary for success;
• thinking skills (critical thinking, problem-solving, creativity, originality, strategizing, for example): of all the skills needed in a knowledge-based society, these are the most important. Businesses increasingly depend on the creation of new products, new services and new processes to keep down costs and increase competitiveness. Also, it is not just in the higher management positions that these skills are required. Trades people in particular are increasingly having to be problem-solvers rather than following standard processes, which tend to become automated. Anyone dealing with the public in a service function must identify needs and find appropriate solutions. Universities in particular have always prided themselves on teaching such intellectual skills, but the move to larger classes and more information transmission, especially at the undergraduate level, undermines this assumption;
• digital skills: most knowledge-based activities depend heavily on the use of technology. However the key issue is that these skills need to be embedded within the knowledge domain in which the activity takes place. This means for instance real estate agents knowing how to use geographical information systems to identify sales trends and prices in different geographical locations, welders knowing how to use computers to control robots examining and repairing pipes, radiologists knowing how to use new technologies that ‘read’ and analyze MRI scans. Thus the use of digital technology needs to be integrated with and evaluated through the knowledge-base of the subject area;
• knowledge management: this is perhaps the most over-arching of all the skills. Knowledge is not only rapidly changing with new research, new developments, and rapid dissemination of ideas and practices over the Internet, but the sources of information are increasing, with a great deal of variability in the reliability or validity of the information. Thus the knowledge that an engineer learns at university can quickly become obsolete. There is so much information now in the health area that it is impossible for a medical student to master all drug treatments, medical procedures and emerging science such as genetic engineering, even within an eight year program. Thus knowledge management is the key skill in a knowledge-based society: how to find, evaluate, analyze, apply and disseminate information, within a particular context. Above all students need to know how to validate or challenge sources of information. Effective knowledge management is a skill that all graduates will need to employ long after graduation.
In 2018, the Royal Bank of Canada issued a report, called ‘Humans Wanted.’ This was based on an analysis of big data derived from job postings over a 12 month period on LinkedIn, in which the actual skills being requested by employers were identified and analysed, and from which an analysis of the demand for different types of labour was conducted.
The main conclusion of the report was that there will be plenty of jobs in the future, but they will require different skills from those generally required at the present. In particular, many of the new skills needed will be what are perhaps confusingly called soft skills, such as attentive listening, critical thinking, digital fluency, active learning, etc. (confusing, because these ‘soft skills’ are often as difficult to cultivate as ‘hard skills’.) These are skills that automation and AI cannot easily replicate or replace but which will be needed in the new digital economy. The Royal Bank identified the following as key skills that will be in high demand between 2018 and 2023 (dark blue = very important; lighter blue = important):
Figure 1.2.2 From ‘Humans Wanted’, Royal Bank of Canada, 2018
Two of the main conclusions from the Royal Bank report were as follows:
• Canada’s education system, training programs and labour market initiatives are inadequately designed to help Canadian youth navigate this new skills economy.
• Canadian employers are generally not prepared, through hiring, training or retraining, to recruit and develop the skills needed to make their organizations more competitive in a digital economy.
1.2.4 Developing skills
What methods of teaching are most likely to develop soft skills? In fact, we can learn a lot from research about skills and skill development (see, for instance, Fischer, 1980, Fallow and Steven, 2000):
• skills development is relatively context-specific. In other words, skills need to be embedded within a knowledge domain. For example, problem solving in medicine is different from problem-solving in business. First of all, of course, the content base used to solve problems is different. Less well understood though is that somewhat different processes and approaches are used to solve problems in these domains (for instance, decision-making in medicine tends to be more deductive, business more intuitive; medicine is more risk averse, business is more likely to accept a solution that will contain a higher element of risk or uncertainty). Embedding skills within a particular context such as a subject discipline is perhaps the biggest challenge for educational institutions in a digital age. How well does an ability to think critically about English literature transfer to other areas of critical thinking, such as political analysis or assessing the behaviour of a workplace colleague? In many cases, some elements of these soft skills do transfer well but other parts are more context specific. More attention needs to be paid to what is known about the transfer of skills, based on research, and to ensuring this evidence affects the way we teach.
• learners need practice – often a good deal of practice – to reach mastery and consistency in a particular skill;
• skills are often best learned in relatively small steps, with ‘jumps’ increasing as mastery is approached;
• learners need feedback on a regular basis to learn skills quickly and effectively; immediate feedback is usually better than late feedback;
• although skills can be learned by trial and error without the intervention of a teacher, coach, or technology, skills development can be greatly enhanced or speeded up with appropriate interventions, which means adopting appropriate teaching methods and technologies for skills development;
• we shall see later that although content can be transmitted equally effectively through a wide range of media, skills development is much more tied to specific teaching approaches and technologies.
What are the implications of this for not only teaching methods, but also curriculum design? It is worth remembering that unlike competencies, many ‘high-level’ soft skills such as critical thinking are cumulative and do not have a clear end-point. Serena Williams keeps winning not because she continues to get faster and stronger than younger players, but because she continues to hone her skills (including strategy) to a level that compensates for her diminishing strength and speed.
Soft skills need to be developed over a program (indeed a lifetime) rather than in a single course. How do we identify then how to build critical thinking skills for example from first year through to graduation in a particular discipline? How does the development of skills in later stages build on work done earlier in a program?
1.2.5 Measuring skills
Another challenge is measuring skills. I was once questioned by a colleague when I said my students were learning to think critically.
‘How do you know?’ he said.
My answer was: ‘I know it when I see it in their assessments.’
‘But how will your students know what you are looking for if you can’t describe it in advance?’
The Higher Education Quality Council of Ontario (HEQCO) published a report in 2018 that claimed to be ‘one of the first major attempts to measure employment-related skills in university and college students on a large scale.’ The second study used a test designed to evaluate students’ ability to analyse evidence, understand implications and consequences, and develop valid arguments.
The HEQCO study concluded that final-year students had somewhat higher scores in literacy and numeracy than their first-year counterparts, although there was considerable variation among programs, but little difference between the test scores of incoming and graduating students in critical-thinking abilities, although critical thinking ability too showed considerable variation among programs.
There are a number of possible criticisms of this study. One of the challenges that the HEQCO study faced was finding valid and reliable ways to assess soft skills. The first study measured literacy, numeracy and problem-solving abilities of adults using everyday scenarios.Why assess these skills outside the knowledge domains in which they were taught, given the importance of context? Were the measurements sensitive enough to really discriminate differences in skill development over time?
Nevertheless, it is worrying that HEQCO found that after four years of post-secondary study there was no noticeable difference in critical thinking skills. Is this because this is not being well taught, or because the tests used were not valid? Any attempt to identify learning outcomes involving skills requires consideration from the beginning of how these skills can validly be assessed. Instructors should not complain about HEQCO’s assessment methods if they cannot justify their own methods of identifying and assessing skills.
1.2.6 Skills and learning outcomes
The Royal Bank of Canada and the HEQCO studies both highlight that it is becoming increasingly important to define learning outcomes in terms of skills acquisition. Both these are valuable studies that identify some of the issues around developing the knowledge and skills that students will need to succeed, not just in the workforce, but in life generally in the last three quarters of this century. However, the two reports have barely touched the tip of this particular iceberg. Neither for instance attempted to suggest how students can develop these skills or what instructors need to do to help students develop such skills.
When developing curricular, in terms of deciding not only what but also how to teach, we need to ask the following questions:
(a) are programs identifying clearly the learning outcomes expected from a program of study?
(b) do these learning outcomes sufficiently take into account skills as well as content/topics?
(c) are these learning outcomes relevant for a digital society?
In other words, we have a major pedagogical challenge in several parts:
• identifying the most important soft skills that students will need (although the RBC report goes a little way in that direction)
• identifying the best way to teach such soft skills
• assessing students’ ability in soft skills (although the HEQCO report similarly goes a little way in that direction)
• identifying the extent to which soft skills are generalisable.
The key point here is that content and skills are tightly related and as much attention needs to be given to skills development as to content acquisition to ensure that learners graduate with the necessary knowledge and skills for a digital age.
1.2.7 Rethinking teaching and learning
These are essentially curriculum and pedagogical issues. It means rethinking not only the curriculum and how we teach it, but also the role that technology can play in developing such skills. How can technology increase empathy and understanding (for example, through creating virtual environments or simulations where students play the role of others)? How can technology be used to provide scenarios that enable skills development and testing in a safe environment? How can technology be used to enable students to solve real world problems?
There are a million possible answers to such questions and they need to be answered by instructors and teachers – and by learners – with deep understanding of their subject matter. But subject knowledge alone is not enough if we are to make the last three quarters of the 21st century a time when all people can thrive and feel free.
Chapters 2 and 3 explore different methods of teaching and will look at how well these methods accommodate skills development. But in the next section I discuss the dangers of tying skills development too closely to the immediate needs of the labour market.
References
The Conference Board of Canada (2014) Employability Skills 2000+ Ottawa ON: Conference Board of Canada
Fallow, S. and Stevens, C. (2000) Integrating Key Skills in Higher Education: Employability, Transferable Skills and Learning for Life London UK/Sterling VA: Kogan Page/Stylus
Finnie, R. et al. (2018) Measuring Critical-thinking Skills of Postsecondary Students Toronto ON: HEQCO
Fischer, K.W. (1980) A Theory of Cognitive Development: The Control and Construction of Hierarchies of Skills Psychological Review, Vol. 84, No. 6
Royal Bank of Canada (2018) Humans Wanted Toronto ON: Royal Bank of Canada
Weingarten, H. et al. (2018) Measuring Essential Skills of Postsecondary Students: Final Report of the Essential Adult Skills Initiative Toronto ON: HEQCO
For my comments on why skills development is so important in a digital age, click on the podcast below
An audio element has been excluded from this version of the text. You can listen to it online here: https://pressbooks.bccampus.ca/teachinginadigitalagev2/?p=34
Activity 1.2 What skills are you developing in your students? Part 1
1. Write down a list of skills you would expect students to develop as a result of studying your courses.
2. Compare these skills to the ones listed above. How well do they match?
3. What do you do as an instructor that enables students to practice or develop the skills you have identified?
There is no feedback provided for this activity, but see podcast above.. | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/01%3A_Fundamental_Change_in_Education/01.2%3A_The_skills_needed_in_a_digital_age.txt |
Figure 1.3.1 Knowledge workers
Image: Phil Whitehouse, 2009. Retrieved from https://www.flickr.com/photos/philli...ca/3344142642/.
However, there is a real danger in tying university, college and schools programs too closely to immediate labour market needs. Labour market demand can shift very rapidly, and in particular, in a knowledge-based society, it is impossible to judge what kinds of work, business or trades will emerge in the future. For instance, who would have predicted 20 years ago that one of the largest companies in the world in terms of stock market valuation would emerge from finding ways to rank the hottest girls on campus (which is how Facebook started)?
The focus on the skills needed in a digital age raises questions about the purpose of universities in particular, but also schools and two year community colleges to some extent. Is their purpose to provide ready-skilled employees for the work-force? Is it really the job of historians or physicists to teach skills such as attentive listening, time management or social perceptiveness?
Certainly the rapid expansion in higher education is largely driven by government, employers and parents wanting a workforce that is employable, competitive and if possible affluent. Indeed, preparing professional workers has always been one role for universities, which have a long tradition of training for the church, law and much later, government administration. The goal here is to ensure that as well as a deep understanding of the content and core values of a subject discipline, students can also develop skills that enable them to apply such knowledge in appropriate contexts.
Secondly, focusing on the skills required for a knowledge-based society (often referred to as 21st century skills) merely reinforces the kind of learning, especially the development of intellectual skills, for which universities have taken great pride in the past. Indeed in this kind of labour market, it is critical to serve the learning needs of the individual rather than specific companies or employment sectors. To survive in the current labour market, learners need to be flexible and adaptable, and should be able to work just as much for themselves as for corporations that increasingly have a very short operational life. The challenge then is not re-purposing education, but making sure it meets that purpose more effectively.
Thirdly, enabling students to live well and to feel some measure of control in a technology-rich society is surely the responsibility of every educator. For instance, all students, whatever their discipline, need to know how to find, evaluate, analyse and apply information within their specific subject discipline. With so much content of varying quality now available at one’s fingertips, such skills are essential for a healthy society.
Thus in some cases it is a language issue: instructors may be achieving some of these ’21st century skills’ such as critical thinking within the requirements of a specific discipline without using this terminology (for example, ‘compare and contrast…’ is a critical thinking activity). However, the HEQCO study (Weingarten et al., 2018) indicates that high-level soft skills are hard to measure and probably need to be defined and communicated more clearly and purposefully by instructors. In particular, development of such skills need to be considered at a program level so instructors can define what level of skill they expect of students when they arrive, and to what level that skill has been increased or improved by the end of a course or program.
A good example of this is from the Faculty of Computer Science at Dalhousie University. The department developed a map showing the inter-relatedness between specific learning outcomes, course content, and course and learning outcome sequencing, so that each instructor understood what level of skills and outcomes students would have from previous courses, and could identify what levels of skills they were passing on when students left their course. One result of this was to move the theory courses from the fourth year to the first year, as this helped students in the later stages of the program.
These activities do not challenge in any way core disciplinary values, or make universities or colleges merely preparatory schools for business, but they do ensure that students leave with skills that prepare them well for living in a very challenging age.
Reference
Weingarten, H. et al. (2018) Measuring Essential Skills of Postsecondary Students: Final Report of the Essential Adult Skills Initiative Toronto ON: HEQCO
Activity 1.3: What are the skills you are developing? Part 2
The new Ontario provincial government in 2019 announced that it would link funding of its post-secondary institutions to ‘performance outcomes’. Institutions would be encouraged to suggest their own performance measures.
Your institution has decided to focus on the development of ’21st century skills’ as a ‘key performance indicator’, and is asking all its academic departments to list the ‘core’ skills that their programs are developing.
If you were asked this, what would you suggest from looking not just at your teaching but the teaching of the department or program as a whole? And what evidence would need to be provided to show such skills are being achieved by your students?
Would having to do this be an infringement of your academic freedom?
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Figure 1.4.1 Harvard University
In the age of constant connectedness and social media, it’s time for the monolithic, millennium-old, ivy-covered walls to undergo a phase change into something much lighter, more permeable, and fluid.
Anya Kamenetz, 2010
Although this book is aimed at teachers and instructors in schools and colleges as well as universities, I want to look particularly at how the digital age is impacting on universities. There is a widely held belief – even among those who have benefited from fine degrees at prestigious universities – that universities are out of touch, that academic freedom is really about protecting professors in a comfortable career that doesn’t require them to change, and that the entire organization of the academy is better left to its medieval past: in other words, universities are an artifact of the past and something new needs to replace them.
Nevertheless, there are very good reasons why universities have been around for more than 800 years, and are likely to remain relevant well into the future. Universities are deliberately designed to resist external pressure. They have seen kings and popes, governments and business corporations, come and go, without any of these external forces fundamentally changing the nature of the institution. Universities pride themselves on their independence, their freedom, and their contribution to society. So let’s start by looking, very briefly, at these core values, because any change that really threatens these core values is likely to be strongly resisted from professors and instructors within the institution.
Universities are fundamentally about the creation, evaluation, maintenance and dissemination of knowledge. This role in society is even more important today than in the past. For universities to perform that role adequately, though, certain conditions are necessary. First they need a good deal of autonomy. The potential value of new knowledge in particular is difficult to predict in advance. Universities provide society with a safe way of gambling on the future, by encouraging innovative research and development that may have no immediate apparent short-term benefits, or may lead to nowhere, without incurring major commercial or social loss. Another critical role is the ability to challenge the assumptions or positions of powerful agencies outside the university, such as government or industry, when these seem to be in conflict with evidence or ethical principles or the general good of society.
Perhaps even more importantly, there are certain principles that distinguish academic knowledge from everyday knowledge, such as rules of logic and reasoning, the ability to move between the abstract and the concrete, ideas supported by empirical evidence or external validation (see for instance, Laurillard, 2001). We expect our universities to operate at a higher level of thinking than we as individuals or corporations can do in our everyday lives.
One of the core values that has helped to sustain universities is academic freedom. Academics who ask awkward questions, who challenge the status quo, who provide evidence that contradicts statements made by government or corporations, are protected from dismissal or punishment within the institution for expressing such views. Academic freedom is an essential condition within a free society. However, it also means that academics are free to choose what they study, and more importantly for this book, how best to communicate that knowledge. University teaching then is bound up with this notion of academic freedom and autonomy, even though some of the conditions that protect that autonomy, such as tenure or a job for life, are increasingly under pressure.
I make this point for one reason and one reason alone. If universities are to change to meet changing external pressures, this change must come from within the organization, and in particular from the professors and instructors themselves. It is the faculty that must see the need for change, and be willing to make those changes themselves. If government or society as a whole tries to enforce changes from outside, especially in a way that challenges the core values of a university such as academic freedom, there is a grave risk that the very thing that makes universities a unique and valuable component of society will be destroyed, thus making them less rather than more valuable to society as a whole. However, this book will provide many reasons why it is also in the best interests of not only learners but instructors themselves to make changes, in terms of managing workload and attracting extra resources to support teaching.
Schools and two-year colleges are in a somewhat different position. It is easier (although not that easy) to impose change from above or through forces from outside the institution, such as government. However, as the literature on change management clearly indicates (see, for instance, Weiner, 2009), change occurs more consistently and more deeply when those undergoing change understand the need for it and have a desire to change. Thus in many ways, schools, two year colleges and universities face the same challenge: how to change while preserving the integrity of the institution and what it stands for.
Activity 1.4 Change and continuity
1. Do you think that universities are irrelevant today? If not, what alternatives are there for developing learners with the knowledge and skills needed in a digital age?
2. What are your views on the core values of a university? How do they differ from the ones outlined here?
3. Do you think schools, colleges and/or universities need to change they way they teach? If so, why, and in what way? How could this best be done without interfering with academic freedom or other core values of educational institutions?
There are no right or wrong answers to these questions but you may want to return to your answers after reading the whole chapter. | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/01%3A_Fundamental_Change_in_Education/01.4%3A_Change_and_continuity.txt |
Figure 1.5.1 More students means bigger lecture classes
Governments in different provinces, states and countries have varied in their response to the need for more highly educated people. Some (as in Canada) have increased state funding to post-secondary education institutions to an extent that matches or even exceeds the increase in student numbers. Others (particularly in the USA, Australia, and England and Wales) have relied mainly on steep cuts in direct state funding for operating budgets, combined with massive increases in tuition fees.
Whatever the government strategy, in every university and college I visit, I am told instructors have more students to teach, class sizes are getting larger, and as a result, more and more classes are just lectures with little interaction. Indeed, statistics support this argument. According to Usher (2013), the overall full-time faculty:full time student ratio in Canadian universities increased from 1:18 in 1995 to 1:22 by 2011, despite a 40 per cent increase in per student funding (after inflation). In fact, a 1:22 ratio means much larger class sizes, because in universities full-time faculty spend only a notional 40 per cent of their time on teaching, and students may take up to 10 different courses a year. The fact is that especially in first and second year classes, class sizes are extremely high. For instance, one Introductory Psychology class in a mid-sized Canadian university has one full-time professor responsible for over 3,000 students.
Tuition fees though are very visible, so many institutions or government jurisdictions have tried to control increases in tuition fees, despite cuts in operating grants, resulting in increased full time instructor:student ratios. Also, as a result of higher tuition fees and increased student debt to finance university and college education, students and parents are becoming more demanding, more like customers than scholars in an academic community. Poor teaching in particular is both visible and less and less acceptable to students paying high tuition fees.
The general complaint from faculty is that government or the institutional administration has not increased funding for faculty in proportion to the increase in student numbers. In fact, the situation is much more complicated than that. Most institutions that have expanded in terms of student numbers have handled the expansion through a number of strategies:
• hiring more contract/sessional lecturers at lower salaries than tenured faculty
• greater use of teaching assistants who themselves are students
• increasing class sizes
• increasing faculty workload.
All of these strategies tend to have a negative impact on quality, if the methods of teaching otherwise remain unchanged.
Contract instructors are cheaper to employ than full time professors but they do not usually have the same roles such as choice of curriculum and reading materials as tenured faculty, and although often well qualified academically, the relatively temporary nature of their employment means that their teaching experience and their knowledge of students are lost when their contracts end. However, of all the strategies, this is likely to have the least negative impact on quality. Unfortunately though it is also the most expensive for institutions.
Teaching assistants may be no more than a couple of years ahead in their studies than the students they are teaching, they are often poorly trained or supervised with regard to teaching, and sometimes, if they are foreign students (as is often the case), their English language skills are poor, making them sometimes difficult to understand. They tend to be used to instruct parallel sections of the same course, so that students studying the same course may have widely different levels of instruction. Employing and paying teaching assistants can be directly linked to the way that post-graduate research is being funded by government agencies.
The increase in class size has tended to result in much more time being devoted to lectures and less time to small group work. Lectures are in fact a very economical way of increasing class size (provided that the lecture halls are large enough to accommodate the extra students). The marginal cost of adding an extra student to a lecture is small, since all students are receiving the same instruction. However, as numbers increase, faculty resort to more quantitative and less flexible forms of assessment, such as multiple-choice questions and automated assessment. Perhaps more importantly, student interaction with faculty decreases rapidly as numbers increase, and the nature of the interaction tends to flow between the instructor and an individual student rather than between students interacting as a group. Research (Bligh, 2000) has shown that in lectures with 100 or more students, less than ten students will ask questions or provide comments over the course of a semester. The result is that lectures tend to focus more heavily on the transmission of information as class size increases, rather than on exploration, clarification or discussion (see Chapter 3, Section 3 for a more detailed analysis of the effectiveness of lectures).
Increasing faculty teaching load (more courses to be taught) is the least common of the four strategies, partly because of faculty resistance, sometimes manifesting itself in collective agreement negotiations. Where increased faculty teaching load does occur, quality again is likely to suffer, as faculty put in less preparation time per class and less time for office hours, and resort to quicker and easier methods of assessment. This inevitably results in larger classes if full-time faculty are teaching less but doing more research. However, increased research funding results in more post-graduate students, who can supplement their income as teaching assistants. As a result there has been a major expansion in the use of teaching assistants for delivering lectures. However, in many Canadian universities, full-time faculty teaching load has been going down (Usher, 2013), leading to even larger class sizes for those that do teach.
In other employment sectors, increased demand does not necessarily result in increased cost if that sector can be more productive. Thus government is increasingly looking for ways to make higher education institutions more productive: more and better students for the same cost or less (see for instance Kao, 2019). Up to now, this pressure has been met by institutions over a fairly long period of time by gradually increasing class size, and using lower cost labour, such as teaching assistants, but there becomes a point fairly quickly where quality suffers unless changes are made to the underlying processes, by which I mean the way that teaching is designed and delivered.
Another side effect of this gradual increase in class size without changes in teaching methods is that faculty and instructors end up having to work harder. In essence they are processing more students, and without changing the ways they do things, this inevitably results in more work. Faculty usually react negatively to the concept of productivity, seeing it as industrializing the educational process, but before rejecting the concept it is worth considering the idea of getting better results without working as hard but more smartly. Could we change teaching to make it more productive so that both students and instructors benefit?
References
Bligh, D. (2000) What’s the Use of Lectures? San Francisco: Jossey-Bass
Kao, J. (2019) Ontario’s 2019 budget reveals plan to significantly tie university funding to performance outcomesThe Varsity, University of Toronto
Usher, A. (2013) Financing Canadian Universities: A Self-Inflicted Wound (Part 5) Higher Education Strategy Associates, September 13
Activity 1.5 How much wriggle room do you have?
1. Are you in general satisfied with your working conditions regarding teaching? If not, what are your main frustrations?
2. What practical solutions (taking into account the financial situation of your institution, student needs, and the time you have available for teaching) could perhaps alleviate some of the frustration?
3. If you could change the way you teach, what would be the main benefits to both yourself and your students? What would need to change for this to happen?
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Figure 1.6.1 More diverse students
Image: © greatinternational students.blogspot.com, 2013
1.6.1 Greater diversity of students
Probably nothing has changed more in higher education over the last 50 years than the students themselves. In ‘the good old days’, when less than a third of students from high schools went on to higher education, most came from families who themselves had been to university or college. They usually came from wealthy or at least financially secure backgrounds. Universities in particular could be highly selective, taking students with the best academic records, and thus those most likely to succeed. Class sizes were smaller and faculty had more time to teach and less pressure to do research. Expertise in teaching, while important, was not as essential then as now; good students were in an environment where they were likely to succeed, even if the prof was not the best lecturer in the world. This ‘traditional’ model still holds true for most elite private universities such as Harvard, MIT, Stanford, Oxford and Cambridge, and for a number of smaller liberal arts colleges. But for the majority of publicly funded universities and two year community colleges in most developed countries, this is no longer the case (if it ever was).
The student base has become much more diverse. For instance, in British Columbia, roughly two‐thirds of the full Grade 8 school cohort of 2007/2008 (67%) entered B.C. public post‐secondary education by Fall 2014 (Heslop, 2016). As state jurisdictions push institutions to participation rates of around 70 per cent going on to some form of post-secondary education (Ontario, 2011), institutions must reach out to previously underserved groups, such as ethnic minorities (particularly Afro-American and Latinos in the USA), new immigrants (in most developed countries), aboriginal students in Canada, and students with English as a second language. Governments are also pushing universities to take more international students, who can be charged full tuition fees or more, which in turn adds to the cultural and language mix. In other words, post-secondary institutions are expected to represent the same kind of socio-economic and cultural diversity as in society at large, rather than being institutions reserved for an elite minority.
We shall also see that in many developed countries, university and college students are older than they used to be and are no longer full-time students dedicated only to lots of study and some fun (or vice versa). The increasing cost of tuition fees and living expenses forces many students now to take part-time work, which inevitably conflicts with regular classroom schedules, even if the students are formally classified as full-time students. As a result students are taking longer to graduate. In the USA, the average completion time for a four year bachelor degree is now 5.1 years (Shapiro, et al., 2016).
1.6.2 The lifelong learning market
The Council of Ontario Universities (2012) noted that students NOT coming direct from high school now constitute 24% of all new admissions, and enrolments from this sector are increasing faster than those from students coming direct from high schools. Perhaps more significantly, many graduates are returning later in their careers to take further courses or programs, in order to keep up in their ever-changing knowledge domain. Many of these students are working full-time, have families and are fitting their studies around their other commitments.
Figure 1.6.2 Lifelong learners are an increasingly important market for higher education
Image: © Evolllution.com, 2013
Yet it is economically critical to encourage and support such students, who need to remain competitive in a knowledge-based society. especially as with falling birthrates and longer lives, in some jurisdictions lifelong learners, students who have already graduated but are coming back for more study, will soon exceed the number of students coming directly from high school. Thus at the University of British Columbia in Canada, the mean age of all its graduates students is now 31, and more than one third of all students are over 24 years old. There is also an increase in students transferring from two year colleges to universities – and vice versa. For instance, in Canada, at the British Columbia Institute of Technology more than 20 per cent of its new enrolments each year already have a university degree.
1.6.3 Digital natives
Another factor that makes students somewhat different today is their immersion in and facility with digital technology, and in particular social media: instant messaging, Twitter, video games, Facebook, and a whole host of applications (apps) that run on a variety of mobile devices such as iPads and mobile phones. Such students are constantly ‘on’. Most students come to university or college immersed in social media, and much of their life evolves around such media. Some commentators such as Mark Prensky (2001) argue that digital natives think and learn fundamentally differently as a result of their immersion in digital media.
Many instructors too often see such technology as a distraction. Attentive listening is impossible if students are scrolling through videos or Facebook pages. Many instructors would like to ban all mobile phones and tablets from their classes. However, a ban on mobile phones is an attempt to deny the reality of living in a digital age. We should be educating our students in the appropriate use of everyday technology for learning and social purposes, not trying to deny the existence of the technology.
Instead we should be encouraging students to use their technological devices to find, analyse, evaluate and apply their knowledge. This means giving them engaging tasks in class time that require the use of their phones. Yes, they will probably use their device to text other students but then that can be also used for group work and social learning. In particular, mobile phones can be used to support the learning of higher level skills, such as problem solving and critical thinking.
But this means providing criteria and procedures for students that enable their learning – and also learning when they need to put their phones down and switch off. These are skills and knowledge that are essential for life in today’s society and it is irresponsible for the education system to ignore such needs. Students expect to use social media in all other aspects of their life. Why should their learning experience be different? We shall explore this further in Chapter 8, Section 6.
1.6.4 From elitism to success
Many older faculty still pine for the good old days when they were students. Even in the 1960s, when the Robbins’ Commission recommended an expansion of universities in Britain, the Vice-Chancellors of the existing universities moaned ‘More means worse.’ However, for public universities, the Socratic ideal of a professor sharing their knowledge with a small group of devoted students under the linden tree no longer exists, except perhaps at graduate level, and is unlikely ever to return to public post-secondary institutions (except perhaps in Britain, where the Conservative government seems to be dialling back the clock to the 1950s). The massification of higher education has, to the alarm of traditionalists, opened up the academy to the great unwashed. However, the massification of higher education is needed as much for economic reasons as for social mobility.
The implications of these changes in the student body for university and college teaching are profound. At one time, German math professors used to pride themselves that only five to ten per cent of their students would succeed in their exams. The difficulty level was so high that only the very best passed. A tiny completion rate showed how rigorous their teaching was. It was the students’ responsibility, not the professors’, to reach the level required. That may still be the goal for top level research students, but we have seen that today universities and colleges have a somewhat different purpose, and that is to ensure, as far as possible, that as many students as possible leave university appropriately qualified for life in a knowledge-based society. We can’t afford to throw away the lives of 95 per cent of students, either ethically or economically. In any case, governments are increasingly using completion rates and degrees awarded as key performance indicators that influence funding.
It is a major challenge for institutions and teachers to enable as many students as possible to succeed, given the wide diversity of the student body. More focus on teaching methods that lead to student success, more individualization of learning, and more flexible delivery are all needed to meet the challenge of an increasingly diverse student body. These developments put much more responsibility on the shoulders of teachers and instructors (as well as students), and require a much higher level of skill in teaching.
Fortunately, over the last 100 years there has been a great deal of research into how people learn, and a lot of research into teaching methods that lead to student success. Unfortunately, that research is not known or applied by the vast majority of university and college instructors, who still rely mainly on teaching methods that were perhaps appropriate when there were small classes and elite students, but are no longer appropriate today (see, for instance, Christensen Hughes and Mighty, 2010). Thus a different approach to teaching, and a better use of technology to help instructors increase their effectiveness across a diverse student body, are now needed.
References
Christensen Hughes, J. and Mighty, J. (2010) Taking Stock: Research on Teaching and Learning in Higher Education Montreal and Kingston: McGill-Queen’s University Press
Council of Ontario Universities (2012) Increased numbers of students heading to Ontario universities Toronto ON: COU
Heslop, J. (2016) Education Pathways for High School Graduates and Non-Graduates Victoria BC: Student Transitions Project, Government of British Columbia
Prensky, M. (2001) ‘Digital natives, Digital Immigrants’ On the Horizon Vol. 9, No. 5
Robbins, L. (1963) Higher Education Report London: Committee on Higher Education, HMSO
Shapiro, D., et al. (2016) Time to Degree: A National View of the Time Enrolled and Elapsed for Associate and Bachelor’s Degree Earners (Signature Report No. 11). Herndon, VA: National Student Clearinghouse Research Center.
Activity 1.6 Dealing with diversity
1. What changes if any have you noticed in the students you are teaching? How does this differ from my analysis?
2. Whose responsibility is it to ensure students succeed? To what extent does the diversity of students place more responsibility on teachers and instructors?
3. Do you agree that ‘More means worse’? If you do, what alternatives would you suggest for higher education? How would this be paid for?
4. Does your country/state have the balance right between academic and vocational education? Do we put too much emphasis on universities and not enough on technical or vocational colleges?
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Figure 1.7.1 Technology is changing the way we teach – and the way students learn Image: Vidyo.com
We shall see in Chapter 7, Section 2 that technology has played an important role in teaching from time immemorial, but until recently, it has remained more on the periphery of education. Technology has been used mainly to support regular classroom teaching, or operated in the form of distance education, for a minority of students or in specialized departments (often in continuing education or extension).
However, in the last ten to fifteen years, technology has been increasingly influencing the core teaching activities of universities and colleges. Some of the ways technology is moving from the periphery to the centre can be seen from the following trends.
1.7.1. Fully online learning
Credit-based online learning in recent years has become a major and central activity of most academic departments in universities, colleges and to some extent even in school/k-12 education.
Online learning enrolments increased by between 10-20 per cent per annum between 2002 and 2012 in North American higher education institutions, compared with an increase in campus-based enrolments of around 2-3 per cent per annum (Allen and Seaman, 2014).
Figure 1.7.2 From Allen and Seaman, 2014
In just the California Community College System alone, there are almost one million online course enrolments (Johnson and Mejia, 2014). There are now at least seven million students in the USA taking at least one fully online course, a lmost 30 per cent of all post-secondary students in the USA; and 14 per cent of all students are taking only distance education courses. The majority of these fully online enrolments (just over two-thirds) are in public institutions in the USA (online enrolments in for-profit institutions plunged after 2012 due to Obama-era regulation). At the same time, the number of students studying on a campus in the USA dropped by almost one million (931,317) between 2012 and 2015 (Digital Learning Compass, 2017).
The situation in Canada is somewhat similar. Most Canadian post-secondary institutions (83 per cent) offered fully online courses for credit in 2017. Roughly 17 per cent of all students were taking at least one online course; online course registrations totalled 1.3 million, accounting for eight per cent of all credit course enrolments. This is equivalent in a system with roughly 70 universities and 150 public colleges to four additional universities of 27,000 students, and five additional colleges of 10,000 students. Online learning was considered by institutional leaders to be very or extremely important for the institution’s future in over two-thirds of all institutions (Bates et al, 2018)
Thus fully online learning is now a key component of many school and post-secondary education systems.
1.7.2. Blended and hybrid learning
As more instructors have become involved in online learning, they have realised that much that has traditionally been done in class can be done equally well or better online (a theme that will be explored more in Chapter 10, Section 2). As a result, instructors have been gradually introducing more online study elements into their classroom teaching. So learning management systems may be used to store lecture notes in the form of slides or PDFs, links to online readings may be provided, or online forums for discussion may be established. Thus online learning is gradually being blended with face-to-face teaching, but without changing the basic classroom teaching model. Here online learning is being used as a supplement to traditional teaching. Although there is no standard or commonly agreed definitions in this area, I will use the term ‘blended learning’ for this use of technology.
More recently, though, lecture capture has resulted in instructors realising that if the lecture is recorded, students could view this in their own time, and then the classroom time could be used for more interactive sessions. This model has become known as the ‘flipped classroom’.
An even more significant move, but still in a minority of classes, is the move to hybrid learning, where some, but not all, of regular classroom time is replaced by online activities. This sometimes leads to a complete re-design of the teaching experience for students.
Some institutions are now developing plans to move a substantial part of their teaching into more blended or flexible modes. Almost two-thirds of the institutions in the 2017 Canadian survey either had a plan for online learning or were developing one, and another 30 per cent reported that they did not have a plan but needed one. For instance in 2013 the University of Ottawa developed a plan to have at least 20 per cent of its courses blended or hybrid within five years The University of British Columbia has a planto redesign most of its first and second year large lecture classes into hybrid classes. Furthermore, some instructors are incorporating emerging technologies such as simulations and educational or serious games, augmented and virtual reality, in ways that fundamentally change the experience of learning. These are all indications of the growing importance of digital learning.
1.7.3. Open learning
Another increasingly important development linked to online learning is the move to ‘open’ education that over the last 10 years has begun to impact directly on conventional institutions. The most immediate is open textbooks – such as what you are reading now. Open textbooks are digital textbooks that can be downloaded in a digital format by students (or instructors) for free, thus saving students considerable money on textbooks. For instance, in Canada, the three provinces of British Columbia, Alberta, and Saskatchewan are collaborating on the production and distribution of peer-reviewed open textbooks for the 40 high-enrolment subject areas in their university and community college programs. By 2018 nearly all post-secondary institutions in British Columbia (90 per cent) had adopted at least one open textbook (Bates et al, 2018).
Open educational resources (OER) are another recent development in open education. These are digital educational materials freely available over the Internet that can be downloaded by instructors (or students) without charge, and if necessary adapted or amended, under a Creative Commons license that provides protections for the creators of the material. Probably the best known source of OER is the Massachusetts Institute of Technology OpenCourseWare project. With individual professors’ permission, MIT has made available for free downloading over the Internet video lectures recorded with lecture capture as well as supporting materials such as slides.
The implications of developments in open learning will be discussed further in Chapter 11.
1.7.4. MOOCs
One of the main developments in online learning has been the rapid growth of Massive Open Online Courses (MOOCs). In 2008, the University of Manitoba in Canada offered the first MOOC with just over 2,000 enrolments, which linked webinar presentations and/or blog posts by experts to participants’ blogs and tweets. The courses were open to anyone and had no formal assessment. In 2012, two Stanford University professors launched a lecture-capture based MOOC on artificial intelligence, attracting more than 100,000 students, and since then MOOCs have expanded rapidly around the world.
Although the format of MOOCs can vary, in general they have the following characteristics:
• open to anyone to enroll and simple enrollment (just an e-mail address)
• very large numbers (from 1,000 to 100,000)
• free access to video-recorded lectures, often from the most elite universities in the USA (Harvard, MIT, Stanford in particular).
• computer-based assessment, usually using multiple-choice questions and immediate feedback, combined sometimes with peer assessment
• a wide range of commitment from learners: up to 50 per cent never do more than register, 25 per cent never take more than the first assignment, less than 10 per cent complete the final assessment.
However, MOOCs are merely the latest example of the rapid evolution of technology, the over-enthusiasm of early adopters, and the need for careful analysis of the strengths and weaknesses of new technologies for teaching. They are evolving over time, and are beginning to find a more limited but still important niche in the higher education market. MOOCs will be discussed more fully in Chapter 5.
1.7.5 Managing the changing landscape of education
These rapid developments in educational technologies mean that faculty and instructors need a strong framework for assessing the value of different technologies, new or existing, and for deciding how or when these technologies make sense for them and their students to use. Blended and online learning, social media and open learning are all developments that are critical for effective teaching in a digital age.
However, these emerging technological developments need to be harnessed to the changing needs of learners in a digital society, which means also looking at different ways of teaching and ensuring these teaching methods and choices of technology are fully aligned with the needs of learners in a digital age.
References
Allen, I. and Seaman, J. (2014) Grade Change: Tracking Online Learning in the United States Wellesley MA: Babson College/Sloan Foundation
Bates, T. et al. (2018) Tracking Online and Distance Education in Canadian Universities and Colleges 2018 Halifax: Canadian Digital Learning Research Association
Digital Learning Compass (2017) Distance Education Enrolment Report 2017 Wellesley MA
Johnson, H. and Mejia, M. (2014) Online learning and student outcomes in California’s community colleges San Francisco CA: Public Policy Institute of California
University of Ottawa (2013) Report of the e-Learning Working Group Ottawa ON: University of Ottawa: see also Report on the Blended Learning Initiative (2016), which reports on progress in implementing the plan
Activity 1.7 The consequences of change
1. Have you in recent years moved to blended or online learning or used new technology in your teaching? If so, what was your reason?
2. If not, what has stopped you trying a new approach with technology?
3. If you have started to use technology in your teaching, what were the main difficulties you encountered? Did you get sufficient help from colleagues or the institution?
4. Did you change your academic goals or did you try to achieve the same learning outcomes as in fully face-to-face teaching?
5. Were there any unintended or unexpected consequences of moving towards the use of more technology in your teaching?
There is no feedback provided for this activity.
01.8: Navigating new developments in technology and online learning
Instructors in both universities and colleges now face the following challenges:
• to teach in ways that help develop the knowledge and skills needed in today’s society;
• to handle increasingly large classes;
• to develop teaching methods that are appropriate for an increasingly diverse student body;
• to deal with a variety of different modes of delivery.
However, in general, teachers and instructors in post-secondary education have little or no training in teaching, pedagogy or the research on learning. Even many school teachers lack adequate training to deal with rapidly changing technologies. We wouldn’t expect pilots to fly a modern jet without any training, yet that is exactly what we are expecting of our teachers and instructors.
This book then aims to provide a framework for making decisions about how to teach, and how best to use technology, in ways that are true to the core values of universities, colleges, and schools, while building on the large amount of research into learning and teaching, and into the use of technology for teaching, that has been done over the last 50 years or so.
The next chapter deals with the most important question of all: how do you want to teach in a digital age?
Activity 1.8 Your main conclusions from Chapter 1
Write down at least five conclusions you would draw from this chapter, in addition to the key takeaways below.
Click here to compare your answers with mine.
Key Takeaways
1. Teaching methods need to be used that help to develop and transfer specific skills that serve both the purposes of knowledge development and dissemination, while at the same time preparing graduates for work in a knowledge-based society.
2. As student numbers have increased, teaching has regressed for a variety of reasons to a greater focus on information transmission and less focus on questioning, exploration of ideas, presentation of alternative viewpoints, and the development of critical or original thinking. Yet these are the very skills needed by students in a knowledge-based society.
3. The wide diversity of the student body is a major challenge for institutions. This requires more focus on teaching methods that provide support for learners, more individualization of learning, and more flexible delivery.
4. Online learning is a continuum; every instructor and every institution now needs to decide: where on this continuum of teaching should a particular course or program be?
5. As more academic content becomes openly and freely available, students will look increasingly to their local institutions for support with their learning, rather than for the delivery of content. This puts a greater focus on teaching skills and less on subject expertise.
6. Faculty and instructors need a strong framework for assessing the value of different technologies, new or existing, and for deciding how or when these technologies make sense for them (and/or their students) to use. | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/01%3A_Fundamental_Change_in_Education/01.7%3A_From_the_periphery_to_the_center%3A_how_technology_is_changing_the_way_we_teach.txt |
For my comments on why this chapter is important for the rest of the book, please click on the podcast below
An audio element has been excluded from this version of the text. You can listen to it online here: https://pressbooks.bccampus.ca/teachinginadigitalagev2/?p=57
All teaching is a mix of art and science. It is an art because any teacher or instructor is faced with numerous and constantly changing variables, which require rapid judgement and decision-making. Good teachers usually have a passion for teaching so the emotional as well as the cognitive side is important. In many cases, it’s also about personal relationships, the extent to which an instructor can empathise with students or appreciate their difficulties in learning, and the extent to which the instructor can communicate effectively.
There is also a science of teaching, based on theory and research. We shall see in fact there are many, often conflicting theories, driven primarily by epistemological differences about the nature of knowledge, and by different value systems. Then over the last 100 years there has been a great deal of empirical research into how students learn, and effective teaching methods, which at its best is driven by a strong, explicit theoretical base, and at its worse by mindless data-collection (such as RateMyProfessor).
As well as research-based practices, there are what are known as best practices, based on teachers’ experience of teaching. While in many cases these have been validated by research or are driven by theories of learning, this is not always the case. As a result, what some people see as best practices are not always universally shared by others, even if best practices are seen in general as current accepted wisdom. Teaching math in primary schools in one example. Lectures are another. In Chapter 3, Section 3, strong evidence is provided that lectures have many limitations, yet many instructors still believe that this is the most appropriate way to teach their subject.
However, even the most extensively trained teachers don’t always make good teachers if they don’t have the talent and emotional connection with learners, and untrained teachers (which covers virtually all university instructors), sometimes succeed, even with little experience, because they have a knack or in-born talent. However, although such instructors are often held up as the triumph of art over science in teaching, they are in practice very rare. Many of these untutored, brilliant instructors have learned rapidly on the job by trial and error, with the inevitable casualties along the way.
For all these reasons, there is no one best way to teach that will fit all circumstances, which is why arguments over ‘modern’ or ‘traditional’ approaches to teaching reading or math, for example, are often so sterile. Good teachers usually have an arsenal of tools, methods and approaches that they can draw on, depending on the circumstances. Also teachers and instructors will differ over what constitutes good teaching, depending on their understandings of what knowledge is, what matters most in learning, and their priorities in terms of desirable learning outcomes.
Nevertheless, these apparent contradictions do not mean that we cannot develop guidelines and techniques to improve the quality of teaching, or that we have no principles or evidence on which to base decisions about teaching, even in a rapidly changing digital age. The aim of this book is to provide such guidelines, while recognizing that one size will not fit all, and that every teacher or instructor will need to select and adapt the suggestions in this book to their own unique context.
For this approach to work, though, we need to explore some fundamental issues about teaching and learning, some of which are rarely addressed in everyday discussions about education. The first and probably most important is epistemology.
Activity 2.1: What do you think makes a good teacher?
1. Write down, in order of priority, what you consider to be the three most important characteristics of a good teacher.
2. Explain why your answer differs from mine.
For the reasons given above, I give no no feedback (and certainly no ‘right or wrong answers’) to these questions. | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/02%3A_The_nature_of_knowledge_and_the_implications_for_teaching/02.1%3A_Art_theory_research_and_best_practices_in_teaching.txt |
Figure 2.B The Dinner Party, from NBC’s The Office
List of characters.
• Peter and Ruth (hosts)
• Stephen (a mechanical engineer and Peter’s brother)
• Caroline (a writer and Ruth’s friend)
Peter to Stephen. I think Caroline’s arrived. Now I know you’ve not met Caroline before, but for goodness sake, do try to be polite and sociable this time. The last time you were here, you hardly said a word.
Stephen. Well, nobody said anything that interested me. It was all about books and art. You know I’m not interested in that sort of thing.
Peter: Well, just try. Here she is. Caroline – lovely to see you again. Come and sit down. This is Stephen, my brother. I don’t think you’ve met, although I’ve told you about him – he’s a professor of mechanical engineering at the local university. But first, what would you like to drink?
Caroline. Hi, Stephen. No, I don’t think we have met. Nice to meet you. Peter, I’ll have a glass of white wine, please.
Peter. While you’re introducing yourselves, I’ll go and get the drinks and give Ruth a hand in the kitchen.
Stephen. Peter says you’re a writer. What do you write about?
Caroline (laughing). Well, you do like to get straight to the point, don’t you? It’s a bit difficult to answer your question. It depends on what I’m interested in at the time.
Stephen. And what are you interested in at the moment?
Caroline. I’m thinking about how someone would react to the loss of someone they love due to the action of someone else they also love deeply. It was prompted by an item on the news of how a father accidentally killed his two year old daughter by running her over when he was backing the car out of the garage. His wife had just let the girl out to play in the front garden and didn’t know her husband was getting the car out.
Stephen. God, that’s awful. I wonder why the hell he didn’t have a rear view video camera installed.
Caroline. Well, the horrible thing about it is that it could happen to anyone. That’s why I want to write something around such everyday tragedies.
Stephen. But how can you possibly write about something like that if you haven’t experienced that kind of thing yourself? Or have you?
Caroline. No, thank goodness. Well, I guess that’s the art of a writer – the ability to embed yourself in other people’s worlds, and to anticipate their feelings, emotions and consequent actions.
Stephen. But wouldn’t you need a degree in psychology or experience as a grief counsellor to do that in that situation?
Caroline. Well, I might talk to people who’ve undergone similar kinds of family tragedies, to see what kind of people they are afterwards, but basically it’s about understanding how I might react in such a situation and projecting that and modifying that according to the kind of characters I’m interested in.
Stephen. But how do you know it would be true, that people really would react the way you think they would?
Caroline. Well, what is ‘truth’ in a situation like that? Different people are likely to act differently. That’s what I want to explore in the novel. The husband reacts one way, the wife another, and then there’s the interaction between the two, and all those round them. I’m particularly interested in whether they could actually grow and become better people, or whether they disintegrate and destroy each other.
Stephen. But how can you not know that before you start?
Caroline. Well, that’s the point, really. I don’t. I want the characters to grow in my imagination, and the outcome will inevitably be determined by that.
Stephen. But if you don’t know the truth, how those two people actually responded to that tragedy, how can you help them or others like them?
Caroline. But I’m a novelist, not a therapist. I’m not attempting to help anyone in such an awful situation. I’m trying to understand the general human condition, and to do that, I have to start with myself, what I know and feel, and project that into another context.
Stephen. But that’s nonsense. How can you possibly understand the human condition just by looking inwards at yourself, and making up a fictional situation, that probably has nothing to do with what actually happened?
Caroline (sighs). Stephen, you’re a typical bloody scientist, with no imagination.
Peter (arriving with the drinks). Well, how are you two getting along?
Obviously at this point, not very well. The problem is that they have different world views on truth and how it can be reached. They start from very different views about what constitutes knowledge, how knowledge is acquired, and how it is validated. As always, the ancient Greeks had a word for thinking about the nature of knowledge: epistemology. We shall see that this is an important driver of how we teach. | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/02%3A_The_nature_of_knowledge_and_the_implications_for_teaching/02.1%3A_Scenario_B%3A_A_pre-dinner_party_discussion.txt |
Figure 2.2.1 Image: © Freethought Kampala, 2017, via Libguides, University of Pittsburgh
2.2.1 What is epistemology?
In the dinner party scenario, Stephen and Caroline had quite different beliefs about the nature of knowledge. The issue here is not who was right, but that we all have implicit beliefs about the nature of knowledge, what constitutes truth, how that truth is best validated, and, from a teaching perspective, how best to help people to acquire that knowledge. The basis of that belief will vary, depending on the subject matter, and, in some areas, such as social sciences, even within a common domain of knowledge.
Our choice of teaching approaches and even the use of technology are absolutely dependent on beliefs and assumptions we have about the nature of knowledge, about the requirements of our subject discipline, and about how we think students learn. The way we teach in higher education will be driven primarily by our beliefs or rather, by the commonly agreed consensus within an academic discipline about what constitutes valid knowledge in the subject area.
The nature of knowledge centres on the question of how we know what we know. What makes us believe that something is ‘true’? Questions of this kind are epistemological in nature. Hofer and Pintrich (1997) state:
Epistemology is a branch of philosophy concerned with the nature and justification of knowledge.
The famous argument at the British Association in 1860 between Thomas Huxley and the Bishop of Oxford, Samuel Wilberforce, over the origin of species is a classic example of the clash between beliefs about the foundations of knowledge. Wilberforce argued that Man was created by God; Huxley argued that Man evolved through natural selection. Bishop Wilberforce believed he was right because ‘true’ knowledge was determined through faith and interpretation of holy scripture; Professor Huxley believed he was right because ‘true’ knowledge was derived through empirical science and rational skepticism.
An important part of higher education is aimed at developing students’ understanding, within a particular discipline, of the criteria and values that underpin academic study of that discipline, and these include questions of what constitutes valid knowledge in that subject area. For many experts in a particular field, these assumptions are often so strong and embedded that the experts may not even be openly conscious of them unless challenged. But for novices, such as students, it often takes a great deal of time to understand fully the underlying value systems that drive choice of content and methods of teaching.
Our epistemological position therefore has direct practical consequences for how we teach.
2.2.2 Epistemology and theories of learning
Most teachers in the school/k-12 sector will be familiar with the main theories of learning, but because instructors in post-secondary education are hired primarily for their subject experience, or research or vocational skills, it is essential to introduce and discuss, if only briefly, these main theories. In practice, even without formal training or knowledge of different theories of learning, all teachers and instructors will approach teaching within one of these main theoretical approaches, whether or not they are aware of the educational jargon surrounding these approaches. Also, as new technologies and new modes of teaching such as online learning, technology-based teaching, and informal digital networks of learners have evolved, new theories of learning are beginning to emerge.
With a knowledge of alternative theoretical approaches, teachers and instructors are in a better position to make choices about how to approach their teaching in ways that will best fit the perceived needs of their students, within the very many different learning contexts that teachers and instructors face. This is particularly important when addressing many of the requirements of learners in a digital age that are set out in Chapter 1. Furthermore, the choice of or preference for a particular epistemology or a particular theoretical approach to teaching will have major implications for the way that technology is used to support teaching.
In fact, there is a huge amount of literature on theories of learning, and I am aware that the treatment in this book is cursory, to say the least. Those who would prefer a more detailed introduction to theories of learning should explore Schunk (2016) or Harasim (2017). The aim of my book though is not to be comprehensive in terms of in-depth coverage of all learning theories, but to provide a basis on which to suggest and evaluate different ways of teaching to meet the diverse needs of learners in a digital age.
The important point here is that every theory of teaching or learning is underpinned by a particular assumption or understanding of what constitutes ‘true’ knowledge: in other words by a particular epistemological position. In the following sections I examine four of the most common theories of learning, and the underlying epistemologies that drive them.
References
Harasim, L. (2017) Learning Theory and Online Technologies 2nd edition New York/London: Taylor and Francis
Hofer, B. and Pintrich, P. (1997) ‘The development of epistemological theories: beliefs about knowledge and knowing and their relation to learningReview of Educational Research Vol. 67, No. 1, pp. 88-140
Schunk, D. (2016) Learning Theories: An Educational Perspective: 7th edition London: Pearson Education
Activity 2.2 Epistemologies at a dinner party
1. Draw two columns. Under one column, write down a list of the justifications that Caroline used for her book in Scenario B. Similarly, in the other column, write down Stephen’s objections.
2. What are the common themes underlying each person’s justification for their arguments? (Try not to make a value judgement about which were the ‘best’ arguments.)
3. Would it be possible to reconcile both approaches?
Feedback to come | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/02%3A_The_nature_of_knowledge_and_the_implications_for_teaching/02.2%3A_Epistemology_and_theories_of_learning.txt |
Figure 2.3.1 The solar system: an objective fact?
Image: © International Astronomical Union/Wikipedia
2.3.1 The objectivist epistemology
Objectivists believe that there exists an objective and reliable set of facts, principles and theories that either have been discovered and delineated or will be over the course of time. This position is linked to the belief that truth exists outside the human mind, or independently of what an individual may or may not believe. Thus the laws of physics are constant, although our knowledge of them may evolve as we discover the ‘truth’ out there.
2.3.2 Objectivist approaches to teaching
A teacher operating from a primarily objectivist view is more likely to believe that a course must present a body of knowledge to be learned. This may consist of facts, formulas, terminology, principles, theories and the like.
The effective transmission of this body of knowledge becomes of central importance. Lectures and textbooks must be authoritative, informative, organized, and clear. The student’s responsibility is accurately to comprehend, reproduce and add to the knowledge handed down to him or her, within the guiding epistemological framework of the discipline, based on empirical evidence and the testing of hypotheses. Course assignments and exams would require students to find ‘right answers’ and justify them. Original or creative thinking must still operate within the standards of an objectivist approach – in other words, new knowledge development must meet the rigorous standards of empirical testing within agreed theoretical frameworks.
An ‘objectivist’ teacher has to be very much in control of what and how students learn, choosing what is important to learn, the sequence, the learning activities, and how learners are to be assessed.
2.3.3 Behaviourism
Although initially developed in the 1920s, behaviourism still dominates approaches to teaching and learning in many places, particularly in the USA. Behaviourism is an objectivist learning theory. Behaviourist psychology is an attempt to model the study of human behaviour on the methods of the physical sciences, and therefore concentrates attention on those aspects of behaviour that are capable of direct observation and measurement. At the heart of behaviourism is the idea that certain behavioural responses become associated in a mechanistic and invariant way with specific stimuli. Thus a certain stimulus will evoke a particular response. At its simplest, it may be a purely physiological reflex action, like the contraction of an iris in the eye when stimulated by bright light.
However, most human behaviour is more complex. Nevertheless behaviourists have demonstrated in labs that it is possible to reinforce through reward or punishment the association between any particular stimulus or event and a particular behavioural response. The bond formed between a stimulus and response will depend on the existence of an appropriate means of reinforcement at the time of association between stimulus and response. This depends on random behaviour (trial and error) being appropriately reinforced as it occurs.
This is essentially the concept of operant conditioning, a principle most clearly developed by Skinner (1968). He showed that pigeons could be trained in quite complex behaviour by rewarding particular, desired responses that might initially occur at random, with appropriate stimuli, such as the provision of food pellets. He also found that a chain of responses could be developed, without the need for intervening stimuli to be present, thus linking an initially remote stimulus with a more complex behaviour. Furthermore, inappropriate or previously learned behaviour could be extinguished by withdrawing reinforcement. Reinforcement in humans can be quite simple, such as immediate feedback for an activity or getting a correct answer to a multiple-choice test.
Figure 2.3.2 YouTube video/film of B.F. Skinner demonstrating his teaching machine, 1954
Click on image to see video
You can see a fascinating five minute film of B.F. Skinner describing his teaching machine in a 1954 film captured on YouTube, either by clicking on the picture above or at: http://www.youtube.com/watch?v=jTH3ob1IRFo
Underlying a behaviourist approach to teaching is the belief that learning is governed by invariant principles, and these principles are independent of conscious control on the part of the learner. Behaviourists attempt to maintain a high degree of objectivity in the way they view human activity, and they generally reject reference to unmeasurable states, such as feelings, attitudes, and consciousness. Human behaviour is above all seen as predictable and controllable. Behaviourism thus stems from a strongly objectivist epistemological position.
Skinner’s theory of learning provides the underlying theoretical basis for the development of teaching machines, measurable learning objectives, computer-assisted instruction, and multiple choice tests. It often is implicit in the application of artificial intelligence to modifying human behaviour. Behaviourism’s influence is still strong in corporate and military training, and in some areas of science, engineering, and medical training. It can be of particular value for rote learning of facts or standard procedures such as multiplication tables, for dealing with children or adults with limited cognitive ability due to brain disorders, or for compliance with industrial or business standards or processes that are invariant and do not require individual judgement. It is also the underlying methodology of social media such as Facebook for influencing behaviour, through ‘likes’, number of hits and connections, and other ‘status’ rewards.
Behaviourism, with its emphasis on reward and punishment as drivers of learning, and on pre-defined and measurable outcomes, is the basis of populist conceptions of learning among many parents, politicians, and, it should be noted, computer scientists interested in automating learning. It is not surprising then that there has also been a tendency until recently to see technology, and in particular computer-aided instruction, as being closely associated with behaviourist approaches to learning, although we shall see in Chapter 5, Section 4 that computers do not necessarily have to be used in a behaviourist way.
Lastly, although behaviourism is an ‘objectivist’ approach to teaching, it is not the only way of teaching ‘objectively’. For instance, problem-based learning can still take a highly objective approach to knowledge and learning.
References
Skinner, B. (1968) The Technology of Teaching, New York: Appleton-Century-Crofts
Activity 2.3 Defining the limits of behaviourism
1. What areas of knowledge do you think would be best ‘taught’ or learned through a behaviourist approach?
2. What areas of knowledge do you think would NOT be appropriately taught through a behaviourist approach?
3. What are your reasons?
Feedback to come | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/02%3A_The_nature_of_knowledge_and_the_implications_for_teaching/02.3%3A_Objectivism_and_behaviourism.txt |
Figure 2.4.1 Benjamin Bloom Image: Wikipedia
2.4.1 What is cognitivism?
An obvious criticism of behaviourism is that it treats humans as a black box, where inputs into the black box, and outputs from the black box, are known and measurable, but what goes on inside the black box is ignored or not considered of interest. However, humans have the ability for conscious thought, decision-making, emotions, and the ability to express ideas through social discourse, all of which are highly significant for learning. Thus we will likely get a better understanding of learning if we try to find out what goes on inside the black box.
Cognitivists therefore have focused on identifying mental processes – internal and conscious representations of the world – that they consider are essential for human learning. Fontana (1981) summarises the cognitive approach to learning as follows:
The cognitive approach … holds that if we are to understand learning we cannot confine ourselves to observable behaviour, but must also concern ourselves with the learner’s ability mentally to re-organize his psychological field (i.e. his inner world of concepts, memories, etc.) in response to experience. This latter approach therefore lays stress not only on the environment, but upon the way in which the individual interprets and tries to make sense of the environment. It sees the individual not as the somewhat mechanical product of his environment, but as an active agent in the learning process, deliberately trying to process and categorize the stream of information fed into him by the external world.’ (p. 148)
Thus the search for rules, principles or relationships in processing new information, and the search for meaning and consistency in reconciling new information with previous knowledge, are key concepts in cognitive psychology. Cognitive psychology is concerned with identifying and describing mental processes that affect learning, thinking and behaviour, and the conditions that influence those mental processes.
2.4.2 Cognitivist learning theory
The most widely used theories of cognitivism in education are based on Bloom’s taxonomies of learning objectives (Bloom et al., 1956), which are related to the development of different kinds of learning skills, or ways of learning. Bloom and his colleagues claimed that there are three important domains of learning:
• cognitive (thinking)
• affective (feeling)
• psycho-motor (doing).
Cognitivism focuses on the ‘thinking’ domain. In more recent years, Anderson and Krathwohl (2000) have slightly modified Bloom et al.’s original taxonomy, adding ‘creating’ new knowledge:
Figure 2.4.2 Cognitive domain
Image: © Atherton J S (2013) CC-NC-ND
Bloom et al. also argued that there is a hierarchy of learning, meaning that learners need to progress through each of the levels, from remembering through to evaluating/creating. As psychologists delve deeper into each of these cognitive activities to understand the underlying mental processes, it becomes an increasingly reductionist exercise (see Figure 2.4.3 below).
Figure 2.4.3 © Faizel Mohidin, UsingMindMaps, 2011.
2.4.3 Applications of cognitivist learning theory
Cognitive approaches to learning, with a focus on comprehension, abstraction, analysis, synthesis, generalization, evaluation, decision-making, problem-solving and creative thinking, seem to fit much better with higher education than behaviourism, but even in school/k-12 education, a cognitivist approach would mean for instance focusing on teaching learners how to learn, on developing stronger or new mental processes for future learning, and on developing deeper and constantly changing understanding of concepts and ideas.
Cognitive approaches to learning cover a very wide range. At the objectivist end, cognitivists consider basic mental processes to be genetic or hard-wired, but can be programmed or modified by external factors, such as new experiences. Early cognitivists in particular were interested in the concept of mind as computer, and more recently brain research has led to a search for linking cognition to the development and reinforcement of neural networks in the brain.
In terms of practice, this concept of mind as computer has led to several technology-based developments in teaching, including:
• intelligent tutoring systems, a more refined version of teaching machines, based on breaking down learning into a series of manageable steps, and analysing learners’ responses to direct them to the most appropriate next step. Adaptive learning is the latest extension of such developments;
• artificial intelligence, which seeks to represent in computer software the mental processes used in human learning (which of course if successful would result in computers replacing many human activities – such as teaching, if learning is considered in an objectivist framework);
• pre-determined learning outcomes, based on an analysis and development of different kinds of cognitive activities, such as comprehension, analysis, synthesis, and evaluation;
• problem-based learning, based on an analysis of the thinking processes successful problem-solvers use to solve problems;
• instructional design approaches that attempt to manage the design of teaching to ensure successful achievement of pre-determined learning outcomes or objectives.
Cognitivists have increased our understanding of how humans process and make sense of new information, how we access, interpret, integrate, process, organize and manage knowledge, and have given us a better understanding of the conditions that affect learners’ mental states.
References
Anderson, L. and Krathwohl, D. (eds.) (2001). A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom’s Taxonomy of Educational Objectives New York: Longman
Atherton J. S. (2013) Learning and Teaching; Bloom’s taxonomy, retrieved 7 May 2019
Bloom, B. S.; Engelhart, M. D.; Furst, E. J.; Hill, W. H.; Krathwohl, D. R. (1956). Taxonomy of educational objectives: The classification of educational goals. Handbook I: Cognitive domain. New York: David McKay Company
Fontana, D. (1981) Psychology for Teachers London: Macmillan/British Psychological Society
Activity 2.4 Defining the limits of cognitivism
1. What areas of knowledge do you think would be best ‘taught’ or learned through a cognitivist approach?
2. What areas of knowledge do you think would NOT be appropriately taught through a cognitivist approach?
3. What are your reasons?
Feedback to come | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/02%3A_The_nature_of_knowledge_and_the_implications_for_teaching/02.4%3A_Cognitivism.txt |
Figure 2.5.1 Project work is one form of constructivist learning
Image: © Jim Olive, Environmental Protection Agency/Wikipedia, 1972
2.5.1 What is constructivism?
Both behaviourist and some elements of cognitive theories of learning are deterministic, in the sense that behaviour and learning are believed to be rule-based and operate under predictable and constant conditions over which the individual learner has no or little control. However, constructivists emphasise the importance of consciousness, free will and social influences on learning. Carl Rogers (1969) stated that:
every individual exists in a continually changing world of experience in which he is the center.
The external world is interpreted within the context of that private world. The belief that humans are essentially active, free and strive for meaning in personal terms has been around for a long time, and is an essential component of constructivism.
Constructivists believe that knowledge is essentially subjective in nature, constructed from our perceptions and mutually agreed upon conventions. According to this view, we construct new knowledge rather than simply acquire it via memorization or through transmission from those who know to those who don’t know. Constructivists believe that meaning or understanding is achieved by assimilating information, relating it to our existing knowledge, and cognitively processing it (in other words, thinking or reflecting on new information). Social constructivists believe that this process works best through discussion and social interaction, allowing us to test and challenge our own understandings with those of others. For a constructivist, even physical laws exist because they have been constructed by people from evidence, observation, and deductive or intuitive thinking, and, most importantly, because certain communities of people (in this example, scientists) have mutually agreed what constitutes valid knowledge.
Constructivists argue that individuals consciously strive for meaning to make sense of their environment in terms of past experience and their present state. It is an attempt to create order in their minds out of disorder, to resolve incongruities, and to reconcile external realities with prior experience. The means by which this is done are complex and multi-faceted, from personal reflection, seeking new information, to testing ideas through social contact with others. Problems are resolved, and incongruities sorted out, through strategies such as seeking relationships between what was known and what is new, identifying similarities and differences, and testing hypotheses or assumptions. Reality is always tentative and dynamic.
One consequence of constructivist theory is that each individual is unique, because the interaction of their different experiences, and their search for personal meaning, results in each person being different from anyone else. Thus behaviour is not predictable or deterministic, at least not at the individual level (which is a key distinguishing feature from cognitivism, which seeks general rules of thinking that apply to all humans). The key point here is that for constructivists, learning is seen as essentially a social process, requiring communication between learner, teacher and others. This social process cannot effectively be replaced by technology, although technology may facilitate it.
2.5.2 Constructivist approaches to teaching
For many educators, the social context of learning is critical. Ideas are tested not just on the teacher, but with fellow students, friends and colleagues. Furthermore, knowledge is mainly acquired through social processes or institutions that are socially constructed: schools, universities, and increasingly these days, online communities. Thus what is taken to be ‘valued’ knowledge is also socially constructed.
Constructivists believe that learning is a constantly dynamic process. Understanding of concepts or principles develops and becomes deeper over time. For instance, as a very young child, we understand the concept of heat through touch. As we get older we realise that it can be quantified, such as minus 20 centigrade being very cold (unless you live in Manitoba, where -20C would be considered normal). As we study science, we begin to understand heat differently, for instance, as a form of energy transfer, then as a form of energy associated with the motion of atoms or molecules. Each ‘new’ component needs to be integrated with prior understandings and also integrated with other related concepts, including other components of molecular physics and chemistry.
Thus ‘constructivist’ teachers place a strong emphasis on learners developing personal meaning through reflection, analysis and the gradual building of layers or depths of knowledge through conscious and ongoing mental processing. Reflection, seminars, discussion forums, small group work, and projects are key methods used to support constructivist learning in campus-based teaching (discussed in more detail in Chapter 3), and online collaborative learning, and communities of practice are important constructivist methods in online learning (Chapter 4).
Although problem-solving can be approached in an objectivist way, by pre-determining a set of steps or processes to go through pre-determined by ‘experts’, it can also be approached in a constructivist manner. The level of teacher guidance can vary in a constructivist approach to problem-solving, from none at all, to providing some guidelines on how to solve the problem, to directing students to possible sources of information that may be relevant to solving that problem, to getting students to brainstorm particular solutions. Students will probably work in groups, help each other and compare solutions to the problem. There may not be considered one ‘correct’ solution to the problem, but the group may consider some solutions better than others, depending on the agreed criteria of success for solving the problem.
It can be seen that there can be ‘degrees’ of constructivism, since in practice the teacher may well act as first among equals, and help direct the process so that ‘suitable’ outcomes are achieved. The fundamental difference is that students have to work towards constructing their own meaning, testing it against ‘reality’, and further constructing meaning as a result.
Constructivists also approach technology for teaching differently from behaviourists. From a constructivist perspective, brains have more plasticity, adaptability and complexity than current computer software programs. Other uniquely human factors, such as emotion, motivation, free will, values, and a wider range of senses, make human learning very different from the way computers operate. Following this reasoning, education would be much better served if computer scientists tried to make software to support learning more reflective of the way human learning operates, rather than trying to fit human learning into the current restrictions of behaviourist computer programming. This will be discussed in more detail in Chapter 4, Section 4.
Although constructivist approaches can be and have been applied to all fields of knowledge, they are more commonly found in approaches to teaching in the humanities, social sciences, education, and other less quantitative subject areas.
References
Rogers, C. (1969) Freedom to Learn Columbus, OH: Charles E. Merrill Publishing Co.
There are many books on constructivism but some of the best are the original works of some of the early educators and researchers, in particular:
Piaget, J. and Inhelder, B., (1958) The Growth of Logical Thinking from Childhood to Adolescence New York: Basic Books, 1958
Searle, J. (1996) The construction of social reality New York: Simon & Shuster
Vygotsky, L. (1978) Mind in Society: Development of Higher Psychological Processes Cambridge MA: Harvard University Press
Activity 2.5 Defining the limits of constructivism
1. What areas of knowledge do you think would be best ‘taught’ or learned through a constructivist approach?
2. What areas of knowledge do you think would NOT be appropriately taught through a constructivist approach?
3. What are your reasons?
Feedback to come | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/02%3A_The_nature_of_knowledge_and_the_implications_for_teaching/02.5%3A_Constructivism.txt |
Figure 2.6.1 Stephen Downes Image: Wikipedia
Figure 2.6.2 George Siemens Image: Wikipedia
2.6.1 What is connectivism?
Another epistemological position, connectivism, has emerged in recent years that is particularly relevant to a digital society. Connectivism is still being refined and developed, and it is currently highly controversial, with many critics.
In connectivism it is the collective connections between all the ‘nodes’ in a network that result in new forms of knowledge. According to Siemens (2005), knowledge is created beyond the level of individual human participants, and is constantly shifting and changing. Knowledge in networks is not controlled or created by any formal organization, although organizations can and should ‘plug in’ to this world of constant information flow, and draw meaning from it. Knowledge in connectivism is a chaotic, shifting phenomenon as nodes come and go and as information flows across networks that themselves are inter-connected with myriad other networks.
The significance of connectivism is that its proponents argue that the Internet changes the essential nature of knowledge. ‘The pipe is more important than the content within the pipe,’ to quote Siemens again. Downes (2007) makes a clear distinction between constructivism and connectivism:
In connectivism, a phrase like “constructing meaning” makes no sense. Connections form naturally, through a process of association, and are not “constructed” through some sort of intentional action. …Hence, in connectivism, there is no real concept of transferring knowledge, making knowledge, or building knowledge. Rather, the activities we undertake when we conduct practices in order to learn are more like growing or developing ourselves and our society in certain (connected) ways.
Figure 2.6.3: A map of connectivism Image: © pkab.wordpress.com. Click and drag for a larger image.
2.6.2 Connectivism and learning
For Siemens (2005), it is the connections and the way information flows that result in knowledge existing beyond the individual. Learning becomes the ability to tap into significant flows of information, and to follow those flows that are significant. He argues that:
Connectivism presents a model of learning that acknowledges the tectonic shifts in society where learning is no longer an internal, individualistic activity….Learning (defined as actionable knowledge) can reside outside of ourselves (within an organization or a database).
Siemens (2005) identifies the principles of connectivism as follows:
• Learning and knowledge rests in diversity of opinions.
• Learning is a process of connecting specialized nodes or information sources.
• Learning may reside in non-human appliances.
• Capacity to know more is more critical than what is currently known
• Nurturing and maintaining connections is needed to facilitate continual learning.
• Ability to see connections between fields, ideas, and concepts is a core skill.
• Currency (accurate, up-to-date knowledge) is the intent of all connectivist learning activities.
• Decision-making is itself a learning process. Choosing what to learn and the meaning of incoming information is seen through the lens of a shifting reality. While there is a right answer now, it may be wrong tomorrow due to alterations in the information climate affecting the decision.
Downes (2007) states that:
at its heart, connectivism is the thesis that knowledge is distributed across a network of connections, and therefore that learning consists of the ability to construct and traverse those networks….[Connectivism] implies a pedagogy that:
(a) seeks to describe ‘successful’ networks (as identified by their properties, which I have characterized as diversity, autonomy, openness, and connectivity) and
(b) seeks to describe the practices that lead to such networks, both in the individual and in society – which I have characterized as modelling and demonstration (on the part of a teacher) – and practice and reflection (on the part of a learner).
2.6.3 Applications of connectivism to teaching and learning
Siemens, Downes and Cormier constructed the first massive open online course (MOOC), Connectivism and Connective Knowledge 2011, partly to explain and partly to model a connectivist approach to learning.
Connectivists such as Siemens and Downes tend to be somewhat vague about the role of teachers or instructors, as the focus of connectivism is more on individual participants, networks and the flow of information and the new forms of knowledge that result. The main purpose of a teacher appears to be to provide the initial learning environment and context that brings learners together, and to help learners construct their own personal learning environments that enable them to connect to ‘successful’ networks, with the assumption that learning will automatically occur as a result, through exposure to the flow of information and the individual’s autonomous reflection on its meaning. There is no need for formal institutions to support this kind of learning, especially since such learning often depends heavily on social media readily available to all participants.
There are numerous criticisms of the connectivist approach to teaching and learning (see Chapter 5, Section 4). Some of these criticisms may be overcome as practice improves, as new tools for assessment, and for organizing co-operative and collaborative work with massive numbers, are developed, and as more experience is gained. More importantly, connectivism is really the first theoretical attempt to radically re-examine the implications for learning of the Internet and the explosion of new communications technologies.
References and further reading
AlDahdouh, A., et al. (2015) Understanding knowledge network, learning and connectivism, International Journal of Instructional Technology and Distance Learning, Vol. 12, No.10
Downes, S. (2007) What connectivism is Half An Hour, February 3
Downes, S. (2014) The MOOC of One, Stephen’s Web, March 10
Siemens, G. (2005) Connectivism: a theory for the digital age International Journal of Instructional Technology and Distance Learning, Vol. 2, No. 1.
Activity 2.6 Defining the limits of connectivism
1. What areas of knowledge do you think would be best ‘taught’ or learned through a connectivist approach?
2. What areas of knowledge do you think would NOT be appropriately taught through a connectivist approach?
3. What are your reasons?
You might like to come back to your answer after you have read Chapter 6 on MOOCs. Otherwise no feedback is provided for this activity. | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/02%3A_The_nature_of_knowledge_and_the_implications_for_teaching/02.6%3A_Connectivism.txt |
Figure 2.7 Academic knowledge is a second-order form of knowledge that seeks abstractions and generalizations based on reasoning and evidence Image: © Wallpoper/Wikipedia
2.7.1 Knowledge and technology
Before moving on to the more pragmatic elements of teaching in a digital age, it is necessary to address the question of whether the development of digital technologies has actually changed the nature of knowledge, because if that is the case, then this will influence strongly what needs to be taught as well as how it will be taught.
Connectivists such as Siemens and Downes argue that the Internet has changed the nature of knowledge. They argue that ‘important’ or ‘valid’ knowledge now is different from prior forms of knowledge, particularly academic knowledge. Downes (2007) has argued that new technologies allow for the de-institutionalisation of learning. Chris Anderson, the editor of Wired Magazine and now Curator of Ted Talks, has argued (2008) that massive meta-data correlations can replace ‘traditional’ scientific approaches to creating new knowledge:
Google’s founding philosophy is that we don’t know why this page is better than that one: If the statistics of incoming links say it is, that’s good enough. No semantic or causal analysis is required. …This is a world where massive amounts of data and applied mathematics replace every other tool that might be brought to bear. Out with every theory of human behavior, from linguistics to sociology. Forget taxonomy, ontology, and psychology. Who knows why people do what they do? The point is they do it, and we can track and measure it with unprecedented fidelity. With enough data, the numbers speak for themselves.
The big target here isn’t advertising, though. It’s science. The scientific method is built around testable hypotheses. These models, for the most part, are systems visualized in the minds of scientists. The models are then tested, and experiments confirm or falsify theoretical models of how the world works. This is the way science has worked for hundreds of years. Scientists are trained to recognize that correlation is not causation, that no conclusions should be drawn simply on the basis of correlation between X and Y (it could just be a coincidence). Instead, you must understand the underlying mechanisms that connect the two. Once you have a model, you can connect the data sets with confidence. Data without a model is just noise. But faced with massive data, this approach to science — hypothesize, model, test — is becoming obsolete.’
(It should be noted this was written before derivative-based investments caused financial markets to collapse, mainly because those using them didn’t understand the underlying logic that created the data.)
Jane Gilbert’s book, ‘Catching the Knowledge Wave’ (2005), directly addresses the assumption that the nature of knowledge is changing. Drawing on publications by Manuel Castells (2009) and Jean-François Lyotard (1984), she writes (p. 35):
‘Castells says that…knowledge is not an object but a series of networks and flows…the new knowledge is a process not a product…it is produced not in the minds of individuals but in the interactions between people…..
According to Lyotard, the traditional idea that acquiring knowledge trains the mind would become obsolete, as would the idea of knowledge as a set of universal truths. Instead, there will be many truths, many knowledges and many forms of reason. As a result… the boundaries between traditional disciplines are dissolving, traditional methods of representing knowledge (books, academic papers, and so on) are becoming less important, and the role of traditional academics or experts are undergoing major change.’
Back in the 1960s Marshall McLuhan (1964) argued that the medium is the message; the way information is represented and transmitted is changed and so is our focus and understanding as information moves between and within different media. If information and knowledge are now represented and more significantly now flow differently, how does that affect educational processes such as teaching and learning?
One way knowledge is certainly changing is in the way it is represented. It should be remembered that Socrates (according to Plato) criticised writing because it could not lead to ‘true’ knowledge which came only from verbal dialogue and oratory. Writing however is important because it provides a permanent record of knowledge. The printing press was important because it enabled the written word to spread to many more people. As a consequence, scholars could challenge and better interpret, through reflection, what others had written, and more accurately and carefully argue their own positions. Many scholars believe that one consequence of the development of mass printing was the Renaissance and the age of enlightenment, and modern academia consequently came to depend very heavily on the print medium.
Now we have other ways to record and transmit knowledge that can be studied and reflected upon, such as video, audio, animations, and graphics, and the Internet does expand enormously the speed and range by which these representations of knowledge can be transmitted. We shall also see in Chapter 8 and Chapter 9 that that media are not neutral, but represent meaning in different ways.
2.7.2 Knowledge as a commodity
All the above authors agree that the ‘new’ knowledge in the knowledge society is about the commercialisation or commodification of knowledge: ‘it is defined not through what it is, but through what it can do.’ (Gilbert, p.35). ‘The capacity to own, buy and sell knowledge has contributed, in major ways, to the development of the new, knowledge-based societies.’ (p.39)
In a knowledge-based society, particular emphasis is placed on the utility of knowledge for commercial purposes. As a result there is more emphasis on certain types of immediately practical knowledge over longer term research, for instance, but because of the strong relationship between pure and applied knowledge, this is probably a mistake, even in terms of economic development.
The issue is not so much the nature of knowledge, but how students or learners come to acquire that knowledge and learn how it can be used. As I argued in Chapter 1, this requires more emphasis on developing and learning skills of how best to apply knowledge, rather than a focus on merely teaching content. Also it will be argued later in the book that students have many more sources of information besides the teacher or instructor and that a key educational issue is the management of vast amounts of knowledge. Since knowledge is dynamic, expanding and constantly changing, learners need to develop the skills and learn to use the tools that will enable them to continue to learn.
But does this mean that knowledge itself is now different? I will argue that in a digital age, some aspects of knowledge do change considerably, but others do not, at least in essence. In particular, I argue that academic knowledge, in terms of its values and goals, does not and should not change a great deal, but the way it is represented and applied will and should change.
2.7.3 The nature of academic knowledge
Academic knowledge is a specific form of knowledge that has characteristics that differentiate it from other kinds of knowledge, and particularly from knowledge or beliefs based solely on direct personal experience. In summary, academic knowledge is a second-order form of knowledge that seeks abstractions and generalizations based on reasoning and evidence.
Fundamental components of academic knowledge are
• transparency,
• codification,
• reproduction, and
• communicability.
Transparency means that the source of the knowledge can be traced and verified. Codification means that the knowledge can be consistently represented in some form (words, symbols, video) that enables interpretation by someone other than the originator. Knowledge can be reproduced or have multiple copies. Lastly, knowledge must be in a form such that it can be communicated and challenged by others.
Laurillard (2001) recognises the importance of relating the student’s direct experience of the world to an understanding of academic concepts and processes, but she argues that teaching at a university level must go beyond direct experience to reflection, analysis and explanations of those direct experiences. Because every academic discipline has a specific set of conventions and assumptions about the nature of knowledge within its discipline, students in higher education need to change the perspectives of their everyday experience to match those of the subject domain.
As a result, Laurillard argues that university teaching is ‘essentially a rhetorical activity, persuading students to change the way they experience the world’ (p.28). Laurillard then goes on to make the point that because academic knowledge has this second-order character, it relies heavily on symbolic representation, such as language, mathematical symbols, ‘or any symbol system that can represent a description of the world, and requires interpretation’ (p.27) to enable this mediation to take place.
If academic knowledge requires mediation, then this has major significance for the use of technology. Language (i.e. reading and speaking) is only one channel for mediating knowledge. Media such as video, audio, and computing can also provide teachers with alternative channels of mediation.
Laurillard’s reflections on the nature of academic knowledge are a counter-balance to the view that students can automatically construct knowledge through argument and discussion with their peers, or self-directed study, or the wisdom of the crowd. For academic knowledge, the role of the teacher is to help students understand not just the facts or concepts in a subject discipline, but the rules and conventions for acquiring and validating knowledge within that subject discipline. Academic knowledge shares common values or criteria, making academic knowledge itself a particular epistemological approach.
2.7.4 Academic versus applied knowledge
In a knowledge-based society, knowledge that leads to innovation and commercial activity is now recognised as critical to economic development. Again, there is a tendency to argue that this kind of knowledge – ‘commercial’ knowledge – is different from academic knowledge. I would argue that sometimes it is and sometimes it isn’t.
I have no argument with the point of view that knowledge is the driver of most modern economies, and that this represents a major shift from the ‘old’ industrial economy, where natural resources (coal, oil, iron), machinery and cheap manual labour were the predominant drivers. I do though challenge the idea that the nature of knowledge has undergone radical changes.
The difficulty I have with the broad generalisations about the changing nature of knowledge is that there have always been different kinds of knowledge. One of my first jobs was in a brewery in the East End of London in 1959. I was one of several students hired during our summer vacation. One of my fellow student workers was a brilliant mathematician. Every lunch hour the regular brewery workers played cards (three card brag) for what seemed to us large sums of money, but they would never let us play with them. My student friend was desperate to get a game, and eventually, on our last week, they let him in. They promptly won all his wages. He knew the numbers and the odds, but there was still a lot of non-academic knowledge he didn’t know about playing cards for money, especially against a group of friends playing together rather than against each other. Gilbert’s point is that academic knowledge has always been more highly valued in education than ‘everyday’ knowledge. However, in the ‘real’ world, all kinds of knowledge are valued, depending on the context. Thus while beliefs about what constitutes ‘important’ knowledge may be changing, this does not mean that the nature of academic knowledge is changing.
Gilbert argues that in a knowledge society, there has been a shift in valuing applied knowledge over academic knowledge in the broader society, but this has not been recognised or accepted in education (and particularly the school system). She sees academic knowledge as associated with narrow disciplines such as mathematics and philosophy, whereas applied knowledge is knowing how to do things, and hence by definition tends to be multi-disciplinary. Gilbert argues (p. 159-160) that academic knowledge is:
‘authoritative, objective, and universal knowledge. It is abstract, rigorous, timeless – and difficult. It is knowledge that goes beyond the here and now knowledge of everyday experience to a higher plane of understanding…..In contrast, applied knowledge is practical knowledge that is produced by putting academic knowledge into practice. It is gained through experience, by trying things out until they work in real-world situations.’
Other kinds of knowledge that don’t fit the definition of academic knowledge are those kinds built on experience, traditional crafts, trial-and-error, and quality improvement through continuous minor change built on front-line worker experience – not to mention how to win at three card brag.
I agree that academic knowledge is different from everyday knowledge, but I challenge the view that academic knowledge is ‘pure’, not applied. It is too narrow a definition, because it thus excludes all the professional schools and disciplines, such as engineering, medicine, law, business, education that ‘apply’ academic knowledge. These are just as accepted and ‘valued’ parts of universities and colleges as the ‘pure’ disciplines of humanities and science, and their activities meet all the criteria for academic knowledge set out by Gilbert.
Making a distinction between academic and applied knowledge misses the real point about the kind of education needed in a knowledge society and a digital age. It is not just knowledge – both pure and applied – that is important, but also digital literacy, skills associated with lifelong learning, and attitudes/ethics and social behaviour.
Knowledge is not just ‘stuff’, or fixed content, but it is dynamic. Knowledge is also not just ‘flow’. Content or ‘stuff’ does matter as well as the discussions or interpretations we have about content. Where does the ‘stuff’ come from that ebbs and flows over the discussions on the internet? It may not originate or end in the heads of individuals, but it certainly flows through them, where it is interpreted and transformed. Knowledge may be dynamic and changing, but at some point each person does settle, if only for a brief time, on what they think knowledge to be, even if over time that knowledge changes, develops or becomes more deeply understood. Thus ‘stuff’ or content does matter, though knowing (a) how to acquire content and (b) what to do with content we have acquired, is even more important.
Thus it is not sufficient just to teach academic content (applied or not). It is equally important also to enable students to develop the ability to know how to find, analyse, organise and apply information/content within their professional and personal activities, to take responsibility for their own learning, and to be flexible and adaptable in developing new knowledge and skills. All this is needed because of the explosion in the quantity of knowledge in any professional field that makes it impossible to memorise or even be aware of all the developments that are happening in the field, and the need to keep up-to-date within the field after graduating.
To do this learners must have access to appropriate and relevant content, know how to find it, and must have opportunities to apply and practice what they have learned. Thus learning has to be a combination of content, skills and attitudes, and increasingly this needs to apply to all areas of study. This does not mean that there is no room to search for universal truths, or fundamental laws or principles, but this needs to be embedded within a broader learning environment. This should include the ability to use digital technologies as an integral part of learning, but tied to appropriate content and skills within their area of study.
Also, the importance of non-academic knowledge in the growth of knowledge-based industries should not be ignored. These other forms of knowledge have proved just as valuable. For instance it is important within a company to manage the every-day knowledge of employees through better internal communication, encouraging external networking, and rewards for collaboration and participation in improving products and services.
2.7.5 The relevance of academic knowledge in the knowledge society
An over-emphasis on the functionality of knowledge will result in ‘academic knowledge’ being implicitly seen as irrelevant to the knowledge society. However, it has been the explosion in academic knowledge that has formed the basis of the knowledge society. It was academic development in sciences, medicine and engineering that led to the development of the Internet, biotechnology, digital financial services, computer software and telecommunications, for example. Indeed, it is no coincidence that those countries most advanced in knowledge-based industries were those that have the highest participation rates in university education.
Thus while academic knowledge is not ‘pure’ or timeless or objectively ‘true’, it is the principles or values that drive academic knowledge that are important. Although it often falls short, the goal of academic studies is to reach for deep understanding, general principles, empirically-based theories, timelessness, etc., even if knowledge is dynamic, changing and constantly evolving. Academic knowledge is not perfect, but does have value because of the standards it requires. Nor have academic knowledge or methods run out of steam. There is evidence all around us: academic knowledge is generating new drug treatments, new understandings of climate change, better technology, and certainly new knowledge generation.
Indeed, more than ever, we need to sustain the elements of academic knowledge, such as rigour, abstraction, evidence-based generalisation, empirical evidence, rationalism and academic independence. It is these elements of education that have enabled the rapid economic growth both in the industrial and the knowledge societies. The difference now is that these elements alone are not enough; they need to be combined with new approaches to teaching and learning.
2.7.6 Academic knowledge and other forms of knowledge
As mentioned earlier, there are many other forms of knowledge that are useful or valued besides academic knowledge. There is increasing emphasis from government and business on the development of vocational or trades skills. Teachers or instructors are responsible for developing these areas of knowledge as well. In particular, skills that require manual dexterity, performance skills in music or drama, production skills in entertainment, skills in sport or sports management, are all examples of forms of knowledge that have not traditionally been considered ‘academic’.
However, one feature of a digital society is that increasingly these vocational skills are now requiring a much higher proportion of academic knowledge or intellectual and conceptual knowledge as well as performance skills. For example higher levels of ability in math and/or science are now demanded of many trades and professions such as network engineers, power engineers, auto mechanics, nurses and other health professionals. The ‘knowledge’ component of their work has increased over recent years.
The nature of the job is also changing. For instance, auto mechanics are now increasingly focused on diagnosis and problem-solving as the value component of vehicles becomes increasingly digitally based and components are replaced rather than repaired. Nurse practitioners now are undertaking areas of work previously done by doctors or medical specialists. Many workers now also need strong inter-personal skills, especially if they are in front-line contact with the public. At the same time, as we saw in Chapter 1, more traditionally academic areas are needing to focus more on skills development, so the somewhat artificial boundaries between pure and applied knowledge are beginning to break down.
In summary, a majority of jobs now require both academic and skills-based knowledge. Academic and skills-based knowledge also need to be integrated and contextualised. As a result, the demands on those responsible for teaching and instruction have increased, but above all, these new demands of teachers in a digital age mean that their own skills level needs to be increased to cope with these demands.
Activity 2.7 Epistemology and academic knowledge
1. Can you state the epistemological position that drives your teaching? Does it fit with any of the epistemological positions described in this chapter? How does that work out in practice in terms of what you do?
2. Can you justify the role of ‘teacher’ in a digital society where individuals can find all they need on the Internet and from friends or even strangers? How do you think that the role of the teacher might, could or should change as a result of the development of a digital society? Or are there ‘constants’ that will remain?
3. Briefly define the subject area or speciality in which you are teaching. Do you agree that academic knowledge is different from everyday knowledge? If so, to what extent is academic knowledge important for your learners? Is its importance growing or diminishing? Why? If it is diminishing, what is it being replaced with – or what should replace it?
Feedback to come | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/02%3A_The_nature_of_knowledge_and_the_implications_for_teaching/02.7%3A_Is_the_nature_of_knowledge_changing.txt |
Figure 2.8.1 The green boxes are left open until we cover teaching methods (the bottom line) in Chapters 3 and 4
I have chosen just a few epistemological approaches that influence teaching and learning, but I could have chosen many others. Theologies reflect another epistemological approach, based on faith. Elements of scholasticism can still be found in elite universities such as Oxford and Cambridge, particularly in their tutorial system.
It can be seen then that there are different epistemologies that influence teaching today. Furthermore, much to the consternation and confusion of many students, teachers themselves will have different epistemological positions, not just across different disciplines, but sometimes within the same discipline. For instance, subject areas such as psychology and economics may contain different epistemological foundations in different parts of the curriculum: statistics is validated differently from Freudian analysis or behavioural factors that influence investor behaviour.
Epistemological positions are rarely explicitly discussed with students, are not always consistent even within a subject discipline, and are not mutually exclusive. For instance a teacher may deliberately choose to use a more objectivist approach with novice students, then move to a more constructivist approach when the students have learned the basic facts and concepts within a topic through an objectivist approach. Even within the same lesson, the teacher may shift epistemological positions, often causing confusion for students.
At this point, I’m not taking sides (although I do favour in general a more constructivist philosophy). Arguments can be made for or against any of these epistemological positions. However, we need to be aware that knowledge and consequently teaching is not a pure, objective concept, but driven by different values and beliefs about the nature of knowledge.
Arguments are also being made today that academic knowledge is now redundant and is being or will be replaced by networked learning or more applied learning. I have made the case though that there are strong reasons to sustain and further develop academic knowledge, but with a focus as much on the development of skills as on learning content.
Different theories of learning reflect different positions on the nature of knowledge. With the possible exception of connectivism, there is some form of empirical evidence to support each of the theories of learning outlined in this chapter. However, while the theories suggest different ways in which all people learn, they do not automatically tell teachers or instructors how to teach. Indeed, theories of behaviourism, cognitivism and constructivism were all developed outside of education, in experimental labs, psychology, neuroscience, and psychotherapy. Educators have had to work out how to move from the theoretical position to the practical one of applying these theories within an educational experience. In other words, they have had to develop teaching methods that build on such learning theories.
For my personal comments on the relationship between epistemologies, theories of learning and teaching methods, please click on the podcast below
An audio element has been excluded from this version of the text. You can listen to it online here: https://pressbooks.bccampus.ca/teachinginadigitalagev2/?p=78
The next chapter examines a range of teaching methods that have been developed, their epistemological roots, and their implications for teaching in a digital age.
Reference
Entwistle, N. (2010) ‘Taking Stock: An Overview of Research Findings’ in Christensen Hughes, J. and Mighty, J. (eds.) Taking Stock: Research on Teaching and Learning in Higher Education Montreal and Kingston: McGill-Queen’s University Press
For more on the relationship between epistemologies, learning theories and methods of teaching, see:
Bates, T. (2015) Thinking about theory and practice, Open Learning and Distance Education Resources, July 29
Activity 2.8 Choosing a theory of learning
Entwistle (2010) states:
There are some important questions to ask when considering how much weight to place on evidence or how valuable a theory will be for pedagogy. For example:
• Is the theory derived from data or observations in an educational context?
• Is the theory presented in language that is readily intelligible to teachers?
• Can the aspects identified as affecting learning be readily changed [by the teacher]?
• Does the theory have direct implications for teaching and learning [in the particular context in which you are working]?
• How realistic and practical are the suggestions?
• Will the theory spark off new ideas about teaching?
It is not sufficient for a pedagogical theory simply to explain how people learn; it also has to provide clear implications about how to improve the quality and efficiency of learning.
Using Entwistle’s criteria and your own knowledge and experience of teaching, answer the questions below.
1. Which theory of learning do you like best, and why? State what main subject you are teaching.
2. Does your preferred way of teaching match any of these theoretical approaches? Write down some of the activities you do when teaching that ‘fit’ with this theory. Can you think of other possible activities you now could use within this theoretical framework for teaching?
3. Does your teaching generally combine different theories – sometimes behaviourist, sometimes cognitive, etc.? If so, what are the reasons or contexts for taking one specific approach rather than another?
4. How useful are these theories in terms of teaching practice? In your view, are they just jargon or useless theorising, or ‘labelling’ of commonly understood practice, or do they provide strong guidelines for how you should teach?
5. How do you think new digital technologies, such as social media, affect these theories? Do new technologies make these theories redundant? Does connectivism replace other theories or merely add another way of looking at teaching and learning?
Feedback to come.
Key Takeaways
1. Teaching is a highly complex occupation, which needs to adapt to a great deal of variety in context, subject matter and learners. It does not lend itself to broad generalizations. Nevertheless it is possible to provide guidelines or principles based on best practices, theory and research, that must then be adapted or modified to local conditions.
2. Our underlying beliefs and values, usually shared by other experts in a subject domain, shape our approach to teaching. These underlying beliefs and values are often implicit and are often not directly shared with our students, even though they are seen as essential components of becoming an ‘expert’ in a particular subject domain.
3. It is argued that academic knowledge is different from other forms of knowledge, and is even more relevant today in a digital age.
4. However, academic knowledge is not the only kind of knowledge that is important in today’s society, and as teachers we have to be aware of other forms of knowledge and their potential importance to our students, and make sure that we are providing the full range of contents and skills needed for students in a digital age. | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/02%3A_The_nature_of_knowledge_and_the_implications_for_teaching/02.8%3A_Summary.txt |
For my personal comments on why I wrote this chapter on campus-based teaching methods, please click on the podcast below
An audio element has been excluded from this version of the text. You can listen to it online here: https://pressbooks.bccampus.ca/teachinginadigitalagev2/?p=83
The first thing to be said about teaching methods is that there is no law or rule that says teaching methods are driven by theories of learning. Especially in post-secondary education, most instructors would be surprised if their teaching was labelled as behaviourist or constructivist. On the other hand, it would be less than accurate to call such teaching ‘theory-free’. We have seen how views about the nature of knowledge are likely to impact on preferred teaching methods. But it would be unwise to press this too hard. A great deal of teaching, at least at a post-secondary level, is based on an apprenticeship model of copying the same methods used by one’s own teachers, then gradually refining them from experience, without a great deal of attention being paid to theories of how students actually learn.
Dan Pratt (1998) studied 253 teachers of adults, across five different countries, and identified ‘five qualitatively different perspectives on teaching,… presenting each perspective as a legitimate view of teaching‘:
• transmission: effective delivery of content (an objectivist approach)
• apprenticeship: modelling ways of being (learning by doing under supervision)
• developmental: cultivating ways of thinking (constructivist/cognitivist)
• nurturing: facilitating self-efficacy (a fundamental tenet of connectivist MOOCs)
• social reform: seeking a better society.
It can be seen that each of these perspectives relates to theories of learning to some extent, and they help to drive methods of teaching. So in practical terms, I will start by looking at some common methods of teaching, and assessing their appropriateness for developing the knowledge and skills outlined in Chapter 1.
I will organise these various methods of teaching into two chapters. The first chapter will discuss design models that derive from more traditional school or campus-based teaching, and the second chapter will be focused on design models that make more use of Internet technologies, although we will see in Chapter 10 that these distinctions are already beginning to break down.
03.1: Scenario C: A stats lecturer fights the system
Figure 3 C Image: Verywellmind.com
Clive (looking carefully at his partner, Jean): So what went wrong at work today?
Jean: So you noticed – nice.
Clive: Now don’t take it out on me. How could I have avoided the slamming of the door, the shouting at the cat, and the almost instant demand for a large glass of wine – which incidentally is sitting on your desk?
Jean (grabbing the wine). Well, today was the last straw. I got the results of the student end-of-term evaluation of my new class I’ve been teaching.
Clive: Bad, eh?
Jean: Well, first the rankings are odd: about 30 per cent As, about 5 per cent Bs, 15 per cent Cs, 15 per cent D’s and 35 per cent E’s – NOT a normal curve of distribution! They either loved me or hated me, but the average – which is all Harvey, the stupid head of department, looks at – came out as a D, which means any chance of a promotion next year just went straight out the window. I’m now going to have to explain myself to that old buffoon who last taught a class when slate tablets were the latest technology.
Clive: I’m not going to say I told you so, but…..
Jean: DON’T go there. I know I’m bloody mad to have stopped lecturing and tried to engage the students more. I could kill that faculty development guy who persuaded me to change how I teach. I didn’t mind all the extra work, not even the continual fighting with the guy from Facilities who kept telling me to put all the tables and chairs back properly – he was just a jerk – and I loved the actual teaching, which was stimulating and deeply satisfying, but what really finished me was when the department wouldn’t change the exam. I’ve been trying to get the kids to question what is meant by a sample, discuss alternative ways of looking at significance, solve problems, and then they go and give the poor kids multiple-choice questions that just assessed their memory of statistical techniques and formulae. No wonder most of the students were mad at me.
Clive: But you’ve always claimed that the students enjoyed your new way of teaching.
Jean: Well, I was fooled by them. From the student comments on the evaluation, it seemed that about a third of them really did like the lessons and some even said it opened up their eyes to what statistics is all about, but apparently what the rest wanted was just a crib sheet they could use to answer the exam questions.
Clive: So what are you going to do now?
Jean: I honestly don’t know. I know what I’m doing is right, now I’ve been through all the changes. Those kids won’t have crib sheets when they start work, they will have to interpret data, and when they get into advanced level science and engineering courses they won’t be able to use statistics properly if I just teach to the exam. They will know a bit about statistics but not how to do it properly.
Clive: So you’ll have to get the department to agree to changing the exam.
Jean: Yeah, good luck with that, because everyone else will have to change how they teach if we do that.
Clive: But I thought the whole reason for you changing your teaching was that the university was worried it wasn’t producing graduates with the right kind of skills and knowledge needed today.
Jean: You’re right, but the problem is Harvey won’t support me – he’s old school down to his socks and underpants and thinks that what I am doing is just trendy – and without his support there’s no way the rest of the department is going to change.
Clive: OK, so just relax for now and have a glass of wine and we’ll go out somewhere nice for dinner. That will help clear my mind of the thought of Harvey in his socks and underpants. Then you can hear about my day. | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/03%3A_Methods_of_teaching%3A_campus-focused/03.1%3A_Five_perspectives_on_teaching.txt |
Figure 3.2.1 Miss Bowls’s class in an unidentified girls’ school, England Date: circa 1905
Image: Southall Board, Flickr
Our institutions are a reflection of the times in which they were created. Francis Fukuyama, in his monumental writing on political development and political decay (2011, 2014), points out that institutions that provide essential functions within a state often become so fixed over time in their original structures that they fail to adapt and adjust to changes in the external environment. We need therefore to examine in particular the roots of our modern educational systems, because teaching and learning in the present day is still strongly influenced by institutional structures developed many years ago. Thus, we need to examine the extent to which our traditional campus-based models of teaching remain fit for a digital age.
The large urban school, college or university, organized by age stratification, learners meeting in groups, and regulated units of time, was an excellent fit for an industrial society. In many ways it matched the way work was organised in factories. In effect, we still have a predominantly industrial model of educational design, which in large part remains our default design model even today.
Some design models are so embedded in tradition and convention that we are often like fish in water – we just accept that this is the environment in which we have to live and breath. The classroom model is a very good example of this. In a classroom based model, learners are organised in classes that meet on a regular basis at the same place at certain times of the day for a given length of time over a given period (a term or semester).
This is a design decision that was taken more than 150 years ago. It was embedded in the social, economic and political context of the 19th century. This context included:
• the industrialization of society which provided ‘models’ for organizing both work and labour, such as factories and mass production;
• the movement of people from rural to urban occupations and communities, with increased density resulting in larger institutions;
• the move to mass education to meet the needs of industrial employers and an increasingly large and complex range of state-managed activities, such as government, health and education;
• voter enfranchisement and hence the need for a better educated voting public;
• over time, demand for more equality, resulting in universal access to education.
However, over the span of 150 years, our society has slowly changed. Many of these factors or conditions no longer exist, while others persist, but often in a less dominant way than in the past. Thus we still have factories and large industries, but we also have many more small companies, greater social and geographical mobility, and above all a massive development of new technologies that allow both work and education to be organized in different ways.
This is not to say that the classroom design model is inflexible. Teachers for many years have used a wide variety of teaching approaches within this overall institutional framework. But in particular, the way in which our institutions are structured strongly affects the way we teach. We need to examine which of the methods built around a classroom model are still appropriate in today’s society, and, more of a challenge, whether we could build new or modified institutional structures that would better meet the needs of today.
References
Fukuyama, F. (2011) The Origins of Political Order: From Prehuman Times to the French Revolution New York: Farrar Strauss and Giroux
Fukuyama, F. (2014) Political Order and Political Decay: From the Industrial Revolution to the Globalisation of Democracy New York: Farrar Strauss and Giroux
Activity 3.2 Thinking outside the [classroom] box
If classrooms, schools and university campuses are the physical products of an industrial age, what type of learning environment would be a product of a digital age? In other words, if we were starting from scratch today, how would you design an environment for teaching and learning that would reflect the age in which we live?
Obviously there is no right or wrong answer to this, but later you may want to look at Chapter 6 on learning environments to see if your ‘digital’ learning environment contains all the necessary components.
You may also want to consider the social, economic and political context of the 21st century in terms of identifying an appropriate learning environment. How much would the campus experience still be necessary? (For instance, how would the environment deal with the effects of too much screen time on children’s development?)
Listen to the podcast below for my response to this:
An audio element has been excluded from this version of the text. You can listen to it online here: https://pressbooks.bccampus.ca/teachinginadigitalagev2/?p=86 | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/03%3A_Methods_of_teaching%3A_campus-focused/03.2%3A_The_origins_of_the_classroom_design_model.txt |
Figure 3.3.1 The lecture is one of the most traditional forms of classroom teaching. Image: Lecture Hall, Baruch College, New York City – Wikipedia
3.3.1 Definition
[Lectures] are more or less continuous expositions by a speaker who wants the audience to learn something.’
Bligh, 2000
This specific definition is important as it excludes contexts where a lecture is deliberately designed to be interrupted by questions or discussion between instructors and students. This form of more interactive lecturing will be discussed in the next section (Chapter 3, Section 4).
3.3.2 The origins of the lecture
Transmissive lectures can be traced back as far as ancient Greek and Roman times, and certainly from at least the start of the European university, in the 13th century. The term ‘lecture’ comes from the Latin, meaning a reading. In the 13th century, most books were extremely rare. They were painstakingly handcrafted and illustrated by monks, often from fragments or collections of earlier and exceedingly rare and valuable scrolls from ancient Greek or Roman times, or were translated from Arabic sources, since much documentation was destroyed in Europe during the Dark Ages following the fall of the Roman empire. As a result, a university would often have only one copy of a book, and it may have been the only copy available in the world. The library and its collection therefore became critical to the reputation of a university, and professors had to borrow the only text from the library and literally read from it to the students, who dutifully wrote down their own version of the lecture.
Lectures themselves belong to an even longer oral tradition of learning, where knowledge is passed on by word of mouth from one generation to the next. In such contexts, accuracy and authority (or power in controlling access to knowledge) are critical for ‘accepted’ knowledge to be successfully transmitted. Thus accurate memory, repetition and a reference to authoritative sources become exceedingly important in terms of validating the information transmitted. The great sagas of the ancient Greeks and, much later, of the Vikings, are examples of the power of oral transmission of knowledge, continued even today through the myths and legends of many indigenous communities.
Figure 3.3.2 A medieval lecture
Artist: Laurentius de Voltolina;
Liber ethicorum des Henricus de Alemannia; Kupferstichkabinett SMPK, Berlin/Staatliche Museen Preussiischer Kulturbesitz, Min. 1233
This illustration from a thirteenth-century manuscript shows Henry of Germany delivering a lecture to university students in Bologna, Italy, in 1233. What is striking is how similar the whole context is to lectures today, with students taking notes, some talking at the back, and one clearly asleep. Certainly, if Rip Van Winkle awoke in a modern lecture theatre after 800 years of sleeping, he would know exactly where he was and what was happening.
Nevertheless, the lecture format has been questioned for many years. Samuel Johnson (1709-1784) over 200 years ago said of lectures:
People have nowadays…got a strange opinion that everything should be taught by lectures. Now, I cannot see that lectures can do as much good as reading the books from which the lectures are taken…Lectures were once useful, but now, when all can read, and books are so numerous, lectures are unnecessary.’
Boswell, 1791
What is remarkable is that even after the invention of the printing press, radio, television, and the Internet, the transmissive lecture, characterised by the authoritative instructor talking to a group of students, still remains the dominant methodology for teaching in many institutions, even in a digital age, where information is available at a click of a mouse. It could be argued that anything that has lasted this long must have something going for it. On the other hand, we need to question whether the transmissive lecture is still the most appropriate means of teaching, given all the changes that have taken place in recent years, and in particular given the kinds of knowledge and skills needed in a digital age.
3.3.3 What does research tell us about the effectiveness of lectures?
Whatever you may think of Samuel Johnson’s opinion, there has indeed been a great deal of research into the effectiveness of lectures, going back to the 1960s, and continued through until today. The most authoritative analysis of the research on the effectiveness of lectures remains Bligh’s (2000). He summarized a wide range of meta-analyses and studies of the effectiveness of lectures compared with other teaching methods and found consistent results:
• the lecture is as effective as other methods for transmitting information (the corollary of course is that other methods – such as video, reading, independent study, or Wikipedia – are just as effective as lecturing for transmitting information);
• most lectures are not as effective as discussion for promoting thought;
• lectures are generally ineffective for changing attitudes or values or for inspiring interest in a subject;
• lectures are relatively ineffective for teaching behavioural skills.
Bligh also examined research on student attention, on memorizing, and on motivation, and concluded (p.56):
We see evidence… once again to suppose that lectures should not be longer than twenty to thirty minutes – at least without techniques to vary stimulation.
These research studies have shown that in order to understand, analyze, apply, and commit information to long-term memory, the learner must actively engage with the material. In order for a lecture to be effective, it must include activities that compel the student to mentally manipulate the information. Many lecturers of course do this, by stopping and asking for comments or questions throughout the lecture – but many do not.
Again, although these findings have been available for a long time, and You Tube videos now last approximately eight minutes and TED talks 20 minutes at a maximum, teaching in many educational institutions is still organized around a standard 50 minute lecture session or longer, with, if students are lucky, a few minutes at the end for questions or discussion. There are two important conclusions from the research:
• even for the sole purpose for which lectures may be effective – the transmission of information – the 50 minute lecture needs to be well organized, with frequent opportunities for student questions and discussion (Bligh provides excellent suggestions on how to do this in his book);
• for all other important learning activities, such as developing critical thinking, deep understanding, and application of knowledge – the kind of skills needed in a digital age – lectures are ineffective. Other forms of teaching and learning – such as opportunities for discussion and student activities – are necessary.
3.3.4 Does new technology make lectures more relevant?
Over the years, institutions have made massive investments in adding technologies to support lecturing. Powerpoint presentations, multiple projectors and screens, clickers for recording student responses, even ‘back-chat’ channels on Twitter, enabling students to comment on a lecture – or more often, the lecturer – in real time (surely the worst form of torture for a speaker), have all been tried. Students have been asked to bring tablets or lap-tops to class, and universities in particular have invested millions of dollars in state of the art lecture theatres. Nevertheless, all this is just lipstick on a pig. The essence of a lecture remains the transmission of information, all of which is now readily and, in most cases, freely available in other media and in more learner-friendly formats.
I worked in a college where in one program all students had to bring laptops to class. At least in these classes, there were some activities to do related to the lecture that required the students to use the laptops during class time. However, in most classes this took less than 25 per cent of the lesson time. Most of the other time, students were talked at, and as a result used their laptops for other, mainly non-academic activities, especially playing online poker.
Faculty often complain about students use of technology such as mobile phones or tablets, for ‘non-relevant’ multitasking in class, but this misses the point. If most students have mobile phones or laptops, why are they still having to come to a lecture hall in person? Why can’t they get a podcast or a video of the lecture? Second, if they are coming, why are the lecturers not requiring them to use their mobile phones, tablets, or laptops for study purposes, such as finding sources? Why not break the students into small groups and get them to do some online research then come back with group answers to share with the rest of the class? If lectures are to be offered, the aim should be to make the lecture engaging in its own right, so the students are not distracted by their non-academic online activity.
3.3.5 Is there then no role for lectures in a digital age?
Lectures though still have their uses. One example is an inaugural lecture I attended for a newly appointed research professor. In this lecture, the professor summarised all the research he and his team had done, resulting in treatments for several cancers and other diseases. This was a public lecture, so he had to satisfy not only other leading researchers in the area, but also a lay public with often no science background. He did this by using excellent visuals and analogies. The lecture was followed by a small wine and cheese reception for the audience. The lecture worked for several reasons:
• first of all, it was a celebratory occasion bring together family, colleagues and friends;
• second, it was an opportunity to pull together nearly 20 years of research into a single, coherent narrative or story;
• third, the lecture was well supported by an appropriate use of graphics and video;
• lastly, he put a great deal of work into preparing this lecture and thinking about who would be in the audience – much more preparation than would have been the case if this was just one of many lectures in a course.
McKeachie and Svinicki (2006, p. 58) believe that lecturing is best used for:
• providing up-to-date material that can’t be found in one source;
• summarizing material found in a variety of sources;
• adapting material to the interests of a particular group;
• initially helping students discover key concepts, principles or ideas;
• modelling expert thinking.
The last point is important. Faculty often argue that the real value of a lecture is to provide a model for students of how the faculty member, as an expert, approaches a topic or problem. Thus the important point of the lecture is not the transmission of content (facts, principles, ideas), which the students could get from just reading, but an expert way of thinking about the topic. The trouble with this argument for lectures is three-fold:
• students are rarely aware that this is the purpose of the lecture, and therefore focus on memorizing the content, rather than the ‘modelling’ of expert thinking;
• faculty themselves are not explicit about how they are doing the modelling (or fail to offer other ways in which modelling could be used, so students can compare and contrast);
• students get no practice themselves in modelling this skill, even if they are aware of the modelling.
Perhaps more importantly, looking at McKeachie and Svinicki’s suggestions, would it not be better for the students, rather than the lecturer, to be doing these activities in a digital age?
So, yes, there are a few occasions when lectures work very well. But in a digital age they should not be the default model for regular teaching. There are much better ways to teach that will result in better learning over the length of a course or program.
3.3.6 Why are lectures still the main form of educational delivery?
Given all of the above, some explanation needs to be offered for the persistence of the lecture into the 21st century. Here are some suggestions:
• in fact, in many areas of education, the lecture has been replaced, particularly in many elementary or primary schools;
• architectural inertia: a huge investment has been made by institutions in facilities that support the lecture model. What is to happen to all that real estate if it is not used? (As Winston Churchill said, ‘We shape our buildings and our buildings shape us‘);
• in North America, the Carnegie unit of teaching, which is based on a notion of one hour per week of classroom time per credit over a 13 week period. It is easy then to divide a three credit course into 39 one hour lectures over which the curriculum for the course must be covered. It is on this basis that teaching load and resources are decided;
• faculty in post-secondary education have no other model for teaching. This is the model they are used to, and because appointment is based on training in research or work experience, and not on qualifications in teaching, they have no knowledge of how students learn or confidence or experience in other methods of teaching;
• many experts prefer the oral tradition of teaching and learning, because it enhances their status as an expert and source of knowledge; being allowed an hour of other people’s time to hear your ideas without major interruption is very satisfying on a personal level (at least for me when I’m lecturing);
• see Scenario C at the start of this chapter.
3.3.7 Is there a future for lectures in a digital age?
That depends on how far into the future one wants to look. Given the inertia in the system, lectures are likely still to predominate for another ten years, but after that, in most institutions, courses based on three lectures a week over 13 weeks will have disappeared. There are several reasons for this:
• all content can be easily digitalized and made available on demand at very low cost (see Chapter 11);
• institutions will be making greater use of dynamic video (not talking heads) for demonstration, simulations, animations, etc. Thus most content modules will be multi-media;
• third, open textbooks incorporating multi-media components and student activities will provide the content, organization and interpretation that are the rationale for most lectures;
• lastly, and most significantly, the priority for teaching will have changed from information transmission and organization to knowledge management, where students have the responsibility for finding, analyzing, evaluating, sharing and applying knowledge, under the direction of a skilled subject expert. Project-based learning, collaborative learning and situated or experiential learning will become much more widely prevalent. Also many instructors will prefer to use the time they would have spent on a series of lectures in providing more direct, individual and group learner support, thus bringing them into closer contact with learners.
This does not mean that lectures will disappear altogether, but they will be special events, and probably multi-media, synchronously and asynchronously delivered. Special events might include:
• a professor’s summary of her latest research,
• the introduction to a course,
• a point mid-way through a course for taking stock and dealing with common difficulties, or
• the wrap-up to a course.
Lectures will provide a chance for instructors to make themselves known, to impart their interests and enthusiasm, and to motivate learners, but this will be just one, relatively small, but important component of a much broader learning experience for students.
For a different and informed perspective on the role and future of lectures, see Christine Gross-Loh, 2016.
References
Bates, A. (1985) Broadcasting in Education: An Evaluation London: Constables
Bligh, D. (2000) What’s the Use of Lectures? San Francisco: Jossey-Bass
Boswell, J. (1791), The Life of Samuel Johnson, New York: Penguin Classics (edited by Hibbert, C., 1986)
Gross-Loh, C. (2016) Should colleges really eliminate the college lecture?The Atlantic, 14 July
McKeachie, W. and Svinicki, M. (2006) McKeachie’s Teaching Tips: Strategies, Research and Theory for College and University Teachers Boston/New York: Houghton Mifflin
Activity 3.3 The future of lectures
1. Do you agree that lectures are dead – or soon will be?
2. Look at the skills needed in a digital age described in Chapter 1. Which of these skills could lectures help develop? Would they need to be redesigned or modified to do this and if so, how?
For feedback on the second question click on the podcast below:
An audio element has been excluded from this version of the text. You can listen to it online here: https://pressbooks.bccampus.ca/teachinginadigitalagev2/?p=89 | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/03%3A_Methods_of_teaching%3A_campus-focused/03.3%3A_Transmissive_lectures%3A_learning_by_listening.txt |
Figure 3.4.1 A tutorial at Oriel College, Oxford University: Image: University of Oxford
3.4.1 The theoretical and research basis for dialogue and discussion
Researchers have identified a distinction, often intuitively recognised by instructors, between meaningful and rote learning (Asubel et al., 1978). Meaningful learning involves the learner going beyond memorization and surface comprehension of facts, ideas or principles, to a deeper understanding of what those facts, ideas or principles mean to them. Marton and Saljö, who have conducted a number of studies that examined how university students actually go about their learning, make the distinction between deep and surface approaches to learning (see, for instance, Marton and Saljö, 1997). Students who adopt a deep approach to learning tend to have a prior intrinsic interest in the subject. Their motivation is to learn because they want to know more about a topic. Students with a surface approach to learning are more instrumental. Their interest is primarily driven by the need to get a pass grade or qualification.
Subsequent research (e.g. Entwistle and Peterson, 2004) showed that as well as students’ initial motivation for study, a variety of other factors also influence students’ approaches to learning. In particular, surface approaches to learning are more commonly found when there is a focus on:
• information transmission,
• tests that rely mainly on memory,
• a lack of interaction and discussion.
On the other hand, deeper approaches to learning are found when there is a focus on:
• analytical or critical thinking or problem-solving,
• in-class discussion,
• assessment based on analysis, synthesis, comparison and evaluation.
Constructivists believe that knowledge is mainly acquired through social processes which are necessary to move students beyond surface learning to deeper levels of understanding. Connectivist approaches to learning also place heavy emphasis on networking learners, with all participants learning through interaction and discussion between each other, driven both by their individual interests and the extent to which these interests connect to the interests of other participants. The very large numbers participating in connectivist MOOCs (see Chapter 5) means that there is a high probability of converging interests for all participants, although those interests may vary considerably over the whole group.
Laurillard (2001), and Harasim (2017), have emphasised that academic knowledge requires students to move constantly from the concrete to the abstract and back again, and to build or construct knowledge based on academic criteria such as logic, evidence and argument. This in turn requires a strong teacher presence within a dialectical environment, in which argument and discussion within the rules and criteria of the subject discipline are encouraged and developed by the instructor or teacher. Laurillard calls this a rhetorical exercise, an attempt to get learners to think about the world differently. Conversation and discussion are critical if this is to be achieved.
The combination of theory and research here suggests the need for frequent interaction between students, and between teacher and students, for the kinds of learning needed in a digital age. This interaction usually takes the form of semi-structured discussion. I will now examine how this kind of learning has traditionally been facilitated by educators.
3.4.2 Seminars and tutorials
3.4.2.1 Definitions
A seminar is a group meeting (either face-to-face or online) where a number of students participate at least as actively as the teacher, although the teacher may be responsible for the design of the group experience, such as choosing topics and assigning tasks to individual students.
A tutorial is either a one-on-one session between a teacher and a student, or a very small group (three or four) of students and an instructor, where the learners are at least as active in discussion and presentation of ideas as the teacher.
3.4.2.2 Seminars
Seminars can range from six or more students, up to 30 students in the same group. Because the general perception is that seminars work best when numbers are relatively small, they tend to be found more at graduate level or the last year of undergraduate programs.
Figure 3.4.1 Socrates and his students: Painter: Johann Friedrich Greuter, 1590: (San Francisco, Achenbach Foundation for Graphic Arts)
Seminars and tutorials again have a very long history, going back at least to the time of Socrates and Aristotle. Both were tutors to the aristocracy of ancient Athens. Aristotle was the private tutor to Alexander the Great when Alexander was young. Socrates was the tutor of Plato, the philosopher, although Socrates denied he was a teacher, rebelling against the idea common at that time in ancient Greece that ‘a teacher was a vessel that poured its contents into the cup of the student’. Instead, according to Plato, Socrates used dialogue and questioning ‘to help others recognize on their own what is real, true, and good.’ (Stanford Encyclopedia of Philosophy.) Thus it can be seen that seminars and tutorials reflect a strongly constructivist approach to learning and teaching.
The format can vary a great deal. One common format, especially at graduate level, although similar practices can be found at the school/k-12 level, is for the teacher to set advance work for a selected number of students, and then have the selected students present their work to the whole group, for discussion, criticism and suggestions for improvement. Although there may be time for only two or three student presentations in each seminar, over a whole semester every student gets their turn. Another format is to ask all the students in a group to do some specified advanced reading or study, then for the teacher to introduce questions for general discussion within the seminar that requires students to draw on their earlier work.
3.4.2.3 Tutorials
Tutorials are a particular kind of seminar that are identified with Ivy League universities, and in particular Oxford or Cambridge. There may be as few as two students and a professor in a tutorial and the meeting often follows closely the Socratic method of the student presenting his or her findings and the professor rigorously questioning every assumption made by the student – and also drawing in the other student to the discussion.
Both these forms of dialogical learning can be found not only in classroom contexts, but also online. Online discussion will be discussed in more detail in Chapter 4, Section 4. However, in general, the pedagogical similarities between online and face-to-face discussions are much greater than the differences.
3.4.3 Are seminars a practical method in a massive education system?
For many faculty, the ideal teaching environment is Socrates sitting under the linden tree, with three or four dedicated and interested students. Unfortunately, the reality of mass higher education makes this impossible for all but the most elite and expensive institutions.
However, seminars for 25-30 students are not unrealistic, even in public undergraduate education. More importantly, they enable the kind of teaching and learning that are most likely to facilitate the types of skills needed from our students in a digital age. Seminars are flexible enough to be offered in class or online, depending on the needs of the students. They are probably best used when students have done individual work before the seminar. Of upmost importance, though, is the ability of teachers to teach successfully in this manner, which requires different skills from transmissive lecturing.
Although expansion of student numbers in higher education is part of the problem, it’s not the whole problem. Other factors, such as senior professors teaching less, and focusing mainly on graduate students, lead to larger classes at undergraduate level that use transmissive lecturing. And if more senior or experienced instructors switched from transmissive lectures, and instead required students to find and analyse content for themselves, this would free up more time for them to spend on seminar-type teaching.
So it as much an organizational issue, a matter of choice and priorities, as an economic issue. The more we can move towards a seminar approach to teaching and learning and away from large, transmissive lectures, the better, if we are to develop students with the skills needed in a digital age.
References
Ausubel, D., Novak, J., & Hanesian, H. (1978). Educational Psychology: A Cognitive View (2nd Ed.). New York: Holt, Rinehart & Winston.
Entwistle, N. and Peterson, E. (2004) Conceptions of Learning and Knowledge in Higher Education: Relationships with Study Behaviour and Influences of Learning Environments International Journal of Educational Research, Vol. 41, No. 6, pp. 407-428
Harasim, L. (2017) Learning Theory and Online Technologies New York/London: Taylor and Francis
Laurillard, D. (2001) Rethinking University Teaching: A Conversational Framework for the Effective Use of Learning Technologies [1]New York/London: Taylor and Francis[2]
Marton, F. & Saljö, R. (1997). ‘Approaches to learning’, in F. Marton, D. Hounsell, & N. Entwistle (Eds.), The experience of learning. Edinburgh: Scottish Academic Press.
Activity 3.4 Developing conceptual learning
1. What kind of teacher interventions in group discussions can you suggest that could help learners develop deep, conceptual learning?
2. How could you reorganise a lecture class of 200 or more students to develop group work and the development of conceptual learning?
Click on the podcast below for my suggestions:
An audio element has been excluded from this version of the text. You can listen to it online here: https://pressbooks.bccampus.ca/teachinginadigitalagev2/?p=92 | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/03%3A_Methods_of_teaching%3A_campus-focused/03.4%3A_Interactive_lectures_seminars_and_tutorials%3A_learning_by_talking.txt |
Figure 3.5.1 Ryerson University’s Law Practice program is a good example of a blended learning approach to experiential learning. For more details, click here
Learning by doing is one of Pratt’s five teaching approaches. There are a number of different approaches or terms within the broad heading of experiential learning, such as cooperative learning, adventure learning and apprenticeship. I will use the term ‘experiential learning‘ as a broad umbrella term to cover this wide variety of approaches to learning by doing. I will deal with apprenticeship as a separate section (Chapter 3.6) because of its traditional (if tacit) role in preparing university and college instructors, although it can be seen as just one of several methods of experiential learning.
3.5.1. What is experiential learning?
Simon Fraser University (2010) has defined experiential learning as:
the strategic, active engagement of students in opportunities to learn through doing, and reflection on those activities, which empowers them to apply their theoretical knowledge to practical endeavours in a multitude of settings inside and outside of the classroom.
There are many different theorists in this area, such as John Dewey (1938) and more recently David Kolb (1984). There is a wide range of design models that aim to embed learning within real world contexts, including:
• laboratory, workshop or studio work;
• apprenticeship;
• problem-based learning;
• case-based learning;
• project-based learning;
• inquiry-based learning;
• cooperative (work- or community-based) learning.
The focus here is on some of the main ways in which experiential learning can be designed and delivered, with particular respect to the use of technology, and in ways that help develop the knowledge and skills needed in a digital age. (For a more detailed analysis of experiential learning, see Moon, 2004).
3.5.2 Core design principles
Experiential learning focuses on learners reflecting on their experience of doing something, so as to gain conceptual insight as well as practical expertise. Kolb’s experiential learning model suggest four stages in this process:
• active experimentation;
• concrete experience;
• reflective observation;
• abstract conceptualization.
Experiential learning is a major form of teaching at the University of Waterloo. Its web site lists the conditions needed to ensure that experiential learning is effective, as identified by the Association for Experiential Education.
The next section examines different ways in which these principles have been applied.
3.5.3 Experiential design models
There are many different design models for experiential learning, but they also have many features in common.
3.5.3.1 Laboratory, workshop or studio work
Figure 3.5.2 Concordia University wood shop
Today, we take almost for granted that laboratory classes are an essential part of teaching science and engineering. Workshops and studios are considered critical for many forms of trades training or the development of creative arts. Labs, workshops and studios serve a number of important functions or goals, which include:
• to give students hands-on experience in choosing and using common scientific, engineering or trades equipment appropriately;
• to develop motor skills in using scientific, engineering or industrial tools or creative media;
• to give students an understanding of the advantages and limitations of laboratory experiments;
• to enable students to see science, engineering or trade work ‘in action’;
• to enable students to test hypotheses or to see how well concepts, theories, procedures actually work when tested under laboratory conditions;
• to teach students how to design and/or conduct experiments;
• to enable students to design and create objects or equipment in different physical media.
An important pedagogical value of laboratory classes is that they enable students to move from the concrete (observing phenomena) to the abstract (understanding the principles or theories that are derived from the observation of phenomena). Another is that the laboratory introduces students to a critical cultural aspect of science and engineering, that all ideas need to be tested in a rigorous and particular manner for them to be considered ‘true’.
One major criticism of traditional educational labs or workshops is that they are limited in the kinds of equipment and experiences that scientists, engineers and trades people need today. As scientific, engineering and trades equipment becomes more sophisticated and expensive, it becomes increasingly difficult to provide students in schools especially but increasingly now in colleges and universities direct access to such equipment. Furthermore traditional teaching labs or workshops are capital and labour intensive and hence do not scale easily, a critical disadvantage in rapidly expanding educational opportunities.
Because laboratory work is such an accepted part of science teaching, it is worth remembering that teaching science through laboratory work is in historical terms a fairly recent development. In the 1860s neither Oxford nor Cambridge University were willing to teach empirical science. Thomas Huxley therefore developed a program at the Royal School of Mines (a constituent college of what is now Imperial College, of the University of London) to teach school-teachers how to teach science, including how to design laboratories for teaching experimental science to school children, a method that is still the most commonly used today, both in schools and universities.
At the same time, scientific and engineering progress since the nineteenth century has resulted in other forms of scientific testing and validation that take place outside at least the kind of ‘wet labs’ so common in schools and universities. Examples are nuclear accelerators, nanotechnology, quantum mechanics and space exploration. Often the only way to observe or record phenomena in such contexts is remotely or digitally. It is also important to be clear about the objectives of lab, workshop and studio work. There may now be other, more practical, more economic, or more powerful ways of achieving these objectives through the use of new technology, such as remote labs, simulations, and experiential learning. These will be examined in more detail later in this book.
3.5.3.2 Problem-based learning
The earliest form of systematised problem-based learning (PBL) was developed in 1969 by Howard Barrows and colleagues in the School of Medicine at McMaster University in Canada, from where it has spread to many other universities, colleges and schools. This approach is increasingly used in subject domains where the knowledge base is rapidly expanding and where it is impossible for students to master all the knowledge in the domain within a limited period of study. Working in groups, students identify what they already know, what they need to know, and how and where to access new information that may lead to resolution of the problem. The role of the instructor (usually called a tutor in classic PBL) is critical in facilitating and guiding the learning process.
Usually PBL follows a strongly systematised approach to solving problems, although the detailed steps and sequence tend to vary to some extent, depending on the subject domain. The following is a typical example:
Figure 3.5.3 (derived from Gijeselaers, 1995)
Traditionally, the first five steps would be done in a small face-to-face class tutorial of 20-25 students, with the sixth step requiring either individual or small group (four or five students) private study, with a the seventh step being accomplished in a full group meeting with the tutor. However, this approach also lends itself to blended learning in particular, where the research solution (step 6) is done mainly online, although some instructors have managed the whole process online, using a combination of synchronous web conferencing and asynchronous online discussion.
Developing a complete problem-based learning curriculum is challenging, as problems must be carefully chosen, increasing in complexity and difficulty over the course of study, and problems must be chosen so as to cover all the required components of the curriculum. Students often find the problem-based learning approach challenging, particularly in the early stages, where their foundational knowledge base may not be sufficient to solve some of the problems. (The term ‘cognitive overload’ has been used to describe this situation.) Others argue that lectures provide a quicker and more condensed way to cover the same topics. Assessment also has to be carefully designed, especially if a final exam carries heavy weight in grading, to ensure that problem-solving skills as well as content coverage are measured.
However, research (see for instance, Strobel and van Barneveld, 2009) has found that problem-based learning is better for long-term retention of material and developing ‘replicable’ skills, as well as for improving students’ attitudes towards learning. There are now many variations on the ‘pure’ PBL approach, with problems being set after initial content has been covered in more traditional ways, such as lectures or prior reading, for instance.
The methodology of problem-based learning however is one essential tool for developing the knowledge and skills needed in a digital society.
3.5.3.3 Case-based learning
With case-based teaching, students develop skills in analytical thinking and reflective judgment by reading and discussing complex, real-life scenarios.
Case-based learning is sometimes considered a variation of PBL, while others see it as a design model in its own right. As with PBL, case-based learning uses a guided inquiry method, but usually requires the students to have a degree of prior knowledge that can assist in analysing the case. There is usually more flexibility in the approach to case-based learning compared to PBL. Case-based learning is particularly popular in business education, law schools and clinical practice in medicine, but can be used in many other subject domains.
Herreid (2004) provides eleven basic rules for case-based learning.
1. Tells a story.
2. Focuses on an interest-arousing issue.
3. Set in the past five years
4. Creates empathy with the central characters.
5. Includes direct quotations from the characters.
6. Relevant to the reader.
7. Must have pedagogic utility.
8. Conflict provoking.
9. Decision forcing.
10. Has generality.
11. Is short.
Using examples from clinical practice in medicine, Irby (1994) recommends five steps in case-based learning:
• anchor teaching in a (carefully chosen) case;
• actively involve learners in discussing, analysing and making recommendations regarding the case;
• model professional thinking and action as an instructor when discussing the case with learners;
• provide direction and feedback to learners in their discussions;
• create a collaborative learning environment where all views are respected.
Case-based learning can be particularly valuable for dealing with complex, interdisciplinary topics or issues which have no obvious ‘right or wrong’ solutions, or where learners need to evaluate and decide on competing, alternative explanations. Case-based learning can also work well in both blended and fully online environments. Marcus, Taylor and Ellis (2004) used the following design model for a case-based blended learning project in veterinary science:
Figure 3.5.4 Blended learning sequence involving online learning resources, Marcus, Taylor and Ellis, 2004
Other configurations are of course also possible, depending on the requirements of the subject.
3.5.3.4 Project-based learning
Project-based learning is similar to case-based learning, but tends to be longer and broader in scope, and with even more student autonomy/responsibility in the sense of choosing sub-topics, organising their work, and deciding on what methods to use to conduct the project. Projects are usually based around real world problems, which give students a sense of responsibility and ownership in their learning activities.
Once again, there are several best practices or guidelines for successful project work. For instance, Larmer and Mergendoller (2010) argue that every good project should meet two criteria:
• students must perceive the work as personally meaningful, as a task that matters and that they want to do well;
• a meaningful project fulfills an educational purpose.
The main danger with project-based learning is that the project can take on a life of its own, with not only students but the instructor losing focus on the key, essential learning objectives, or important content areas may not get covered. Thus project-based learning needs careful design and monitoring by the instructor.
3.5.3.5 Inquiry-based learning
Inquiry-based learning (IBL) is similar to project-based learning, but the role of the teacher/instructor is somewhat different. In project-based learning, the instructor decides the ‘driving question’ and plays a more active role in guiding the students through the process. In inquiry-based learning, the learner explores a theme and chooses a topic for research, develops a plan of research and comes to conclusions, although an instructor is usually available to provide help and guidance when needed.
Banchi and Bell (2008) suggest that there are different levels of inquiry, and students need to begin at the first level and work through the other levels to get to ‘true’ or ‘open’ inquiry as follows:
Figure 3.5.5 Levels of inquiry-based learning, from Banchi and Bell (2008)
It can be seen that the fourth level of inquiry describes the graduate thesis process, although proponents of inquiry-based learning have advocated its value at all levels of education.
3.5.4 Experiential learning in online learning environments
Some advocates of experiential learning are highly critical of online learning, because, they argue, it is impossible to embed learning in real world examples. However, this is an oversimplification, and there are contexts in which online learning can be used very effectively to support or develop experiential learning, in all its variations:
• blended or flipped learning: although group sessions often start off the process, and/or bring a problem or project to a conclusion, they are usually done in a classroom or lab setting. However students can increasingly conduct the research and information gathering by accessing resources online, by using online multimedia resources to create reports or presentations, and by collaborating online through group project work or through critique and evaluation of each other’s work;
• fully online: increasingly, instructors are finding that experiential learning can be applied fully online, through a combination of synchronous tools such as web conferencing, asynchronous tools such as discussion forums and/or social media for group work, e-portfolios and multimedia for reporting, and remote labs for experimental work.
Indeed, there are circumstances where it is impractical, too dangerous, or too expensive to use real world experiential learning. Online learning can be used to simulate real conditions and to reduce the time to master a skill. Flight simulators have long been used to train commercial pilots, enabling trainee pilots to spend less time mastering fundamentals on real aircraft. Commercial flight simulators are still extremely expensive to build and operate, but in recent years the costs of creating realistic simulations has dropped dramatically.
Figure 3.5.6 Virtual world border crossing, Loyalist College, Ontario
Instructors at Loyalist College have created a ‘virtual’ fully functioning border crossing and a virtual car in Second Life to train Canadian Border Services Agents. Each student takes on the role of an agent, with his/her avatar interviewing the avatars of the travellers wishing to enter Canada. Other students play the travellers. All communication is done by voice communications in Second Life, with the people playing the travellers in a separate room from the students. Each student interviews three or four travellers and the entire class observes the interactions and discusses the situations and the responses. A secondary site for auto searches features a virtual car that can be completely dismantled so students learn all possible places where contraband may be concealed. This learning is then reinforced with a visit to the auto shop at Loyalist College and the search of an actual car. The students in the customs and immigration track are assessed on their interviewing techniques as part of their final grades. Students participating in the first year of the Second Life border simulation achieved a grade standing that was 28 per cent higher than the previous class who did not utilize a virtual world. The next class, using Second Life, scored a further 9 per cent higher. More details can be found here.
Staff in the Emergency Management Division at the Justice Institute of British Columbia have developed a simulation tool called Praxis that helps to bring critical incidents to life by introducing real-world simulations into training and exercise programs. Because participants can access Praxis via the web, it provides the flexibility to deliver immersive, interactive and scenario-based training exercises anytime, anywhere. A typical emergency might be a major fire in a warehouse containing dangerous chemicals. ‘Trainee’ first responders, who will include fire, police and paramedical personnel, as well as city engineers and local government officials, are ‘alerted’ on their mobile phones or tablets, and have to respond in real time to a fast developing scenario, ‘managed’ by a skilled facilitator, following procedures previously taught and also available on their mobile equipment. The whole process is recorded and followed later by a face-to-face debriefing session.
Once again, design models are not in most cases dependent on any particular medium. The pedagogy transfers easily across different delivery methods. Learning by doing is an important method for developing many of the skills needed in a digital age.
3.5.5 Strengths and weaknesses of experiential learning models
How one evaluates experiential learning designs depends partly on one’s epistemological position. Constructivists strongly support experiential learning models, whereas those with a strong objectivist position are usually highly skeptical of the effectiveness of this approach. Nevertheless, problem-based learning in particular has proved to be very popular in many institutions teaching science or medicine, and project-based learning is used across many subject domains and levels of education. There is evidence that experiential learning, when properly designed, is highly engaging for students and leads to better long-term memory. Proponents also claim that it leads to deeper understanding, and develops skills for a digital age such as problem-solving, critical thinking, improved communications skills, and knowledge management. In particular, it enables learners to manage better highly complex situations that cross disciplinary boundaries, and subject domains where the boundaries of knowledge are difficult to manage.
Critics though such as Kirschner, Sweller and Clark (2006) argue that instruction in experiential learning is often ‘unguided’, and pointed to several ‘meta-analyses’ of the effectiveness of problem-based learning that indicated no difference in problem-solving abilities, lower basic science exam scores, longer study hours for PBL students, and that PBL is more costly. They conclude:
In so far as there is any evidence from controlled studies, it almost uniformly supports direct, strong instructional guidance rather than constructivist-based minimal guidance during the instruction of novice to intermediate learners. Even with students with considerable prior knowledge, strong guidance when learning is most often found to be equally effective as unguided approaches.
Certainly, experiential learning approaches require considerable re-structuring of teaching and a great deal of detailed planning if the curriculum is to be fully covered. It usually means extensive re-training of faculty, and careful orientation and preparation of students. I would also agree with Kirschner et al. that just giving students tasks to do in real world situations without guidance and support is likely to be ineffective.
However, many forms of experiential learning can and do have strong guidance from instructors, and one has to be very careful when comparing matched groups that the tests of knowledge include measurement of the skills that are claimed to be developed by experiential learning, and are not just based on the same assessments as for traditional methods, which often have a heavy bias towards memorisation and comprehension.
On balance then, I would support the use of experiential learning for developing the knowledge and skills needed in a digital age, but as always, it needs to be done well, following best practices associated with the different design models.
References
Banchi, H., and Bell, R. (2008) ‘The Many Levels of Inquiry’ Science and Children, Vol. 46, No. 2
Dewey, J. (1938). Experience & Education. New York, NY: Macmillan
Gijselaers, W., (1995) ‘Perspectives on problem-based learning’ in Gijselaers, W, Tempelaar, D, Keizer, P, Blommaert, J, Bernard, E & Kapser, H (eds) Educational Innovation in Economics and Business Administration: The Case of Problem-Based Learning. Dordrecht, Kluwer.
Herreid, C. F. (2007). Start with a story: The case study method of teaching college science. Arlington VA: NSTA Press.
Irby, D. (1994) Three exemplary models of case-based teaching Academic Medicine, Vol. 69, No. 12
Kirshner, P., Sweller, J. amd Clark, R. (2006) Why Minimal Guidance During Instruction Does Not Work: An Analysis of the Failure of Constructivist, Discovery, Problem-Based, Experiential, and Inquiry-Based Teaching Educational Psychologist, Vo. 41, No.2
Kolb. D. (1984) Experiential Learning: Experience as the source of learning and development Englewood Cliffs NJ: Prentice Hall
Larmer, J. and Mergendoller, J. (2010) Seven essentials for project-based learning Educational Leadership, Vol. 68, No. 1
Marcus, G. Taylor, R. and Ellis, R. (2004) Implications for the design of online case-based learning activities based on the student blended learning experience: Perth, Australia: Proceedings of the ASCILITE conference, 2004
Moon, J.A. (2004) A Handbook of Reflective and Experiential Learning: Theory and Practice New York: Routledge
Simon Fraser University (2010) Task Force on Teaching and Learning: Recommendations Report Burnaby BC: Simon Fraser University
Strobel, J. , & van Barneveld, A. (2009). When is PBL More Effective? A Meta-synthesis of Meta-analyses Comparing PBL to Conventional Classrooms. Interdisciplinary Journal of Problem-based Learning, Vol. 3, No. 1
Activity 3.5 Assessing experiential design models
1. If you have experiences with experiential learning, what worked well and what didn’t?
2. Are the differences between problem-based learning, case-based learning, project-based learning and inquiry-based learning significant, or are they really just minor variations on the same design model?
3. Do you have a preference for any one of the models? If so, why?
4. Do you agree that experiential learning can be done just as well online as in classrooms or in the field? If not, what is the ‘uniqueness’ of doing it face-to-face that cannot be replicated online? Can you give an example?
5. Kirschner, Sweller and Clark’s paper is a powerful condemnation of PBL. Read it in full, then decide whether or not you share their conclusion, and if not, why not.
Click on the podcast below for my feedback on these questions.
An audio element has been excluded from this version of the text. You can listen to it online here: https://pressbooks.bccampus.ca/teachinginadigitalagev2/?p=103 | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/03%3A_Methods_of_teaching%3A_campus-focused/03.5%3A_Learning_by_doing%3A_Experiential_learning.txt |
Figure 3.6.1 BMW Group UK Apprentice Recruitment, 2013
Image: © Motoring Insight, 2013
3.6.1 The importance of apprenticeship as a teaching method
Apprenticeship is one of the most common and well established forms of experiential learning. Bloom and his colleagues designated psycho-motor skills as the third domain of learning back in 1956. Learning by doing is particularly common in teaching motor skills, such as learning to ride a bicycle or play a sport, but examples can also be found in higher education, such as teaching practice, medical internships, and laboratory studies.
Apprenticeship is a particular way of enabling students to learn by doing. It is often associated with vocational training where a more experienced tradesman or journeyman models behaviour, the apprentice attempts to follow the the model, and the journeyman provides feedback. However, apprenticeship is the most common method used to train post-secondary education instructors in teaching (at least implicitly), so there is a wide range of applications for an apprenticeship approach to teaching.
Because a form of apprenticeship is the often implicit, default model also for university teaching, and in particular for pre-service training of university instructors, apprenticeship will be discussed separately from other forms of experiential learning, although it is really just one, very commonly used, version.
3.6.2 Key features of apprenticeship
Figure 3.6.2 An apprentice being supervised
Image: © BBC, 2014
It is useful to remember that apprenticeship is not an invisible phenomenon. It has key elements: a particular way of viewing learning, specific roles and strategies for teachers and learners, and clear stages of development, whether for traditional or cognitive apprenticeship. But mostly it’s important to remember that in this perspective, one cannot learn from afar. Instead, one learns amid the engagement of participating in the authentic, dynamic and unique swirl of genuine practice.
Pratt and Johnson, 1998
Schön (1983) argues that apprenticeship operates in ‘situations of practice that…are frequently ill-defined and problematic, and characterized by vagueness, uncertainty and disorder‘. Learning in apprenticeship is not just about learning to do (active learning), but also requires an understanding of the contexts in which the learning will be applied. In addition there is a social and cultural element to the learning, understanding and embedding the accepted practices, customs and values of experts in the field. Pratt and Johnson (1998) identify the characteristics of a master practitioner, whom they define as:
a person who has acquired a thorough knowledge of and/or is especially skilled in a particular area of practice. Master practitioners:
1. possess great amounts of knowledge in their area of expertise, and are able to apply that knowledge in difficult practice settings;
2. have well-organized, readily accessible schemas (cognitive maps) which facilitate the acquisition of new information;
3. have well-developed repertoires of strategies for acquiring new knowledge, integrating and organizing their schemas, and applying their knowledge and skills in a variety of contexts….;
4. …are motivated to learn as part of the process of developing their identities in their communities of practice. They are not motivated to learn simply to reach some external performance goal or reward;
5. frequently display tacit knowledge in the form of:
• spontaneous action and judgements;
• being unaware of having learned to do these things;
• being unable or having difficulty in describing the knowing which their actions reveal.
Pratt and Johnson further distinguish two different but related forms of apprenticeship: traditional and cognitive. A traditional apprenticeship experience, based on developing a motor or manual skill, involves learning a procedure and gradually developing mastery, during which the master and learner go through several stages.
3.6.3 University apprenticeship
An intellectual or cognitive apprenticeship model is somewhat different because this form of learning is less easily observable than learning motor or manual skills. Pratt and Johnson argue that in this context, master and learner must say what they are thinking during applications of knowledge and skills, and must make explicit the context in which the knowledge is being developed, because context is so critical to the way knowledge is developed and applied. Pratt and Johnson suggest five stages for cognitive and intellectual modelling (p. 99):
1. modelling by the master and development of a mental model/schema by the learner;
2. learner approximates replication of the model with master providing support and feedback (scaffolding/coaching);
3. learner widens the range of application of the model, with less support from master;
4. self-directed learning within the specified limits acceptable to the profession;
5. generalizing: learner and master discuss how well the model might work or would have to be adapted in a range of other possible contexts.
Pratt and Johnson provide a concrete example of how this apprenticeship model might work for a novice university professor (pp. 100-101). They argue that for cognitive apprenticeship it is important to create a forum or set of opportunities for:
articulate discussion and authentic participation in the realities of practice from within the practice, not from just one single point of view. Only from such active involvement, and layered and cumulative experience does the novice move towards mastery.
The main challenge of the apprenticeship model in a university setting is that it is not usually applied in a systematic matter. The hope that young or new university teachers will have automatically learned how to teach just by observing their own professors teach leaves far too much to chance.
[Removed from Version 1: 3.5.4 Apprenticeship in online environments]
3.6.4 Strengths and weaknesses
The main advantages of an apprenticeship model of teaching can be summarised as follows:
• teaching and learning are deeply embedded within complex and highly variable contexts, allowing rapid adaptation to real-world conditions;
• it makes efficient use of the time of experts, who can integrate teaching within their regular work routine;
• it provides learners with clear models or goals to aspire to;
• it acculturates learners to the values and norms of the trade or profession.
On the other hand, there are some serious limitations with an apprenticeship approach, particularly in preparing for university teaching:
• much of a master’s knowledge is tacit, partly because their expertise is built slowly through a very wide range of activities;
• experts often have difficulty in expressing consciously or verbally the schema and ‘deep’ knowledge that they have built up and taken almost for granted, leaving the learner often to have to guess or approximate what is required of them to become experts themselves;
• experts often rely solely on modelling with the hope that learners will pick up the knowledge and skills from just watching the expert in action, and don’t follow through on the other stages that make an apprenticeship model more likely to succeed;
• there is clearly a limited number of learners that one expert can manage, given that the experts themselves are fully engaged in applying their expertise in often demanding work conditions which may leave little time for paying attention to the needs of novice learners in the trade or profession;
• traditional vocational apprenticeship programs have a very high attrition rate: for instance, in British Columbia, more than 60 per cent of those that enter a formal campus-based vocational apprenticeship program withdraw before successful completion of the program. As a result, there are large numbers of experienced tradespeople in the workforce without full accreditation, limiting their career development and slowing down economic development where there are shortages of fully qualified skilled workers;
• in trades or occupations undergoing rapid change in the workplace, the apprenticeship model can slow adaptation or change in working methods, because of the prevalence of traditional values and norms being passed down by the ‘master’ that may no longer be as relevant in the new conditions facing workers. This limitation of the apprenticeship model can be clearly seen in the post-secondary education sector, where traditional values and norms around teaching are increasingly in conflict with external forces such as new technology and the massification of higher education.
Nevertheless, the apprenticeship model, when applied thoroughly and systematically, is a very useful model for teaching in highly complex, real-world contexts.
References
Bloom, B.S. (Ed.). Engelhart, M.D., Furst, E.J., Hill, W.H., Krathwohl, D.R. (1956). Taxonomy of Educational Objectives, Handbook I: The Cognitive Domain. New York: David McKay Co Inc.
Pratt, D. and Johnson, J. (1998) ‘The Apprenticeship Perspective: Modelling Ways of Being’ in Pratt, D. (ed.) Five Perspectives on Teaching in Adult and Higher Education Malabar FL: Krieger Publishing Company
Schön, D. (1983) The Reflective Practitioner: How Professionals Think in Action New York: Basic Books
Activity 3.6 Applying apprenticeship to university teaching
1. Do you agree that learning to teach in a university depends heavily on an apprenticeship model? In what ways does it resemble apprenticeship and in what ways does it differ? In what ways could it be improved?
2. What are the key features required for an apprenticeship model to work?
Click on the podcast below for my response to this activity
An audio element has been excluded from this version of the text. You can listen to it online here: https://pressbooks.bccampus.ca/teachinginadigitalagev2/?p=96 | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/03%3A_Methods_of_teaching%3A_campus-focused/03.6%3A_Learning_by_doing%3A_Apprenticeship.txt |
Figure 3.7.1 Image: Michigan State University, 2019
In this section I will briefly discuss the last two of Pratt’s five teaching perspectives, nurturing and social reform.
3.7.1 The nurturing perspective
A nurturing perspective on teaching can best be understood in terms of the role of a parent. Pratt (1998) states:
We expect ‘successful’ parents to understand and empathize with their child; and that they will provide kind, compassionate, and loving guidance through content areas of utmost difficulty….The nurturing educator works with other issues…in different contexts and different age groups, but the underlying attributes and concerns remain the same. Learners’ efficacy and self-esteem issues become the ultimate criteria against which learning success is measured, rather than performance-related mastery of a content body.
There is a strong emphasis on the teacher focusing on the interests of the learner, on empathizing with how the learner approaches learning, of listening carefully to what the learner is saying and thinking when learning, and providing appropriate, supportive responses in the form of ‘consensual validation of experience‘. This perspective is driven partly by the observation that people learn autonomously from a very early age, so the trick is to create an environment for the learner that encourages rather than inhibits their ‘natural’ tendency to learn, and directs it into appropriate learning tasks, decided by an analysis of the learner’s needs. This is further elaborated in Chapter 6, on Building an Effective Learning Environment.
3.7.2 The social reform perspective
Pratt (1998, p. 173) states:
Teachers holding a social reform perspective are most interested in creating a better society and view their teaching as contributing to that end. Their perspective is unique in that it is based upon an explicitly stated ideal or set of principles linked to a vision of a better social order. Social reformers do not teach in one single way, nor do they hold distinctive views about knowledge in general…these factors all depend on the particular ideal that inspires their actions.
This then in some ways is less a theory of teaching as an epistemological position, that society needs change, and the social reformer knows how to bring about this change through teaching and education. Indeed, as Figure 3.7.2 below illustrates, the social reform model of learning can be driven as much by the passions and concerns of learners as by those of their instructors.
Figure 3.7.2
3.7.3 Past and future: the relevance of the nurturing and social reform methods for connectivism
These two perspectives on teaching again have a long history, with echoes of:
• Jean-Jacques Rousseau (1762): ‘education should be carried out, so far as possible, in harmony with the development of the child’s natural capacities by a process of apparently autonomous discovery‘ (Stanford Encyclopedia of Philosophy)
• Malcolm Knowles (1984): ‘As a person matures his self concept moves from one of being a dependent personality toward one of being a self-directed human being.’
• Paulo Freire (2004): ‘education makes sense because women and men learn that through learning they can make and remake themselves, because women and men are able to take responsibility for themselves as beings capable of knowing—of knowing that they know and knowing that they don’t.’
• Ivan Illich (1971) (in his criticism of the institutionalization of education): ‘The current search for new educational funnels must be reversed into the search for their institutional inverse: educational webs which heighten the opportunity for each one to transform each moment of his living into one of learning, sharing, and caring.’
The reason why the nurturing and social reform perspectives on teaching are important is because they reflect many of the assumptions or beliefs around connectivism (Chapter 2.6). Indeed, as early as 1971, Illich made this remarkable statement for the use of advanced technology to support “learning webs”:
The operation of a peer-matching network would be simple. The user would identify himself by name and address and describe the activity for which he sought a peer. A computer would send him back the names and addresses of all those who had inserted the same description. It is amazing that such a simple utility has never been used on a broad scale for publicly valued activity.
Well, those conditions certainly exist today. Learners do not necessarily need to go through institutional gateways to access information or knowledge, which is increasing available and accessible through the Internet. As we shall see in Chapter 5, MOOCs help to identify those common interests and connectivist MOOCs in particular aim to provide the networks of common interests and the environment for self-directed learning. The digital age provides the technology infrastructure and support needed for this kind of learning.
3.7.4 The roles of learners and teachers
Of all the perspectives on teaching these two are the most learner-centred. They are based on a profoundly optimistic view of human nature, that people will seek out and learn what they need, and will find the necessary support from caring, dedicated educators and/or from others with similar interests and concerns, and that individuals have the capacity and ability to identify and follow through with their own educational needs. It is also a more radical view of education, because it seeks to escape the political and controlling aspects of state or private institutions.
Within each of these two perspectives, there are differences of view about the centrality of teachers for successful learning. For Pratt, the teacher plays a central role in nurturing learning; for others such as Illich or Freire, professionally trained teachers are more likely to be the servant of the state than of the individual learner. For those supporting these perspectives on teaching, volunteer mentors or social groups organised around certain ideals or social goals provide the necessary support for learners.
3.7.5 Strengths and weaknesses of these two approaches
There are, as always, a number of drawbacks to these two perspectives on teaching:
• The teacher in a nurturing approach needs to adopt a highly dedicated and unselfish approach, putting the demands and needs of the learner first. This often means for teachers who are experts in their subject holding back the transmission and sharing of their knowledge until the learner is ‘ready’, thus denying to many subject experts their own identity and needs to a large extent;
• Pratt argues that ‘although content is apparently neglected, children taught by nurturing educators do continue to master it at much the same rate as children taught by curriculum-driven teaching methodologies‘, but no empirical evidence is offered to support this statement, although it does derive in Pratt’s case from strong personal experience of teaching in this way;
• like all the other teaching approaches the nurturing perspective is driven by a very strong belief system, which will not necessarily be shared by other educators (or parents – or even learners, for that matter);
• a nurturing perspective necessitates probably the most labour-intensive of all the teaching models other than apprenticeship, requiring a deep understanding on the part of the teacher of each learner and that learner’s needs; every individual learner is different and needs to be treated differently, and teachers need to spend a great deal of time identifying learners’ needs, their readiness to learn, and building or creating supportive environments or contexts for that learning;
• there may well be a conflict between what the learner identifies as their personal learning needs, and the demands of society in a digital age. Dedicated teachers may be able to help a learner negotiate that divide, but in situations where learners are left without professional guidance, learners may end up just talking to other individuals with similar views that do not progress their learning (remembering that academic teaching is a rhetorical exercise, challenging learners to view the world differently);
• social reform depends to a large extent on learners and teachers embracing similar belief systems, and can easily descend into dogmatism without challenges from outside the ‘in-community’ established by self-referential groups.
Nevertheless, there are aspects of both perspectives that have significance for a digital age:
• both nurturing and social reform perspectives seems to work well for many adults in particular, and the nurturing approach also works well for younger children;
• nurturing is an approach that has been adopted as much in advanced corporate training in companies such as Google as in informal adult education (see for instance, Tan, 2012);
• we shall see in Chapter 5 that connectivist MOOCs strongly reflect both the nurturing approach and the ability to create webs of connections that enable the development of self-efficacy and attempts at social reform;
• both perspectives seem to be effective when learners are already fairly well educated and already have good prior knowledge and conceptual development;
• perspectives that focus on the needs of individuals rather than institutions or state bureaucracies can liberate thinking and learning and thus make the difference between ‘good’ and ‘excellent’ in creative thinking, problem-solving, and application of knowledge in complex and variable contexts.
References
Freire, P. (2004). Pedagogy of Indignation . Boulder CO: Paradigm
Illich, I. (1971) Deschooling Society, New York: Harper and Row
Knowles, M. (1984) Andragogy in Action. Applying modern principles of adult education, San Francisco: Jossey Bass
Pratt, D. and Associates (1998) Five Perspectives on Teaching in Adult and Higher Education Malabar FL: Krieger Publishing Company
Rousseau, J.-J. (1762) Émile, ou de l’Éducation (Trans. Allan Bloom). New York: Basic Books, 1979
Tan, C.-M. (2012) Search Inside Yourself New York: Harper Collins
Activity 3.7 Nurturing, social reform and connectivism
1. Do you have experience of teaching in one or both of these ways? If so, do you agree with the analysis of the strengths and weaknesses of each component?
2. Do you think that connectivism is a modern reflection of either of these models of teaching – or is connectivism a distinct and unique method of teaching in itself? If so, what distinguishes it as a teaching method from all the other methods I have covered?
There is no immediate feedback for these questions, although the issues will be raised again in Chapter 5. | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/03%3A_Methods_of_teaching%3A_campus-focused/03.7%3A_Learning_by_being%3A_The_nurturing_and_social_reform_models_of_teaching%3A.txt |
Figure 3.8 A workshop on blended learning, where instructors apply principles from a lecture on blended learning to designing a unit of teaching (a mix of transmissive and experiential learning methods reflecting a constructivist epistemology). Image: Tony Bates, 2017
3.8.1 Relating epistemology, learning theories and teaching methods
3.8.1.1 Pragmatism trumps ideology in teaching
Although there is often a direct relationship between a method of teaching, a learning theory and an epistemological position, this is by no means always the case. It is tempting to try to put together a table and neatly fit each teaching method into a particular learning theory, and each theory into a particular epistemology, but unfortunately education is not as tidy as computer science, so it would be misleading to try to do a direct ontological classification. For instance a transmissive lecture might be structured so as to further a cognitivist rather than a behaviourist approach to learning, or a lecture session may combine several elements, such as transmission of information, learning by doing, and discussion.
Purists may argue that it is logically inconsistent for a teacher to use methods that cross epistemological boundaries (and it may certainly be confusing for students) but teaching is essentially a pragmatic profession and teachers will do what it takes to get the job done. If students need to learn facts, principles, standard procedures or ways of doing things, before they can start an informed discussion about their meaning, or before they can start solving problems, then a teacher may well consider behaviourist methods to lay this foundation before moving to more constructivist approaches later in a course or program.
3.8.1.2 Teaching methods are not determined by technology
Secondly technology applications such as MOOCs or video recorded lectures may replicate exactly a particular teaching method or approach to learning used in the classroom. In many ways methods of teaching, theories of learning and epistemologies are independent of a particular technology or medium of delivery, although we shall see in Chapters 7, 8, 9 and 10 that technologies can be used to transform teaching, and a particular technology will in some cases further one method of teaching more easily than other methods, depending on the characteristics or ‘affordances’ of that technology.
Thus, teachers who are aware of not only a wide array of teaching methods, but also of learning theories and their epistemological foundation will be in a far better position to make appropriate decisions about how to teach in a particular context. Also, as we shall see, having this kind of understanding will also facilitate an appropriate choice of technology for a particular learning task or context.
3.8.2 Relating teaching methods to the knowledge and skills needed in a digital age
The main purpose of this chapter has been to enable you as a teacher to identify the classroom teaching methods that are most likely to support the development of the knowledge and skills that students or learners will need in a digital age. We still have a way to go before we have all the information and tools needed to make this decision, but we can at least have a stab at it from here, while recognising that such decisions will depend on a wide variety of factors, such as the nature of the learners and their prior knowledge and experience, the demands of particular subject areas, the institutional context in which teachers and learners find themselves, and the likely employment context for learners.
First, we can identify a number of different types of skills needed:
• conceptual skills, such as knowledge management, critical thinking, analysis, synthesis, problem-solving, creativity/innovation, experimental design;
• developmental or personal skills, such as independent learning, communications skills, ethics, networking, responsibility and teamwork;
• digital skills, embedded within and related to a particular subject or professional domain;
• manual and practical skills, such as machine or equipment operation, safety procedures, observation and recognition of data, patterns, and spatial factors.
We can also identify that in terms of content, we need teaching methods that enable students to manage information or knowledge, rather than methods that merely transmit information to students.
There are several key points for a teacher or instructor to note:
• the teacher needs to be able to identify/recognise the skills they are hoping to develop in their students;
• these skills are often not easily separated but tend to be contextually based and often integrated;
• teachers need to identify appropriate methods and contexts that will enable students to develop these skills;
• students will need practice to develop such skills;
• students will need feedback and intervention from the teacher and other students to ensure a high level of competence or mastery in the skill;
• an assessment strategy needs to be developed that recognises and rewards students’ competency and mastery of such skills.
In a digital age, just choosing a particular teaching method such as seminars or apprenticeship is not going to be sufficient. It is unlikely that one method, such as transmissive lectures, or seminars, will provide a rich enough learning environment for a full range of skills to be developed within the subject area. It is necessary to provide a rich learning environment for students to develop such skills that includes contextual relevance, and opportunities for practice, discussion and feedback. As a result, we are likely to combine different methods of teaching.
Secondly, this chapter has focused mainly on classroom or campus-based approaches to teaching. In the next chapter a range of teaching methods that incorporate online/digital technologies will be examined. So it would be foolish at this stage to say that any single method, such as seminars, or apprenticeship, or nurturing, is the best method for developing the knowledge and skills needed in a digital age. At the same time, the limitations of transmissive lectures, especially if they are used as the main method for teaching, are becoming more apparent.
Activity 3.8 ‘Labelling’ your own teaching
1. Think of what you consider in the past to have been your most successful unit of teaching (a class or a whole course). Can you identify the underlying epistemology? What theory or theories of learning would best describe how students learned in that context? What was the main teaching method(s) you used?
2. Look at one of the courses you are likely to be teaching next year. How would you change your teaching methods on that course, now you have read Chapters 1, 2 and 3?
There is no direct feedback from me on this activity as it is a reflective exercise.
Key Takeaways
This list of classroom or campus-based teaching methods is not meant to be exhaustive or comprehensive. The aim is to show that there many different ways to teach, and all are in some ways legitimate in certain circumstances. Most instructors will mix and match different methods, depending on the needs of both the subject matter and the needs of their students at a particular time. There are though some core conclusions to be drawn from this comparative review of different approaches to teaching.
1. No single method is likely to meet all the requirements teachers face in a digital age.
2. Nevertheless, some forms of teaching fit better with the development of the skills needed in a digital age. In particular, methods that focus on conceptual development, such as dialogue and discussion, knowledge management (rather than information transmission), and experiential learning in real-world contexts, are all methods more likely to develop the high level conceptual skills required in a digital age.
3. It is not just conceptual skills though that are needed. It is the combination of conceptual, practical, personal and social skills in highly complex situations that are needed. This again means combining a variety of teaching methods.
4. Nearly all of these teaching methods are media or technology independent. In other words, they can be used in classrooms or online. What matters from a learning perspective is not so much the choice of technology as the efficacy and expertise in appropriately choosing and using the teaching method.
5. Nevertheless, we shall see in the next chapter that new technologies offer new possibilities for teaching, including offering more practice or time on task, reaching out to new target groups, and increasing the productivity of both teachers and the system as a whole. | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/03%3A_Methods_of_teaching%3A_campus-focused/03.8%3A_Main_conclusions.txt |
For my personal comments on some of the issues raised in this chapter, please click on the podcast below, which discusses the relationship between quality, modes of delivery, teaching methods and design.
An audio element has been excluded from this version of the text. You can listen to it online here: https://pressbooks.bccampus.ca/teachinginadigitalagev2/?p=114
Online learning is increasingly influencing both classroom/campus-based teaching but more importantly it is leading to new models or designs for teaching and learning.
When commercial movies were first produced, they were basically a transfer of previous music hall and vaudeville acts to the movie screen. Then along came D.W. Griffith’s ‘Birth of a Nation’, which transformed the design of movies, by introducing techniques that were unique to cinema at the time, such as panoramic long shots, panning shots, realistic battle scenes, and what are now known as special effects.
A similar development has taken place with online learning. Initially, there were two separate influences: designs from classroom teaching; and designs inherited from print-based or multimedia distance education. Over time, though, new designs that fully exploit the unique characteristics of online learning are beginning to emerge.
What we do when we move teaching online is to change the learning environment. Thus, I am beginning to move from talking about teaching methods (which can be the same both in class and online) to design models, where the teaching method is deliberately adapted to the learning environment.
04.1: Scenario D: Developing historical thinking
Figure 4 D An artifact used by students in their history of Beijing, 1964-2014
Image: © zonaeuropa.com
Ralph Goodyear is a professor of history in a public research university in the central United States. He has a class of 72 undergraduate students taking HIST 305, ‘Historiography’. For the first three weeks of the course, Goodyear had recorded a series of short 15 minute video lectures that covered the following topics/content:
• the various sources used by historians (e.g. earlier writings, empirical records including registries of birth, marriage and death, eye witness accounts, artifacts such as paintings, photographs, and physical evidence such as ruins);
• the themes around which historical analysis tend to be written;
• some of the techniques used by historians, such as narrative, analysis and interpretation;
• three different positions or theories about history (objectivist, marxist, post modernist).
Students downloaded the videos according to a schedule suggested by Goodyear. Students attended two one hour classes a week, where specific topics covered in the videos were discussed. Students also had an online discussion forum in the course space on the university’s learning management system, where Goodyear had posted similar topics for discussion. Students were expected to make at least one substantive contribution to each online topic for which they received a mark that went towards their final grade. Students also had to read a major textbook on historiography over this three week period.
In the fourth week, he divided the class into twelve groups of six, and asked each group to research the history of any city outside the United States over the last 50 years or so. They could use whatever sources they could find, including online sources such as newspaper reports, images, research publications, and so on, as well as the university’s own library collection. In writing their report, they had to do the following:
• pick a particular theme that covered the 50 years and write a narrative based around the theme;
• identify the sources they finally used in their report, and discuss why they selected some sources and dismissed others;
• compare their approach to the three positions covered in the lectures;
• post their report in the form of an online e-portfolio in the course space on the university’s learning management system.
They had five weeks to do this.
The last three weeks of the course were devoted to presentations by each of the groups, with comments, discussion and questions, both in class and online (the in class presentations were recorded and made available online). At the end of the course, students assigned grades to each of the other groups’ work. Goodyear took these student gradings into consideration, but reserved the right to adjust the grades, with an explanation of why he did the adjustment. Goodyear also gave each student an individual grade, based on both their group’s grade, and their personal contribution to the online and class discussions.
Goodyear commented that he was surprised and delighted at the quality of the students’ work. He said: ‘What I liked was that the students weren’t learning about history; they were doing it.’
Based on an actual case, but with some embellishments | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/04%3A_Methods_of_teaching_with_an_online_focus/04.1%3A_Online_learning_and_teaching_methods.txt |
Figure 4.2.1 Live video streaming of lecture Image: Planet eStream, 2019
We start with classroom teaching methods that have been moved into a technological format with little change to the overall design principles. I will argue that these are essentially old designs in new bottles.
4.2.1 Live, streamed video
This is basically a classroom lecture delivered at the time of delivery to remote students (although there may also be live students in the lecture theatre as well). The remote students may be watching on their own at home, work or in transit, or (more often) in small groups at another campus or local learning centre. There is no change in the design, although the instructor may need to make sure that the remote students are not ignored if there are questions or discussion. For an example, see here.
This is often the first step instructors take into online learning, because they do not have to do anything new other than learn how to set up and switch on the equipment. As the technology became cheaper and easier to use, the use of live streamed lectures doubled between 2016 and 2017 in Canada (Bates et al., 2018).
Some instructors require all students to be present during the live lecture in order to ensure discussion, but this can be counter-productive if the aim of going online is to increase flexibility for students. This can be countered by using an online asynchronous discussion forum in a learning management system (for more on this, see Chapter 4.4). In most cases, though, lecturers prefer also to record the live transmission so all students can access the lecture at any time (see the next section below).
4.2.2 Classes using lecture capture
This technology, which automatically records a classroom lecture, was originally designed to enhance the classroom model by making lectures available for repeat viewings online at any time for students regularly attending classes – in other words, a form of homework or revision.
Figure 4.2.2 An MIT classroom lecture recorded and made available through MIT’s OpenCourseWare. Click on image to see the lecture.
Flipped classrooms, which pre-record a lecture for students to watch on their own, followed by discussion in class, are an attempt to exploit more fully this potential. The main advantage of lecture capture is increased access, especially if students have long commutes or harsh weather to navigate. In some cases, it can reduce student drop-out dramatically. For an example of this see here.
One of the biggest impacts of lecture capture has been for ‘instructionist’ massive open online courses (xMOOCs), such as those offered by Coursera, Udacity and edX. However, even this type of MOOC is really a basic classroom design model (MOOCs are discussed in more detail in Chapter 5). The main difference with a MOOC is that in a MOOC the classroom is open to anyone – but then in principle so are many university lectures – but MOOCs are available to unlimited numbers at a distance. Thus, if an institution decided to put all its recorded lectures up on an openly accessible server or on YouTube, they would become MOOCs. Nevertheless, whether lecture captures are available only to students registered in a course or as a MOOC, the design of the teaching has not changed markedly, although increasingly lectures are recorded in smaller chunks, partly as a result of research on MOOCs (for more on this research see Chapter 8.4).
4.2.2 Courses using learning management systems
Learning management systems (LMSs) are software that enable instructors and students to log in and work within a password protected online learning environment. Most learning management systems, such as Blackboard, Desire2Learn and Moodle, are in fact used to replicate a classroom design model. They have weekly units or modules, the instructor selects and presents the material to all students in the class at the same time, a large class enrolment can be organized into smaller sections with their own instructors, there are opportunities for (online) discussion, students work through the materials at roughly the same pace, and assessment is by end-of-course tests or essays.
Figure 4.2.3 A screenshot of the University of British Columbia’s LMS, Blackboard Connect
The main design differences are that the content is primarily text based rather than oral (although increasingly video and audio are now integrated into LMSs), the online discussion is mainly asynchronous rather than synchronous, and the course content is available at any time from anywhere with an Internet connection. These are important differences from a physical classroom, and skilled teachers and instructors can modify or adapt LMSs to meet different teaching or learning requirements (as they can in physical classrooms), but the basic organizing framework of the LMS remains the same as for a physical classroom.
Nevertheless, the LMS is still an advance over online designs that merely put lectures on the Internet as pre-recorded videos, or load up pdf copies of Powerpoint lecture notes, as is still the case unfortunately in many online programs. There is also enough flexibility in the design of learning management systems for them to be used in ways that break away from the traditional classroom model, which is important, as good online design should take account of the special requirements of online learners, so the design needs to be different from that of a classroom model.
4.2.3 The limitations of the classroom design model for online learning
Old wine can still be good wine, whether the bottle is new or not. What matters is whether classroom design meets the changing needs of a digital age. However, just adding technology to the mix, or delivering the same design online, does not automatically result in meeting changing needs.
It is important then to look at the design that makes the most of the educational affordances of new technologies, because unless the design changes significantly to take full advantage of the potential of the technology, the outcome is likely to be inferior to that of the physical classroom model which it is attempting to imitate. Thus even if the new technology, such as lecture capture and computer-based multiple-choice questions organised in a MOOC, result in helping more students memorise better or learn more content, for example, this may not be sufficient to meet the higher level skills needed in a digital age.
The second danger of just adding new technology to the classroom design is that we may just be increasing cost, both in terms of technology and the time of instructors, without changing outcomes.
The most important reason though is that students studying online are in a different learning environment or context than students learning in a classroom, and the design needs to take account of this. This will be discussed more fully in the rest of the book.
Education is no exception to the phenomenon of new technologies being used at first merely to reproduce earlier design models before they find their unique potential. However, changes to the basic design model are needed if the demands of a digital age and the full potential of new technology are to be exploited in education.
References
Bates, A. et al. (2018) Tracking Online and Distance Education in Canadian Universities and Colleges: 2018 Halifax NS: Canadian Digital Learning Research Association
Activity 4.2 Moving the classroom model online
1. Do you agree that the classroom design model is a product of the 19th century and needs to changed for teaching in a digital age? Or is there still enough flexibility in the classroom model for our times?
2. Do you agree that courses using LMSs are basically a classroom model delivered online, or are they a unique design model in themselves. If so, what makes them unique?
3. What are the advantages and disadvantages of breaking up a 50 minute lecture into say five 10 minute chunks for recording? Would you call this a significant design change – if so, what makes it significant?
For my personal views on these three questions listen to the podcast below:
An audio element has been excluded from this version of the text. You can listen to it online here: https://pressbooks.bccampus.ca/teachinginadigitalagev2/?p=118 | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/04%3A_Methods_of_teaching_with_an_online_focus/04.2%3A_Old_wine_in_new_bottles%3A_classroom-type_online_learning.txt |
Figure 4.3.1 The ADDIE model.
© Flexible Learning Australia, 2014
There have been many books written about the ADDIE model (see for instance, Morrison, 2010; Dick and Carey, 2004). I give here just a very brief introduction.
4.3.1 What is ADDIE?
ADDIE stands for:
Analyse
• identify all the variables that need to be considered when designing the course, such as learner characteristics, learners’ prior knowledge, resources available, etc. This stage is similar to the describing the learning environment outlined in Appendix 1 of this book;
Design
• this stage focuses on identifying the learning objectives for the course and how materials will be created and designed (for instance, it may include describing what content areas are to be covered and a storyboard outlining what will be covered in text, audio and video and in what order), and deciding on the selection and use of technology, such as an LMS, video or social media;
Develop
• the creation of content, including whether to develop in-house or outsource, copyright clearance for third party materials, loading of content into a web site or LMS, and so on;
Implement
• this is the actual delivery of the course, including any prior training or briefing of learner support staff, and student assessment;
Evaluate
• feedback and data is collected in order to identify areas that require improvement and this feeds into the design, development and implementation of the next iteration of the course.
4.3.2 Where is ADDIE used?
This is a design model used by many professional instructional designers for technology-based teaching. ADDIE has been almost a standard for professionally developed, high quality distance education programs, whether print-based or online. It is also heavily used in corporate e-learning and training. There are many variations on this model (my favourite is ‘PADDIE’, where planning and/or preparation are added at the start). The model is mainly applied on an iterative basis, with evaluation leading to re-analysis and further design and development modifications. One reason for the widespread use of the ADDIE model is that it is extremely valuable for large and complex teaching designs. ADDIE’s roots go back to the Second World War and derive from system design, which was developed to manage the hugely complex Normandy landings.
Many open universities, such as the U.K. Open University and the OU of the Netherlands, Athabasca University and Thompson Rivers Open University in Canada, still make heavy use of ADDIE to manage the design of complex multi-media distance education courses. When the U.K. OU opened in 1971 with an initial intake of 20,000, it used radio, television, specially designed printed modules, text books, reproduced research articles in the form of selected readings that were mailed to students, and regional study groups, with teams of often 20 academics, media producers and technology support staff developing courses, and with delivery and learner support provided by an army of regional tutors and senior counsellors. Creating and delivering its first courses within two years of receiving its charter would have been impossible without a systematic instructional design model, and in 2014, with over 200,000 students, the OU was still using the ADDIE approach for many of its courses.
Although ADDIE and instructional design in general originated in the USA, the U.K. Open University’s success in developing high quality learning materials influenced many more institutions that were offering distance education on a much smaller scale to adopt the ADDIE model, if in a more modest way, typically with a single instructor working with an instructional designer. As distance education courses became increasingly developed as online courses, the ADDIE model continued, and is now being used by instructional designers in many institutions for the re-design of large lecture classes, hybrid learning, and for fully online courses.
4.4.3 What are the benefits of ADDIE?
One reason it has been so successful is that it is heavily associated with good quality design, with clear learning objectives, carefully structured content, controlled workloads for faculty and students, integrated media, relevant student activities, and assessment strongly tied to desired learning outcomes. Although these good design principles can be applied with or without the ADDIE model, ADDIE is a model that allows these design principles to be identified and implemented on a systematic and thorough basis. It is also a very useful management tool, allowing for the design and development of large numbers of courses to a standard high quality.
4.4.5 What are the limitations of ADDIE?
The ADDIE approach can be used with any size of teaching project, but works best with large and complex projects. Applied to courses with small student numbers and a deliberately simple or traditional classroom design, it becomes expensive and possibly redundant, although there is nothing to stop an individual teacher following this strategy when designing and delivering a course.
A second criticism is that the ADDIE model is what might be called ‘front-end loaded’ in that it focuses heavily on content design and development, but does not pay as much attention to the interaction between instructors and students during course delivery. Thus it has been criticised by constructivists for not paying enough attention to learner-instructor interaction, and for privileging more behaviourist approaches to teaching.
Another criticism is that while the five stages are reasonably well described in most descriptions of the model, the model does not provide guidance on how to make decisions within that framework. For instance, it does not provide guidelines or procedures for deciding how to choose between different media, or what assessment strategies to use. Instructors have to go beyond the ADDIE framework to make these decisions.
The over-enthusiastic application of the ADDIE model can result in overly complex design stages, with many different categories of workers (faculty, instructional designers, editors, web designers) and consequently a strong division of labour, resulting in courses taking up to two years from initial approval to actual delivery. The more complex the design and management infrastructure, the more opportunities there are for cost over-runs and very expensive programming. It is a very good example of the industrial approach to course design.
My main criticism though is that the model is too inflexible for the digital age. How does a teacher respond to rapidly developing new content, new technologies or apps being launched on a daily basis, to a constantly changing student base? Although the ADDIE model has served us well in the past, and provides a good foundation for designing teaching and learning, it can be too pre-determined, linear and inflexible to handle more volatile learning contexts. I will discuss more flexible models for design in Section 4.7.
References
Dick, W., and Carey, L. (2004). The Systematic Design of Instruction Allyn & Bacon; 6 edition Allyn & Bacon
Morrison, Gary R. (2010) Designing Effective Instruction, 6th Edition New York: John Wiley & Sons
Activity 4.3 Using the ADDIE model
1. Take a course you are currently offering. How many of the stages of the ADDIE model did you go through? If you missed out on some of the stages, do you think the course would have been better if you had included these stages? Given the amount of work needed to work through each of the stages, do you think the results would be worth the effort?
2. If you are thinking of designing a new course, use the Flexible Learning Australia infographic to work through the four steps of analysis they recommend. Was this helpful? If so, you might want to continue with the other recommended steps.
3. If you have previously used the ADDIE model, are you happy with it? Do you agree with my criticisms? Is it flexible enough for the context in which you are working?
I do not provide feedback on these questions as they are for you to think about based on your own experience. | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/04%3A_Methods_of_teaching_with_an_online_focus/04.3%3A_The_ADDIE_model.txt |
Figure 4.4.1. Collaborative group work. In this example the teacher has organized a class of 19 students into six groups. The teacher can interact with individual students or with each group as a whole. In online collaborative learning each group can have its own discussion area which can be ‘closed’ (except to the teacher) or open to the other students. In this model, all communication is textual, over the Internet, using online discussion forum software. However, the model could be applied to video-conferencing, but usually with smaller numbers of students due to bandwidth restrictions, or to classroom teaching. Each mode of delivery though will need its own variations in design for it to work well. Image: Tony Bates, 2019.
4.4.1 What is online collaborative learning?
The concurrence of both constructivist approaches to learning and the development of the Internet has led to the development of a particular form of constructivist teaching, originally called computer-mediated communication (CMC), or networked learning, but which has been developed into what Harasim (2017) now calls online collaborative learning theory (OCL). She describes OCL as follows (p. 90):
OCL theory provides a model of learning in which students are encouraged and supported to work together to create knowledge: to invent, to explore ways to innovate, and, by so doing, to seek the conceptual knowledge needed to solve problems rather than recite what they think is the right answer. While OCL theory does encourage the learner to be active and engaged, this is not considered to be sufficient for learning or knowledge construction……In the OCL theory, the teacher plays a key role not as a fellow-learner, but as the link to the knowledge community, or state of the art in that discipline. Learning is defined as conceptual change and is key to building knowledge. Learning activity needs to be informed and guided by the norms of the discipline and a discourse process that emphasises conceptual learning and builds knowledge.
OCL builds on and integrates theories of cognitive development that focus on conversational learning (Pask, 1975), conditions for deep learning (Marton and Saljø, 1997; Entwistle, 2000), development of academic knowledge (Laurillard, 2001), and knowledge construction (Scardamalia and Bereiter, 2006).
From the very early days of online learning, some instructors have focused heavily on the communication affordances of the Internet (see for instance, Hiltz and Turoff, 1978). They have based their teaching on the concept of knowledge construction, the gradual building of knowledge mainly through asynchronous online discussion among students and between students and an instructor.
Online discussion forums go back to the 1970s, but really took off as a result of a combination of the invention of the WorldWide Web in the 1990s, high speed Internet access, and the development of learning management systems, most of which now include an area for online discussions. These online discussion forums have some differences though with classroom seminars:
• first, they are text based, not oral;
• second, they are asynchronous: participants can log in at any time, and from anywhere with an Internet connection;
• third, many discussion forums allow for ‘threaded’ connections, enabling a response to be attached to the particular comment which prompted the response, rather than just displayed in chronological order. This allows for dynamic sub-topics to be developed, with sometimes more than ten responses within a single thread of discussion. This enables participants to follow multiple discussion topics over a period of time.
4.4.2 Core design principles of OCL
Harasim emphasises the importance of three key phases of knowledge construction through discourse:
• idea generating: this is literally brainstorming, to collect the divergent thinking within a group;
• idea organising: this is where learners compare, analyse and categorise the different ideas previously generated, again through discussion and argument;
• intellectual convergence: the aim here is to reach a level of intellectual synthesis, understanding and consensus (including agreeing to disagree), usually through the joint construction of some artefact or piece of work, such as an essay or assignment.
This results in what Harasim calls a Final Position, although in reality the position is never final because for a learner, once started, the process of generating, organising and converging on ideas continues at an ever deeper or more advanced level. The role of the teacher or instructor in this process is seen as critical, not only in facilitating the process and providing appropriate resources and learner activities that encourage this kind of learning, but also, as a representative of a knowledge community or subject domain, in ensuring that the core concepts, practices, standards and principles of the subject domain are fully integrated into the learning cycle.
Harasim provides the following diagram to capture this process:
Figure 4.4.2: Harasim’s pedagogy of group discussion (from Harasim, 2017, p. 95, with permission)
Another important factor is that in the OCL model, discussion forums are not an addition or supplement to core teaching materials, such as textbooks, recorded lectures, or text in an LMS, but are the core component of the teaching. Textbooks, readings and other resources are chosen to support the discussion, not the other way round.
This is a key design principle, and explains why often instructors or tutors complain, in more ‘traditional’ online courses, that students don’t participate in discussions. Often this is because where online discussions are secondary to more didactic teaching, or are not deliberately designed and managed to lead to knowledge construction, students see the discussions as optional or extra work, because they have no direct impact on grades or assessment.
It is also a reason why awarding grades for participation in discussion forums misses the point. It is not the extrinsic activity that counts, but the intrinsic value of the discussion, that matters (see, for instance, Brindley, Walti and Blashke, 2009). Thus although instructors using an OCL approach may use learning management systems for convenience, they are used differently from courses where traditional didactic teaching is moved online.
4.4.3 Community of Inquiry
The Community of Inquiry Model (CoI) is somewhat similar to the OCL model. As defined by Garrison, Anderson and Archer (2000):
An educational community of inquiry is a group of individuals who collaboratively engage in purposeful critical discourse and reflection to construct personal meaning and confirm mutual understanding.
Garrison, Anderson and Archer argue that there are three essential elements of a community of inquiry:
• social presence: is “the ability of participants to identify with the community (e.g., course of study), communicate purposefully in a trusting environment, and develop inter-personal relationships by way of projecting their individual personalities.”
• teaching presence: is “the design, facilitation, and direction of cognitive and social processes for the purpose of realizing personally meaningful and educationally worthwhile learning outcomes
• cognitive presence: is “the extent to which learners are able to construct and confirm meaning through sustained reflection and discourse“.
Figure 4.4.3: Community of Inquiry Image: © Terry Anderson/Marguerite Koole, 2013
However, CoI is more of a theory than a model, since it does not indicate what activities or conditions are needed to create these three ‘presences’. The two models (OCL and CoI) are also more complementary rather than competing.
4.4.4 Developing meaningful online discussion
Since the publication of the original CoI paper in 2000, there have been a number of studies that have identified the importance of these ‘presences’ within especially online learning (click here for a wide selection). Although there has been a wide range of researchers and educators engaged in the area of online collaborative learning and communities of inquiry, there is a high degree of convergence and agreement about successful strategies and design principles. For academic and conceptual development, discussions need to be well organized by the teacher, and the teacher needs to provide the necessary support to enable the development of ideas and the construction of new knowledge for the students.
Partly as a result of this research, and partly as the result of experienced online instructors who have not necessarily been influenced by either the OCL or the Community of Inquiry literature, several other design principles have been associated with successful (online) discussion, such as:
• appropriate technology (for example, software that allows for threaded discussions);
• clear guidelines on student online behaviour, such as written codes of conduct for participating in discussions, and ensuring that they are enforced;
• student orientation and preparation, including technology orientation and explaining the purpose of discussion;
• clear goals for the discussions that are understood by the students, such as: ‘to explore gender and class issues in selected novels’ or ‘to compare and evaluate alternative methods of coding’;
• choice of appropriate topics, that complement and expand issues in the study materials, and are relevant to answering assessment questions;
• setting an appropriate ‘tone’ or requirements for discussion (for example, respectful disagreement, evidence-based arguments);
• defining clearly learner roles and expectations, such as ‘you should log in at least once a week to each discussion topic and make at least one substantive contribution to each topic each week’;
• monitoring the participation of individual learners, and responding accordingly, by providing the appropriate scaffolding or support, such as comments that help students develop their thinking around the topics, referring them back to study materials if necessary, or explaining issues when students seem to be confused or misinformed;
• regular, ongoing instructor ‘presence’, such as monitoring the discussions to prevent them getting off topic or too personal, and providing encouragement for those that are making real contributions to the discussion, heading off those that are trying to hog or dominate the discussions, and tracking those not participating, and helping them to participate;
• ensuring strong articulation between discussion topics and assessment.
These issues are discussed in more depth by Salmon (2000); Bates and Poole (2003); and Paloff and Pratt (2005;2007).
4.4.5 Cultural and epistemological issues
Students come to the educational experience with different expectations and backgrounds. As a result there are often major cultural differences in students with regard to participating in discussion-based collaborative learning that in the end reflect deep differences with regard to traditions of learning and teaching. Thus teachers need to be aware that there are likely to be students in any class who may be struggling with language, cultural or epistemological issues, but in online classes, where students can come from anywhere, this is a particularly important issue.
In many countries, there is a strong tradition of the authoritarian role of the teacher and the transmission of information from the teacher to the student. In some cultures, it would be considered disrespectful to challenge or criticize the views of teachers or even other students. In an authoritarian, teacher-based culture, the views of other students may be considered irrelevant or unimportant. Other cultures have a strong oral tradition, or one based on story-telling, rather than on direct instruction.
Online environments then can present real challenges to students when a constructivist approach to the design of online learning activities is adopted. This may mean taking specific steps to help students who are unfamiliar with a constructivist approach to learning, such as asking a student to send drafts to the instructor by e-mail for approval before posting a ‘class’ contribution. For a fuller discussion of cross-cultural issues in online learning, see Jung and Gunawardena (2014) and the journal Distance Education, Vol. 22, No. 1 (2001), the whole edition of which is devoted to papers on this topic.
4.4.6 Strengths and weaknesses of online collaborative learning
This approach to the use of technology for teaching is very different from the more objectivist approaches found in computer-assisted learning, teaching machines, and artificial intelligence applications to education, which primarily aim to use computing to replace at least some of the activities traditionally done by human teachers. With online collaborative learning, the aim is not to replace the teacher, but to use the technology primarily to increase and improve communication between teacher and learners, with a particular approach to the development of learning based on knowledge construction assisted and developed through social discourse. This social discourse furthermore is not random, but managed in such a way as to ‘scaffold’ learning:
• by assisting with the construction of knowledge in ways that are guided by the instructor;
• that reflect the norms or values of the discipline;
• that also respect or take into consideration the prior knowledge within the discipline.
Thus there are two main strengths of this model:
• when applied appropriately, online collaborative learning can lead to deep, academic learning, or transformative learning, as well as, if not better than, discussion in campus-based classrooms. The asynchronous and recorded ‘affordances’ of online learning more than compensate for the lack of physical cues and other aspects of face-to-face discussion;
• online collaborative learning as a result can also directly support the development of a range of high level intellectual skills, such as critical thinking, analytical thinking, synthesis, and evaluation, which are key requirements for learners in a digital age.
There are though some limitations:
• it does not scale easily, requiring highly knowledgeable and skilled instructors, and a limited number of learners per instructor;
• it is more likely to accommodate to the epistemological positions of faculty and instructors in humanities, social sciences, education and some areas of business studies and health and conversely it is likely to be less accommodating to the epistemological positions of faculty in science, computer science and engineering. However, if combined with a problem-based or inquiry-based approach, it might have acceptance even in some of the STEM subject domains.
4.4.7 Summary
Many of the strengths and challenges of collaborative learning apply both in face-to-face or online learning contexts. It could be argued that there is no or little difference between online collaborative learning and well-conducted traditional classroom, discussion-based teaching. Although there are necessary adaptations depending on the mode of delivery, many of the core principles of successful collaborative learning (see Barkley, Major and Cross, 2014) will apply in both online and face-to-face teaching. Once again, we see that the mode of delivery is less important than the design model, which can work well in both contexts. Indeed, it is possible to conduct successful collaborative learning synchronously or asynchronously, at a distance or face-to-face.
However, there is plenty of evidence that collaborative learning can be done just as well online, which is important, given the need for more flexible models of delivery to meet the needs of a more diverse student body in a digital age. Also, the necessary conditions for success in teaching this way are now well known, even though they are not always universally applied.
References
Barkley, E., Major, C.H., and Cross, K.P. (2017) Collaborative Learning Techniques San Francisco: Jossey-Bass/Wiley
Bates, A. and Poole, G. (2003) Effective Teaching with Technology in Higher Education: Foundations for Success San Francisco: Jossey-Bass
Brindley, J., Walti, C. and Blashke, L. (2009) Creating Effective Collaborative Learning Groups in an Online Environment International Review of Research in Open and Distance Learning, Vol. 10, No. 3
Entwistle, N. (2000) Promoting deep learning through teaching and assessment: conceptual frameworks and educational contexts Leicester UK: TLRP Conference
Garrison, R., Anderson, A. and Archer, W. (2000) Critical Inquiry in a Text-based Environment: Computer Conferencing in Higher Education The Internet and Higher Education, Vol. 2, No. 3
Harasim, L. (2017) Learning Theory and Online Technologies 2nd edition New York/London: Taylor and Francis
Hiltz, R. and Turoff, M. (1978) The Network Nation: Human Communication via Computer Reading MA: Addison-Wesley
Jung, I. and Gunawardena, C. (eds.) (2014) Culture and Online Learning: Global Perspectives and Research Sterling VA: Stylus
Laurillard, D. (2001) Rethinking University Teaching: A Conversational Framework for the Effective Use of Learning Technologies New York/London: Routledge
Marton, F. and Saljö, R. (1997) Approaches to learning, in Marton, F., Hounsell, D. and Entwistle, N. (eds.) The experience of learning: Edinburgh: Scottish Academic Press (out of press, but available online)
Paloff, R. and Pratt, K. (2005) Collaborating Online: Learning Together in Community San Francisco: Jossey-Bass
Paloff, R. and Pratt, K. (2007) Building Online Learning Communities: Effective Strategies for the Virtual Classroom San Francisco: Jossey-Bass
Pask, G. (1975) Conversation, Cognition and Learning Amsterdam/London: Elsevier (out of press, but available online here)
Salmon, G. (2000) e-Moderating: The Key to Teaching and Learning Online London: Taylor and Francis
Scardamalia, M. and Bereiter, C. (2006) Knowledge Building: Theory, pedagogy and technology in Sawyer, K. (ed.) Cambridge Handbook of the Learning Sciences New York: Cambridge University Press
Activity 4.4: Evaluating online collaborative learning models
1. Can you see the differences between ‘Open Collaborative Learning’ (OCL) and ‘Communities of Inquiry’? Or are they really the same model with different names?
2. Do you agree that either of these models can be applied just as successfully online or face-to-face?
3. Do you see other strengths or weaknesses with these models?
4. Is this common sense dressed up as theory?
5. Does it make sense to apply either of these models to courses in the quantitative sciences such as physics or engineering? If so, under what conditions?
For my comments on these questions, click on the podcast below:
An audio element has been excluded from this version of the text. You can listen to it online here: https://pressbooks.bccampus.ca/teachinginadigitalagev2/?p=125 | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/04%3A_Methods_of_teaching_with_an_online_focus/04.4%3A_Online_collaborative_learning.txt |
Figure 4.5.1 e-Commerce business course competencies, Capella University
4.5.1 What is competency-based learning?
Competency-based learning begins by identifying specific competencies or skills, and enables learners to develop mastery of each competency or skill at their own pace, usually working with a mentor. Learners can develop just the competencies or skills they feel they need (for which increasingly they may receive a ‘badge’ or some form of validated recognition), or can combine a whole set of competencies into a full qualification, such as a certificate, diploma or increasingly a full degree.
Learners work individually, usually online, rather than in cohorts. If learners can demonstrate that they already have mastery of a particular competency or skill, through a test or some form of prior learning assessment, they may be allowed to move to the next level of competency without having to repeat a prescribed course of study for the prior competency. Competency-based learning attempts to break away from the regularly scheduled classroom model, where students study the same subject matter at the same speed in a cohort of fellow students.
The value of competency-based learning for developing practical or vocational skills or competencies is more obvious, but increasingly competency-based learning is being used for education requiring more abstract or academic skills development, sometimes combined with other cohort-based courses or programs.
4.5.2 Who uses competency-based learning?
The Western Governors University in the USA, with nearly 40,000 students, has pioneered competency-based learning, but, with the more recent support of the Federal Department of Education, competency-based learning is expanding rapidly in the USA. Other institutions making extensive use of competency-based learning are Southern New Hampshire University through its College for America, designed specifically for working adults and their employers, Northern Arizona University, and Capella University.
Competency-based learning is particularly appropriate for adult learners with life experience who may have developed competencies or skills without formal education or training, for those who started school or college and dropped out and wish to return to formal study, but want their learning ‘after school’ to be recognized, or for those learners wanting to develop specific skills but not wanting a full program of studies. Competency-based learning can be delivered through a campus program, but it is increasingly delivered fully online, because many students taking such programs are already working or seeking work, and because technology enables each student a distinct pathway through content based on their prior knowledge.
4.5.3 Designing competency-based learning
There are various approaches, but the Western Governors’ model illustrates many of the key steps.
4.5.3.1 Defining competencies
A feature of most competency-based programs is a partnership between employers and educators in identifying the competencies required, at least at a high level. Some of the skills outlined in Chapter 1, such as problem-solving or critical thinking, may be considered high-level, but competency-based learning tries to break down abstract or vague goals into specific, measurable competencies.
For instance, at Western Governors University (WGU), for each degree, a high-level set of competencies is defined by the University Council, and then a working team of contracted subject matter experts takes the ten or so high level competencies for a particular qualification and breaks them down into about 30 more specific competencies, around which are built online courses to develop mastery of each competency. Competencies are based upon what graduates are supposed to know in the workplace and as professionals in a chosen career. Assessments are designed specifically to assess the mastery of each competency; thus students receive either a pass/no pass following assessment. A degree is awarded when all 30 specified competencies are successfully achieved.
Defining competencies that meet the needs of students and employers in ways that are progressive (in that one competency builds on earlier competencies and leads to more advanced competencies) and coherent (in that the sum of all the competencies produces a graduate with all the knowledge and skills required within a business or profession) is perhaps the most important and most difficult part of competency-based learning.
4.5.3.2 Course and program design
At WGU, courses are created by in-house subject matter experts selecting existing online curriculum from third parties and/or resources such as e-textbooks through contracts with publishers. Increasingly open educational resources are used. WGU does not use a learning management system but a specially designed portal for each course. E-textbooks are offered to students without extra cost to the student, through contracts between WGU and the publishers. Courses are pre-determined for the student with no electives. Students are admitted on a monthly basis and work their way through each competency at their own pace.
Students who already possess competencies may accelerate through their program in two ways: transferring in credits from a previous associate degree in appropriate areas (e.g. general education, writing); or by taking exams when they feel they are ready.
4.5.3.3 Learner support
Again this varies from institution to institution. WGU currently employs approximately 750 faculty who act as mentors. There are two kinds of mentors: ‘student’ mentors and ‘course’ mentors. Student mentors, who have qualifications within the subject domain, usually at a masters level, are in at least bi-weekly telephone contact with their students, depending on the needs of the student in working through their courses, and are the main contact for students. A student mentor is responsible for roughly 85 students. Students start with a mentor from their first day and stay with their mentor until graduation. Student mentors assist students in determining and maintaining an appropriate pace of study, and step in with help when students are struggling.
Course mentors are more highly qualified, usually with a doctorate, and provide extra support for students when needed. Course mentors will be available to between 200-400 students at a time, depending on the subject requirement.
Students may contact either student or course mentors at any time (unlimited access) and mentors are expected to deal with student calls within one business day. Mentors are full-time but work flexible hours, usually from home. Mentors are reasonably well paid, and receive extensive training in mentoring.
4.5.3.4 Assessment
WGU uses written papers, portfolios, projects, observed student performance and computer-marked assignments as appropriate, with detailed rubrics. Assessments are submitted online and if they require human evaluation, qualified graders (subject matter experts trained by WGU in assessment) are randomly assigned to mark work on a pass/fail basis. If students fail, the graders provide feedback on the areas where competency was not demonstrated. Students may resubmit if necessary.
Figure 4.5.2 Remote proctoring of exams at Western Governors’ University
Students will take both formative (pre-assessment) and summative (proctored) exams. WGU is increasingly using online proctoring, enabling students to take an exam at home under video supervision, using facial recognition technology to ensure that the registered student is taking the exam. In areas such as teaching and health, student performance or practice is assessed in situ by professionals (teachers, nurses).
Figure 4.5.3 Example transcript from Northern Arizona University
4.5.4 Strengths and weaknesses
Proponents have identified a number of strengths in the competency-based learning approach:
• it meets the immediate needs of businesses and professions; students are either already working, and receive advancement within the company, or if unemployed, are more likely to be employed once qualified;
• it enables learners with work or family commitments to study at their own pace;
• for some students, it speeds up time to completion of a qualification by enabling prior learning to be recognized;
• students get individual support and help from their mentors;
• tuition fees are affordable (US\$6,000 per annum at WGU) and programs can be self-funding from tuition fees alone, since WGU uses already existing study materials and increasingly open educational resources;
• competency-based education is being recognized as eligible for Federal loans and student aid in the USA.
Consequently, institutions such as WGU, the University of Southern New Hampshire, and Northern Arizona University, using a competency-based approach, at least as part of their operations, have seen annual enrolment growth in the range of 30-40 per cent per annum.
Its main weakness is that it works well with some learning environments and less well with others. In particular:
• it focuses on immediate employer needs and is less focused on preparing learners with the flexibility needed for a more uncertain future;
• it does not suit subject areas where it is difficult to prescribe specific competencies or where new skills and new knowledge need to be rapidly accommodated;
• it takes an objectivist approach to learning; constructivists would argue that skills are not either present or absent (pass or fail), but have a wide range of performance and continue to develop over time;
• it ignores the importance of social learning;
• it will not fit the preferred learning styles of many students.
A 2015 report by EAB, a private educational consultancy, identified three ‘myths’ about about competency-based education:
• high demand: in fact EAB reported a lack of demand from students or employers
• faster and cheaper for students: in fact it is difficult for students, especially working adults, to complete competencies fast enough for there to be savings over conventional programs
• cheaper for institutions: in fact, because of the need for new systems such as on-demand registration, and different reporting for government financial aid, institutional costs are often higher than anticipated
4.5.5 In conclusion
Competency-based learning is a relatively new approach to learning design which is proving increasingly popular with employers and suits certain kinds of learners such as adults seeking to re-skill or searching for mid-level jobs requiring relatively easily identifiable skills. It does not suit though all kinds of learners and may be limited in developing the higher level, more abstract knowledge and skills requiring creativity, high-level problem-solving and decision-making and critical thinking.
Further reading
At the time of writing, there is comparatively little literature and even less research on competency-based learning compared with most other teaching approaches. It is also an area that has recently evolved from earlier, more training-focused approaches to competency. I have therefore limited myself to more recent publications. The following publications are recommended for those who would like to pursue this area further:
Book, P. (2014) All Hands on Deck: Ten Lessons from Early Adopters of Competency-based Education Boulder CO: WCET
Cañado, P. and Luisa, M. (eds.) (2013) Competency-based Language Teaching in Higher Education New York: Springer
EAB (2015) Three Myths About Competency-Based Education Washington DC: Education Advisory Board
Garrett, R. and Lurie, H. (2016) Deconstructing CBE: An Assessment of Institutional Activity, Goals and Challenges in Higher Education Boston MA: Ellucian/Eduventures
Rothwell, W. and Graber, J. (2010) Competency-Based Training Basics Alexandria VA: ADST
Weise, M. (2014) Got Skills? Why Online Competency-Based Education Is the Disruptive Innovation for Higher Education EDUCAUSE Review, November 10
The Southern Regional Educational Board in the USA has a comprehensive Competency-based Learning Bibliography
Activity 4.5 Thinking about competency-based education?
1. What factors are likely to influence you to adopt a competency-based approach to teaching? Could you describe a scenario where you could use this approach effectively?
2. What are the advantages and disadvantages of students studying individually, rather than in a cohort? What skills are they likely to miss out on through individual study?
3. Is competency-based learning something an individual instructor should contemplate? What institutional support would be necessary to make this approach work?
For my response to these questions, click on the podcast below:
An audio element has been excluded from this version of the text. You can listen to it online here: https://pressbooks.bccampus.ca/teachinginadigitalagev2/?p=130 | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/04%3A_Methods_of_teaching_with_an_online_focus/04.5%3A_Competency-based_learning.txt |
Figure 4.6.1 Bank of America’s Vital Voices program links women executives of small and medium sized enterprises from around the world
Image: © Belfast Telegraph, 2014
4.6.1 The theories behind communities of practice
The design of teaching often integrates different theories of learning. Communities of practice are one of the ways in which experiential learning, social constructivism, and connectivism can be combined, illustrating the limitations of trying to rigidly classify learning theories. Practice tends to be more complex.
4.6.2 What are communities of practice?
4.6.2.1 Definition:
Communities of practice are groups of people who share a concern or a passion for something they do and learn how to do it better as they interact regularly.
Wenger, 2014
4.6.2.2 What are communities of practice?
The basic premise behind communities of practice is simple: we all learn in everyday life from the communities in which we find ourselves. Communities of practice are everywhere. Nearly everyone belongs to some community of practice, whether it is through our working colleagues or associates, our profession or trade, or our leisure interests, such as a book club. Wenger (2000) argues that a community of practice is different from a community of interest or a geographical community in that it involves a shared practice: ways of doing things that are shared to some significant extent among members.
4.6.2.3 Characteristics
Wenger argues that there are three crucial characteristics of a community of practice:
• domain: a common interest that connects and holds together the community;
• community: a community is bound by the shared activities they pursue (for example, meetings, discussions) around their common domain;
• practice: members of a community of practice are practitioners; what they do informs their participation in the community; and what they learn from the community affects what they do.
4.6.2.4 Innovation and change
Wenger (2000) has argued that although individuals learn through participation in a community of practice, more important is the generation of newer or deeper levels of knowledge through the sum of the group activity. If the community of practice is centered around business processes, for instance, this can be of considerable benefit to an organization. Smith (2003) notes that:
…communities of practice affect performance..[This] is important in part because of their potential to overcome the inherent problems of a slow-moving traditional hierarchy in a fast-moving virtual economy. Communities also appear to be an effective way for organizations to handle unstructured problems and to share knowledge outside of the traditional structural boundaries. In addition, the community concept is acknowledged to be a means of developing and maintaining long-term organizational memory.
Brown and Duguid (2000) describe a community of practice developed around the Xerox customer service representatives who repaired the machines in the field. The Xerox reps began exchanging tips and tricks over informal meetings at breakfast or lunch and eventually Xerox saw the value of these interactions and created the Eureka project to allow these interactions to be shared across the global network of representatives. The Eureka database has been estimated to have saved the corporation \$100 million. Companies such as Google and Apple are encouraging communities of practice through the sharing of knowledge across their many specialist staff.
2.6.2.5 Technologies
Technology provides a wide range of tools that can support communities of practice, as indicated by Wenger (2014) in the diagram below:
Figure 4.6.2 Tools that support communities of practice
Image: Wenger, 2014
4.6.3 Designing effective communities of practice
Most communities of practice have no formal design and tend to be self-organising systems. They have a natural life cycle, and come to an end when they no longer serve the needs of the community. However, there is now a body of theory and research that has identified actions that can help sustain and improve the effectiveness of communities of practice.
Wenger, McDermott and Snyder (2002) have identified seven key design principles for creating effective and self-sustaining communities of practice, related specifically to the management of the community, although the ultimate success of a community of practice will be determined by the activities of the members of the community themselves. Designers of a community of practice need to:
4.6.3.1 Design for evolution
Ensure that the community can evolve and shift in focus to meet the interests of the participants without moving too far from the common domain of interest.
4.6.3.2 Open a dialogue between inside and outside perspectives
Encourage the introduction and discussion of new perspectives that come or are brought in from outside the community of practice.
4.6.3.3 Encourage and accept different levels of participation
Different levels of participation include:
• the ‘core’ (most active members),
• those who participate regularly but do not take a leading role in active contributions,
• hose (likely the majority) who are on the periphery of the community but may become more active participants if the activities or discussions start to engage them more fully.
4.6.3.4 Develop both public and private community spaces
Communities of practice are strengthened if they encourage individual or group activities that are more personal or private as well as the more public general discussions; for instance, individuals may decide to blog about their activities, or a small group in an online community that live or work close together may also decide to meet informally on a face-to-face basis.
4.6.3.5 Focus on value
Attempts should be made explicitly to identify, through feedback and discussion, the contributions that the community most values.
4.6.3.6 Combine familiarity and excitement
Focus both on shared, common concerns and perspectives, but also on the introduction of radical or challenging perspectives for discussion or action.
4.6.3.7 Create a rhythm for the community
There needs to be a regular schedule of activities or focal points that bring participants together on a regular basis, within the constraints of participants’ time and interests.
4.6.4 Critical factors for success
Subsequent research has identified a number of critical factors that influence the effectiveness of participants in communities of practice, These include being:
• aware of social presence: individuals need to feel comfortable in engaging socially with other professionals or ‘experts’ in the domain, and those with greater knowledge must be willing to share in a collegial manner that respects the views and knowledge of other participants (social presence is defined as the awareness of others in an interaction combined with an appreciation of the interpersonal aspects of that interaction.)
• motivated to share information for the common good of the community
• able and willing to collaborate.
EDUCAUSE has developed a step-by-step guide for designing and cultivating communities of practice in higher education (Cambridge, Kaplan and Suter, 2005).
Lastly, research on other related sectors, such as collaborative learning or MOOCs, can inform the design and development of communities of practice. For instance, communities of practice need to balance between structure and chaos: too much structure and many participants are likely to feel constrained in what they need to discuss; too little structure and participants can quickly lose interest or become overwhelmed.
Many of the other findings about group and online behaviour, such as the need to respect others, observing online etiquette, and preventing certain individuals from dominating the discussion, are all likely to apply. However, because many communities of practice are by definition self-regulating, establishing rules of conduct and even more so enforcing them is really a responsibility of the participants themselves.
4.6.5 Learning through communities of practice in a digital age
Communities of practice are a powerful manifestation of informal learning. They generally evolve naturally to address commonly shared interests and problems. By their nature, they tend to exist outside formal educational organisations. Participants are not usually looking for formal qualifications, but to address issues in their life and to be better at what they do. Furthermore, communities of practice are not dependent on any particular medium; participants may meet face-to-face socially or at work, or they can participate in online or virtual communities of practice.
It should be noted that communities of practice can be very effective in a digital world, where the working context is volatile, complex, uncertain and ambiguous. A large part of the lifelong learning market will become occupied by communities of practice and self-learning, through collaborative learning, sharing of knowledge and experience, and crowd-sourcing new ideas and development. Such informal learning provision will be particularly valuable for non-governmental or charitable organizations, such as the Red Cross, Greenpeace or UNICEF, or local government, looking for ways to engage communities in their areas of operation.
These communities of learners will be open and free, and hence will provide a competitive alternative to the high priced lifelong learning programs being offered by research universities. This will put pressure on universities and colleges to provide more flexible arrangements for recognition of informal learning, in order to hold on to their current monopoly of post-secondary accreditation.
One of the significant developments in recent years has been the use of massive open online courses (MOOCs) for developing online communities of practice. MOOCs are discussed in more detail in Chapter 5, but it is worth discussing here the connection between MOOCs and communities of practice. The more instructionist xMOOCs are not really developed as communities of practice, because they use mainly a transmissive pedagogy, from experts to those considered less expert.
In comparison, connectivist MOOCs are an ideal way to bring together specialists scattered around the world to focus on a common interest or domain. Connectivist MOOCs are much closer to being virtual communities of practice, in that they put much more emphasis on sharing knowledge between more or less equal participants. However, current connectivist MOOCs do not always incorporate what research indicates are best practices for developing communities of practice, and those wanting to establish a virtual community of practice at the moment need some kind of MOOC provider to get them started and give them access to the necessary MOOC software.
Although communities of practice are likely to become more rather than less important in a digital age, it is probably a mistake to think of them as a replacement for traditional forms of education. There is no single, ‘right’ approach to the design of teaching. Different groups have different needs. Communities of practice are more of an alternative for certain kinds of learners, such as lifelong learners, and are likely to work best when participants already have some domain knowledge and can contribute personally and in a constructive manner – which suggests the need for at least some form of prior general education or training for those participating in effective communities of practice.
In conclusion, it is clear is that in an increasingly volatile, uncertain, complex, and ambiguous world, and given the openness of the Internet, the social media tools now available, and the need for sharing of knowledge on a global scale, virtual communities of practice will become even more common and important. Smart educators and trainers will look to see how they can harness the strength of this design model, particularly for lifelong learning. However, merely lumping together large numbers of people with a common interest is unlikely to lead to effective learning. Attention needs to be paid to those design principles that lead to effective communities of practice.
Update and further reading
Wenger, E., Trayner, B. and de Laat, M. (2011)Promoting and assessing value creation in communities and networks: a conceptual framework Heerlen NL: The Open University of the Netherlands
This document presents a conceptual foundation for promoting and assessing value creation in communities and networks. By value creation we mean the value of the learning enabled by community involvement and networking.
For an interesting critique of this paper, see:
Dingyloudi, F. and Strijbos, J. (2015) Examining value creation in a community of learning practice: Methodological reflections on story-telling and story-reading Seminar.net, Vol. 11, No.3
Activity 4.6 Making communities of practice work
1. Can you identify a community of practice to which you belong? Is it successful and does it meet the key design principles outlined above?
2. Could you think of a way to develop a community of practice that would support your work as a teacher?
3. Is there anything special you would need to do to make an online community of practice succeed that would not be necessary in a face-to-face community?
For my (not very deep) thoughts on these questions, click on the podcast below.
An audio element has been excluded from this version of the text. You can listen to it online here: https://pressbooks.bccampus.ca/teachinginadigitalagev2/?p=134 | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/04%3A_Methods_of_teaching_with_an_online_focus/04.6%3A_Communities_of_practice.txt |
4.7.1 The need for more agile design models
Adamson (2012) states:
The systems under which the world operates and the ways that individual businesses operate are vast and complex – interconnected to the point of confusion and uncertainty. The linear process of cause and effect becomes increasingly irrelevant, and it is necessary for knowledge workers to begin thinking in new ways and exploring new solutions.
In particular, knowledge workers must deal with situations and contexts that are volatile, uncertain, complex and ambiguous (what Adamson calls a VUCA environment). This certainly applies to teachers working with ever new, emerging technologies, very diverse students, and a rapidly changing external world that puts pressure on institutions to change.
If we look at course design, how does a teacher respond to rapidly developing new content, new technologies or apps being launched on a daily basis, to a constantly changing student base, to pressure to develop the knowledge and skills that are needed in a digital age? For instance, even setting prior learning outcomes is fraught in a VUCA environment, unless you set them at an abstract ‘skill’ level such as thinking flexibly, networking, and information retrieval and analysis. Students need to develop the key knowledge management skills of knowing where to find relevant information, how to assess, evaluate and appropriately apply such information. This means exposing students to less than certain knowledge and providing them with the skills, practice and feedback to assess and evaluate such knowledge, then apply that to solving real world problems.
In order to do this, learning environments need to be created that are rich and constantly changing, but which at the same time enable students to develop and practice the skills and acquire the knowledge they will need in a volatile, uncertain, complex and ambiguous world.
4.7.2 Core features of agile design models
Describing the design features of this model is a challenge, for two reasons. First, there is no single approach to agile design. The whole point is to be adaptable to the circumstances in which it operates. Second, it is only with the development of light, easy to use technology and media in the last few years that instructors and course designers have started to break away from the standard design models, so agile designs are still emerging. However, this is a challenge that software designers have also been facing (see for instance, Larman and Vodde, 2009; Ries, 2011) and perhaps there are lessons that can be applied to educational design.
First, it is important to distinguish ‘agile’ design from rapid instructional design (Meier, 2000) or rapid prototyping, which are really both streamlined versions of the ADDIE model. Although rapid instructional design/rapid protyping enable courses or modules to be designed more quickly (especially important for corporate training), they still follow the same kind of sequential or iterative processes as in the ADDIE model, but in a more compressed form. Rapid instructional design and rapid prototyping might be considered particular kinds of agile design, but they lack some of the most important characteristics outlined below:
4.7.2.1 Light and nimble
If ADDIE is a 100-piece orchestra, with a complex score and long rehearsals, then agile design is a jazz trio who get together for a single performance then break up until the next time. Although there may be a short preparation time before the course starts, most of the decisions about what will go into the course, what tools will be used, what activities learners will do, and sometimes even how students will be assessed, are decided as the course progresses.
On the teaching side, there are usually only a few people involved in the actual design, one or sometimes two instructors and possibly an instructional designer, who nevertheless meet frequently during the offering of the course to make decisions based on feedback from learners and how learners are progressing through the course. However, many more content contributors may be invited – or spontaneously offer – to participate on a single occasion as the course progresses.
4.7.2.2 Content, learner activities, tools used and assessment vary, according to the changing environment
The content to be covered in a course is likely to be highly flexible, based more on emerging knowledge and the interests or prior experience of the learners, although the core skills that the course aims to develop are more likely to remain constant. For instance, for ETEC 522 in Scenario F, the overall objective is to develop the skills needed to be a pioneer or innovator in education, and this remains constant over each iteration of the course. However, because the technology is rapidly developing with new products, apps and services every year, the content of the course is quite different from year to year.
Also learner activities and methods of assessment are also likely to change, because students can use new tools or technology themselves for learning as they become available. Very often learners themselves seek out and organise much of the core content of the course and are free to choose what tools they use.
4.7.2.3 The design attempts to exploit the affordances of either existing or emerging technologies
Agile design aims to exploit fully the educational potential of new tools or software, which means sometimes changing at least sub-goals. This may mean developing different skills in learners from year to year, as the technology changes and allows new things to be done. The emphasis here is not so much on doing the same thing better with new technology, but striving for new and different outcomes that are more relevant in a digital world.
ETEC 522 for instance did not start with a learning management system. Instead, a web site, built in WordPress, was used as the starting point for student activities, because students as well as instructors were posting content, but in another year the content focus of the course was mainly on mobile learning, so apps and other mobile tools were strong components of the course.
4.7.2.4 Sound, pedagogical principles guide the overall design of a course – to a point
Just as most successful jazz trios work within a shared framework of melody, rhythm, and musical composition, so is agile design shaped by overarching principles of best practice. Most successful agile designs have been guided by core design principles associated with ‘good’ teaching, such as clear learning outcomes or goals, assessment linked to these goals, strong learner support, including timely and individualised feedback, active learning, collaborative learning, and regular course maintenance based on learner feedback, all within a rich learning environment (see Appendix 1). Sometimes though deliberate attempts are made to move away from an established best practice for experimental reasons, but usually on a small scale, to see if the experiment works without risking the whole course.
4.7.2.5 Experiential, open and applied learning
Usually agile course design is strongly embedded in the real, external world. Much or all the course may be open to other than registered students. For instance, much of ETEC 522, such as the final YouTube business pitches, is openly available to those interested in the topics. Sometimes this results in entrepreneurs contacting the course with suggestions for new tools or services, or just to share experience.
Another example is a course on Latin American studies from a Canadian university. This particular course had an open, student-managed wiki, where they could discuss contemporary events as they arose. This course was active at the same time that the Argentine government nationalised the Spanish oil company, Repsol. Several students posted comments critical of the government action, but after a week, a professor from a university in Argentina, who had come across the wiki by accident while searching the Internet, responded, laying out a detailed defence of the government’s policy. This was then made a formal topic for discussion within the course.
Such courses may though be only partially open. Discussion of sensitive subjects for instance may still take place behind a password controlled discussion forum, while other parts of the course may be open to all. As experience grows in this kind of design, other and perhaps clearer design principles are likely to emerge.
4.7.3 Strengths and weaknesses of flexible design models
The main advantage of agile design is that it focuses directly on preparing students for a volatile, uncertain, complex and ambiguous world. It aims explicitly at helping students develop many of the specific skills they will need in a digital age, such as knowledge management, multimedia communication skills, critical thinking, innovation, and digital literacy embedded within a subject domain. Where agile design has been successfully used, students have found the design approach highly stimulating and great fun, and instructors have been invigorated and enthusiastic about teaching. Agile design enables courses to be developed and offered quickly and at much lower initial cost than ADDIE-based approaches.
However, agile design approaches are very new and have not really been much written about, never mind evaluated. There is no ‘school’ or set of agreed principles to follow, although there are similarities between the agile approach to design for learning with ‘agile’ design for computer software. Indeed it could be argued that most of the things in agile design are covered in other teaching models, such as online collaborative learning or experiential learning. Despite this, innovative instructors are beginning to develop courses in a similar way to ETEC 522 and there is a consistency in the basic design principles that give them a certain coherence and shape, even though each course or program appears on the surface to be very different (another example of agile design, but campus-based, with quite a different overall program from ETEC 522, is the Integrated Science program at McMaster University.)
Certainly agile design approaches require confident instructors willing to take a risk, and success is heavily dependent on instructors having a good background in best teaching practices and/or strong instructional design support from innovative and creative instructional designers. Because of the relative lack of experience in such design approaches the limitations are not well identified yet. For instance, this approach can work well with relatively small class sizes but how well will it scale? Successful use probably also depends on learners already having a good foundational knowledge base in the subject domain. Nevertheless I expect more agile designs for learning to grow over the coming years, because they are more likely to meet the needs of a VUCA world.
References
Adamson, C. (2012) Learning in a VUCA world, OEB Insights, November 13,
Bertram, J. (2013) Agile Learning Design for Beginners New Palestine IN: Bottom Line Performance Larman, C. and Vodde, B. (2009) Scaling Lean and Agile Development New York: Addison-Wesley Meier, D. (2000). The Accelerated Learning Handbook. New York: McGraw-Hill Rawsthorne, P. (2012) Agile Instructional Design St. John’s NF: Memorial University of Newfoundland Ries, E. (2011) The Lean Start-Up New York: Crown Business/Random House
Activity 4.7 Taking risks with ‘agile’ design
1. Do you think a ‘agile’/flexible design approach will increase or undermine academic excellence? What are your reasons?
2. Would you like to try something like this in your own teaching (or are you already doing something like this)? What would be the risks and benefits in your subject area of doing this?
For my comments on this activity, click on the podcast below:
An audio element has been excluded from this version of the text. You can listen to it online here: https://pressbooks.bccampus.ca/teachinginadigitalagev2/?p=139 | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/04%3A_Methods_of_teaching_with_an_online_focus/04.7%3A_Agile_Design%3A_flexible_designs_for_learning.txt |
Figure 4.8.1 Making decisions about which design model to choose
4.8.1 Choosing a method
Chapters 3 and 4 cover a range of different teaching methods and design models. There are many more that could have been included. I will be discussing open pedagogy in Chapter 11, Section 4. MOOCs are also a notable omission. However, the design models behind MOOCs require a full chapter of their own (Chapter 5.)
Your choice of teaching method and the design of the teaching within that method will depend very much on the context in which you are teaching. However, a key criterion should be the suitability of the method and/or design model for developing the knowledge and skills that learners will need in a digital age. Other critical factors will be the demands of the subject domain, characteristics of the learners you will likely be teaching, the resources available, especially in terms of supporting learners, and probably most important of all, your own views and beliefs about what constitutes ‘good teaching.’
Furthermore, the teaching methods covered in Chapters 3 and 4 by and large are not mutually exclusive. They can probably be mixed and matched to a certain degree, but there are limitations in doing this. Moreover, a consistent approach will be less confusing not only to learners, but also to you as a teacher or instructor.
So: how would you go about choosing an appropriate teaching method? I set out below in Figure 4.8.2 one way of doing this. I have chosen five criteria as headings along the top of the table:
4.8.1.1 Epistemological basis
What epistemology does this method suggest? Does the method suggest a view of knowledge as content that must be learned, does the method suggest a rigid (‘correct’) way of designing learning (objectivist)? Or does the method suggest that learning is a dynamic process and knowledge needs to be discovered and is constantly changing (constructivist)? Does the method suggest that knowledge lies in the connections and interpretations of different nodes or people on networks and that connections matter more in terms of creating and communicating knowledge than the individual nodes or people on the network (connectivist)? Or is the method epistemologically neutral, in that one could use the same method to teach from different epistemological positions?
4.8.1.2 Industrial (20th century) or digital (21st century)
Does this method lead to the kind of learning that would prepare people for an industrial society, with standardised learning outcomes, will it help identify and select a relatively small elite for higher education or senior positions in society, does it enable learning to be easily organised into similarly performing groups of learners?
Alternatively, does the method encourage the development of the soft skills and the effective management of knowledge needed in a digital world? Does the method enable and support the appropriate educational use of the affordances of new technologies? Does it provide the kind of educational support that learners need to succeed in a volatile, uncertain, complex and ambiguous world? Does it enable and encourage learners to become global citizens?
4.8.1.3 Academic quality
Does the method lead to deep understanding and transformative learning? Does it enable students to become experts in their chosen subject domain?
4.8.1.4 Flexibility
Does the method meet the needs of the diversity of learners today? Does it encourage open and flexible access to learning? Does it help teachers and instructors to adapt their teaching to ever changing circumstances?
Now these are my criteria, and you may well want to use different criteria (cost or your time is another important factor), but I have drawn up the table this way because it has helped me consider better where I stand on the different methods or design models. Where I think a method or design model is strong on a particular criterion, I have given it three stars, where weak, one star, and n/a for not applicable. Again, you may – no, should – rank the models differently. (See, that’s why I’m a constructivist – if I was an objectivist, I’d tell you what damned criteria to use!)
Figure 4.8.2 Choosing design models
It can be seen that the only method that ranks highly on all three criteria of 21st century learning, academic quality and flexibility is online collaborative learning. Experiential learning and agile design also score highly. Transmissive lectures come out worst. This is a pretty fair reflection of my preferences. However, if you are teaching first year civil engineering to over 500 students, your criteria and rankings will almost certainly be different from mine. So please see Figure 4.8.2 as a heuristic device and not as a general recommendation.
4.8.2 Design models and the quality of teaching and learning
Lastly, the review of different methods indicate some of the key issues around quality:
• first, what students learn is more likely to be influenced by choosing an appropriate teaching method for the context in which you are teaching, than by focusing on a particular technology or delivery method (face-to-face or online). Technology and delivery method are more about access and flexibility and hence learner characteristics than they are about learning. Learning is affected more by pedagogy and the design of instruction;
• second, different teaching methods are likely to lead to different kinds of learning outcomes. This is why there is so much emphasis in this book on being clear about what knowledge and skills are needed in a digital age. These are bound to vary somewhat across different subject domains, but only to a limited degree. Understanding of content is always going to be important, but the skills of independent learning, critical thinking, innovation and creativity are even more important. Which teaching method is most likely to help develop these skills in your students?
• third, quality depends not only on the choice of an appropriate teaching method, but also on how that approach to teaching is implemented. Online collaborative learning can be done well, or it can be done badly. The same applies to other methods. Following core design principles is critical for the successful use of any particular teaching method. Also there is considerable research on what the conditions are for success in using some of the newer methods or design models. The findings from such research need to be applied when implementing a particular method (this is discussed further throughout the book, but specifically in Chapter 12);
• lastly students and teachers get better with practice. If you are moving to a new method of teaching or design model, give yourself (and your students) time to get comfortable with it. It will probably take two or three courses where the new method or design is applied before you begin to feel comfortable that it is producing the results you were hoping for. However, it is better to make some mistakes along the way than to continue to teach comfortably, but not produce the graduates that are needed in the future.
There are still two major teaching methods to be discussed, Open Pedagogy in Chapter 11, Section 4, and MOOCs, which needs their own chapter (next).
For my personal comments on some of the issues raised in this chapter, please click on the podcast below.
An audio element has been excluded from this version of the text. You can listen to it online here: https://pressbooks.bccampus.ca/teachinginadigitalagev2/?p=143
Activity 4.8 Making choices
Describe your main subject area and level. Then try to answer each of the following questions:
1. What are the main learning outcomes (at a high level) that I need to achieve in this course or program, if the students are to be properly prepared for the future?
2. What teaching method is most likely to enable me to help learners achieve these outcomes?
3. How much would I have to change what I’m doing now, and what would the course or program look like in the future? Could I write a scenario to describe how I would be teaching in the future? Or how students will be learning in my course or program?
4. What support am I likely to get from my institution, in terms of supporting my ideas, supporting change, providing resources such as training in new methods, or professional help such as instructional designers?
5. How will my students react to the changes I’m contemplating? How could I ‘sell’ it to them?
No feedback is provided on this activity; it is for your personal reflection.
Key Takeaways (Chapters 3 and 4)
1. Traditional classroom teaching, and especially transmissive lectures, were designed for another age. Although lectures have served us well, we are now in a different age that requires different methods.
2. The key shift is towards greater emphasis on skills, particularly knowledge management, and less on memorising content. We need teaching methods for teaching and learning that lead to the development of the skills needed in a digital age.
3. There is no one teaching method or ‘best’ design model for all circumstances. The choice of teaching method needs to take account of the context in which it will be applied, but nevertheless, some methods are better than others for developing the knowledge and skills needed in a digital age. For the contexts with which I’m most associated, online collaborative learning, experiential learning and agile design best meet my criteria.
4. Teaching methods in general are not dependent on a particular mode of delivery; they can operate in most cases as well online as in class.
5. In an increasingly volatile, uncertain, complex and ambiguous world, we need methods of teaching that are light and nimble. | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/04%3A_Methods_of_teaching_with_an_online_focus/04.8%3A_Making_decisions_about_teaching_methods.txt |
Figure 4 E Image: Harper Adams University
Mike: Hey, George, come and sit down and tell Allison and Rav about that weird course you’re taking from UBC.
George: Hi, you two. Yeah, it’s a great course, very different from any other I’ve taken.
Rav.: What’s it about?
George: It’s how to go about starting up a technology company.
Allison: But I thought you were doing a masters in education.
George: Yeah, I am. This course is looking at how new technologies can be used in education and how to build a business around one of these technologies.
Mike: Really, George? So what about all your socialist principles, the importance of public education, and all that? Are you giving up and going to become a fat capitalist?
George: No, it’s not like that. What the course is really making me do is think about how we could be using technology better in school or college.
Mike: And how to make a profit out of it, by the sound of it.
Rav.: Shut up, Mike – I’m curious, George, since I’m doing a real business program. You’re going to learn how to set up a business in 13 weeks? Gimme a break.
George: It’s more about becoming an entrepreneur – someone who takes risks and tries something different.
Mike.: With someone else’s money.
George: Do you really want to know about this course, or are you just wanting to give me a hard time?
Allison: Yes, shut up, Mike. Have you chosen a technology yet, George?
George: Almost. We spend most of the course researching and analysing emerging technologies that could have an application in education. We have to find a technology, research it then come up with a plan of how it could be used in education, and how a business could be built around it. But I think the real aim is to get us to think about how technology could improve or change teaching or learning..
Rav.: So what’s the technology you’ve chosen?
George: You’re jumping too far ahead, Rav. We go through two boot camps, one on analysing the edtech marketplace, and one on entrepreneurship: what it takes to be an entrepreneur. Why are you laughing, Mike?
Mike: I just can’t see you in combat uniform, crawling through tubes under gun fire, with a book in your hand.
George: Not that kind of bootcamp. This course is totally online. Our instructor points us in the direction of a few technologies to get us started, but because there’s more stuff coming out all the time, we’re encouraged to make our own choices about what to research. And we all help each other. I must have looked at more than 50 products or services so far, and we all share our analyses. I’m down to possibly three at the moment, but I’m going to have to make my mind up soon, as I have to do a YouTube elevator pitch for my grade.
Rav.: A what?
George: If you look at most of these products, there’s a short YouTube video that pitches the business. I’ve got to make the case for whatever technology I choose in just under eight minutes. That’s going to be 25% of my grade.
Allison: Wow, that’s tough.
George: Well, we all help each other. We have to do a preliminary recording, then everyone pitches in to critique it. Then we have a few days to send in our final version.
Allison: What else do you get grades for?
George: I got 25% of my marks for an assignment that analysed a particular product called Dybuster which is used to help learners with dyslexia. I looked mainly at its educational strengths and weaknesses, and its likely commercial viability. For my second assignment, also worth 25%, we had to build an application of a particular product or service, in my case a module of teaching using a particular product. There were four of us altogether working as a team to do this. Our team designed a short instructional module that showed a chemical reaction, using an off-the-shelf online simulation tool that is free for people to use. I’ll get my last 25% from analysing my own contribution to discussions and activities.
Rav.: What, you give yourself the grade?
George: No, I have to collect my best contributions together in a sort of portfolio, then send them in to the instructor, who then gives the grade based on the quality of the contributions.
Allison: But what I don’t understand is: what’s the curriculum? What text books do you have to read? What do you have to know?
George: Well, there are the two boot camps, but really, we the students, set the curriculum. Our instructor asks us for our first week’s work to look at a range of emerging technologies that might be relevant for education, then we select eight which form the basis of our work groups. I’ve already learned a lot, just by searching and analysing different products over the Internet. We have to think about and justify our decisions. What kind of teaching philosophy do they imply? What criteria am I using when I support or reject a particular product? Is this a sustainable tool? (You don’t want to have to get rid of good teaching material because the company’s gone bust and doesn’t support the technology any more). What I’m really learning though is to think about technology differently. Previously I wasn’t really thinking about teaching differently. I was just trying to find a technology that made my life easier. But this course has woken me up to the real possibilities. I feel I’m in a much better position now to shake up my own school and move them into the digital age.
Allison (sighs): Well, I guess that’s the difference between an undergraduate and a graduate course. You couldn’t do this unless you already knew a lot about education, could you?
George: I’m not so sure about that, Allison. It doesn’t seem to have stopped a lot of entrepreneurs from developing tools for teaching!
Mike: George, I’m sorry. I can’t wait for you to become a rich capitalist – it’s your turn to buy the drinks.
Scenario based on a UBC graduate course for the Master in Educational Technology.
The instructors are David Vogt and David Porter, assisted by Jeff Miller, the instructional designer for the course. | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/04%3A_Methods_of_teaching_with_an_online_focus/04.8%3A_Scenario_E%3A_ETEC_522%3A_Ventures_in_e-Learning.txt |
Figure 5.1.1 Daphne Koller’s TED Talk, 2012
To see this YouTube video, click on the graphic. For a response to this video, see: ‘What’s right and what’s wrong with Coursera-style MOOCs’.
The term MOOC was used for the first time in 2008 for a course offered by the Extension Division of the University of Manitoba in Canada. This non-credit course, Connectivism and Connective Knowledge (CK08) was designed by George Siemens, Stephen Downes and Dave Cormier. It enrolled 27 on-campus students who paid a tuition fee but was also offered online for free. Much to the surprise of the instructors, 2,200 students enrolled in the free online version. Downes classified this course and others like it that followed as connectivist or cMOOCs, because of their design (Downes, 2012).
In the fall of 2011, two computer science professors from Stanford University, Sebastian Thrun and Peter Norvig, launched a MOOC on The Introduction to AI (artificial intelligence) that attracted over 160,000 enrollments, followed quickly by two other MOOCs, also in computer sciences, from Stanford instructors Andrew Ng and Daphne Koller. Thrun went on to found Udacity, and Ng and Koller established Coursera. These are for-profit companies using their own specially developed software that enable massive numbers of registrations and a platform for the teaching. Udacity and Coursera formed partnerships with other leading universities where the universities pay a fee to offer their own MOOCs through these platforms. Udacity in 2013 changed direction to focus on the vocational and corporate training market.
The Massachusetts Institute of Technology (MIT) and Harvard University in March 2012 developed an open source platform for MOOCs called edX, which also acts as a platform for online registration and teaching. edX has also developed partnerships with leading universities to offer MOOCs without direct charge for hosting their courses, although some may pay to become partners in edX. Other platforms for MOOCs, such as the U.K. Open University’s FutureLearn, have also been developed. Because the majority of MOOCs offered through these various platforms are based mainly on video lectures and computer-marked tests, Downes has classified these as xMOOCs, to distinguish them from the more connectivist cMOOCs.
In March, 2019 there were more than 11,000 MOOC courses from 900 universities globally, with just over 100 million registrations (Shah and Pickard, 2019). The big change in 2017-2018 was a move to MOOC-based degrees, with seven universities announcing 15 degrees in 2017, and in 2018, 30 more universities joined in, and launched more than 45 degrees (Johnson, 2019).
In addition to full degrees, EdX and Coursera both offer multiple micro-credentials, each with their own branding. Overall, 630 micro-credentials existed at the end of 2018, but most of the new credentials came from just two credentials, Coursera specialization, and edX professional certificate (Johnson, 2019).
05.2: What is a MOOC
Figure 5.2.1 Making sense of MOOCs © Giulia Forsythe, 2012 and JISC, 2012
5.2.1 MOOCS: a massive disruption?
Probably no development in teaching in recent years has been as controversial as the development of Massive Open Online Courses (MOOCs). In 2013, the writer Thomas Friedman wrote in the New York Times:
...nothing has more potential to enable us to reimagine higher education than the massive open online course ….For relatively little money, the U.S. could rent space in an Egyptian village, install two dozen computers and high-speed satellite Internet access, hire a local teacher as a facilitator, and invite in any Egyptian who wanted to take online courses with the best professors in the world, subtitled in Arabic…I can see a day soon where you’ll create your own college degree by taking the best online courses from the best professors from around the world ….paying only the nominal fee for the certificates of completion. It will change teaching, learning and the pathway to employment.
Many others have referred to MOOCs as a prime example of the kind of disruptive technology that Clayton Christensen (2010) has argued will change the world of education. Others have argued that MOOCs are not a big deal, just a more modern version of educational broadcasting, and do not really affect the basic fundamentals of education, and in particular do not address the type of learning needed in a digital age.
MOOCs can be seen then as either a major revolution in education or just another example of the overblown hyperbole often surrounding technology, particularly in the USA. I shall be arguing that MOOCs are a significant development, but they have severe limitations for developing the knowledge and skills needed in a digital age.
5.2.2 Key characteristics
All MOOCs have some common features, although we shall see that the term MOOC covers an increasingly wide range of designs.
5.2.2.1 Massive
By 2019, Coursera claimed over 35 million sign-ups with its largest course claiming 240,000 participants. The huge numbers (in the hundred of thousands) enrolling in the earliest MOOCs are not always replicated in later MOOCs, but the numbers are still substantial. For instance, in 2013, the University of British Columbia offered several MOOCs through Coursera, with the numbers initially signing up ranging from 25,000 to 190,000 per course (Engle, 2014).
However, even more important than the actual numbers is that in principle MOOCs have infinite scalability. There is technically no limit to their final size, because the marginal cost of adding each extra participant is nil for the institutions offering MOOCs. (In practice this is not quite true, as central technology, backup and bandwidth costs increase, and as we shall see, there can be some knock-on costs for an institution offering MOOCs as numbers increase. However, the cost of each additional participant is so small, given the very large numbers, that it can be more or less ignored). The scalability of MOOCs is probably the characteristic that has attracted the most attention, especially from governments, but it should be noted that this is also a characteristic of broadcast television and radio, so it is not unique to MOOCs.
5.2.2.2 Open
At least for the initial MOOCs, access was free for participants, although an increasing number of MOOCs are charging a fee for assessment leading to a badge or certificate or other fees. For instance, in 2019 Coursera was charging between US\$29-\$99 per course.
There are no pre-requisites for participants other than access to a computer/mobile device and the Internet. However, broadband access is essential for MOOCs that use video streaming, which severely limits their potential for widening access to higher education in the least developed countries.
There is another significant way in which MOOCs through Coursera and some other MOOC platforms are not fully open (see Chapter 11 for more on what constitutes ‘open’ in education). Coursera owns the rights to the materials, so they cannot be repurposed or reused without permission, and the material may be removed from the Coursera site when the course ends. Also, Coursera decides which institutions can host MOOCs on its platform – this is not an open access for institutions. On the other hand, edX is an open source platform, so any institution that joins edX can develop their own MOOCs with their own rules regarding rights to the material. cMOOCs are generally completely open, but since individual participants of cMOOCs create a lot if not all of the material it is not always clear whether they own the rights and how long the MOOC materials will remain available.
Indeed, there are many other kinds of online material that are also open and free over the Internet, such as open textbooks and open educational resources, often in ways that are more accessible for reuse than MOOC material (see Chapter 11)[1].
5.2.2.3 Online
MOOCs are offered at least initially wholly online, but increasingly institutions are negotiating with the rights holders to use MOOC materials in a blended format for use on campus. In other words, the institution provides learner support for the MOOC materials through the use of campus-based instructors. For instance at San Jose State University, on-campus students used MOOC materials from Udacity courses, including lectures, readings and quizzes, and then instructors spent classroom time on small-group activities, projects and quizzes to check progress (Collins, 2013). More variations in the design of MOOCs will be discussed in more detail in Section 5.3.
Again though it should be noted that MOOCs are not unique in offering courses online. In 2017, there were 6.3 million students in the USA alone taking online courses for credit, as part of regular degree programs (Seaman et. al, 2018).
5.2.2.4 Courses
One characteristic that distinguishes MOOCs from most other open educational resources is that they are organized into a whole course. However, what this actually means for participants is not exactly clear. Although many MOOCs offer certificates or badges for successful completion of a course, to date these have not in most cases been accepted for admission to universities or for advanced standing or credit, even (or especially) by the institutions offering the MOOCs.
5.2.3 Summary
It can be seen that all the key characteristics of MOOCs exist in some form or other outside MOOCs. What makes MOOCs unique though is the combination of the four key characteristics, and in particular the fact that they scale massively and are open for participants (although not always free).
References
Christensen, C. (2010) Disrupting Class, Expanded Edition: How Disruptive Innovation Will Change the Way the World Learns New York: McGraw-Hill
Collins, E. (2013) SJSU Plus Augmented Online Learning Environment Pilot Project Report San Jose CA: San Jose State University
Engle, W. (2104) UBC MOOC Pilot: Design and Delivery Vancouver BC: University of British Columbia
Friedman, T. (2013) Revolution Hits the Universities New York Times, January 26
Seaman, J.E., Allen, I.E., and Seaman, J. (2018) Grade Increase: Tracking Distance Education in the United States Wellesley MA: The Babson Survey Research Group
Activity 5.2
1. When is a MOOC not a MOOC? What are the essential characteristics for a course to be a MOOC?
2. Can you find examples of MOOCs from providers within your own state or province? Do they differ in any way from the main MOOC platforms such as Coursera or edX? In what ways?
3. Are they an inferior or low quality form of education? If so, why? What criteria would you use for judging the quality of a MOOC? Write down your answers then check these when you have read the rest of this chapter and see if you have changed your mind.
For my feedback on these questions click the podcast below
An audio element has been excluded from this version of the text. You can listen to it online here: https://pressbooks.bccampus.ca/teachinginadigitalagev2/?p=150 | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/05%3A_MOOCs/05.1%3A_Brief_history.txt |
Figure 5.3 There are many variations of the basic MOOC design
Image: © Dairy Cattle, India, 2014© Dairy Cattle, India, 2014
In this section the main MOOC designs will be analysed. However, MOOCs are still a relatively new phenomenon, and design models are still evolving.
5.3.1 xMOOCs
MOOCs developed initially by Stanford University professors and a little later by MIT and Harvard instructors are based primarily on a strongly behaviourist, information transmission model, the core teaching being through online recorded videos of short lectures, combined with computer automated testing, and sometimes also through the use of peer assessment. These MOOCs are offered through special cloud-based software platforms such as Coursera, edX and FutureLearn.
xMOOCs is a term coined by Stephen Downes (2012) for courses developed by Coursera, Udacity and edX. At the time of writing (2019) xMOOCs are by far the most common MOOC. Instructors have considerable flexibility in the design of the course, so there is considerable variation in the details, but in general xMOOCs have the following common design features:
5.3.1.1 Specially designed platform software
Most very large xMOOCs use specially designed platform software such as Coursera, edX or FutureLearn that allows for the registration of very large numbers of participants, provides facilities for the storing and streaming on demand of digital materials, and automates assessment procedures and student performance tracking. The software platform also allows the companies that provide the software to collect and analyse student data.
However, more and more smaller institutions are offering their own xMOOCs through using or adapting their continuing education online registration process, their own video servers, and ‘off-the-shelf’ automated feedback, testing and marking tools.
5.3.1.2 Video lectures
xMOOCs use the standard lecture mode, delivered online by participants downloading on demand recorded video lectures. These video lectures are normally available on a weekly basis over a period of 10-13 weeks. Initially these were often 50 minute lectures, but as a result of experience some xMOOCs now are using shorter recordings (sometimes down to 15 minutes in length) and thus there may be more video segments. As well, xMOOC courses are becoming shorter in length, some now lasting only five weeks. Various video production methods have been used, including lecture capture (recording face-to-face on-campus lectures, then storing them and streaming them on demand), full studio production, or desk-top recording by the instructor.
5.3.1.3 Computer-marked assignments
Students complete an online test and receive immediate computerised feedback. These tests are usually offered throughout the course, and may be used just for participant feedback. Alternatively the tests may be used for determining the award of a certificate. Another option is for an end of course grade or certificate based solely on an end-of-course online test. Most xMOOC assignments are based on multiple-choice, computer-marked questions, but some MOOCs have also used text or formula boxes for participants to enter answers, such as coding in a computer science course, or mathematical formulae, and in one or two cases, short text answers, but in most cases these will be computer-marked.
5.3.1.4 Peer assessment
Some xMOOCs have experimented with assigning students randomly to small groups for peer assessment, especially for more open-ended or more evaluative assignment questions. This has often proved problematic though because of wide variations in expertise between the different members of a group, and because of the different levels of involvement in the course of different participants.
5.3.1.5 Supporting materials
Sometimes copies of slides, supplementary audio files, urls to other resources, and online articles may be included for downloading by participants.
5.3.1.6 A shared comment/discussion space
These are places where participants can post questions, ask for help, or comment on the content of the course.
5.3.1.7 No, or very light, discussion moderation
The extent to which the discussion or comments are moderated varies probably more than any other feature in xMOOCs, but at its most, moderation is directed at all participants rather than to individuals. Because of the very large numbers participating and commenting, moderation of individual comments by the instructor(s) offering the MOOC is rarely possible. Some instructors offer no moderation whatsoever, so participants rely on other participants to respond to questions or comments. Some instructors ‘sample’ comments and questions, and post comments in response to these. Some instructors use volunteers or paid teaching assistants to comb comments to identify common areas of concern shared by a number of participants then the instructor and/or the teaching assistants will respond. However, in most cases, participants moderate each other’s comments or questions.
5.3.1.8 Badges or certificates
Most xMOOCs award some kind of recognition for successful completion of a course, based on a final computer-marked assessment. However, at the time of writing, MOOC badges or certificates have in most cases not been recognised for credit or admission purposes even by the institutions offering a MOOC – even when the lectures are the same as for on-campus students. Little evidence exists to date about employer acceptance of MOOC qualifications (see for instance, Banks and Meinart, 2016 or Gatuguta-Gitau, 2017). However, with the increasing development of partnerships between major employers and MOOC providers to develop microcredentials, this may change (see for example, Gordon, 2018).
5.3.1.9 Learning analytics
Although to date there has not been a great deal of published information about the use of learning analytics in xMOOCs, the xMOOC platforms have the capacity to collect and analyse ‘big data’ about participants and their performance, enabling, at least in theory, for immediate feedback to instructors about areas where the content or design needs improving and possibly directing automated cues or hints for individuals. For examples of the use of learning analytics in MOOCs, see Laveti et al., 2017 or Eradze and Tammets, 2017.
5.3.1.10 xMOOCs Summary
xMOOCs therefore primarily use a teaching model focused on the transmission of information, with high quality content delivery, computer-marked assessment (mainly for student feedback purposes), and automation of all key transactions between participants and the learning platform. There is rarely any direct interaction between an individual participant and the instructor responsible for the course, although instructors may post general comments in response to a range of participants’ comments. Thus there is a highly behaviouristic/objectivist epistemology underlying xMOOCs.
5.3.2 cMOOCs
cMOOCs, the first of which was developed by three instructors for a course at the University of Manitoba in 2008, are based on network learning, where learning develops through the connections and discussions between participants over social media. There is no standard technology platform for cMOOCs, which use a combination of webcasts, participant blogs, tweets, software that connects blogs and tweets on the same topic via hashtags, and online discussion forums. Although usually there are some experts who initiate and participate in cMOOCs, they are by and large driven by the interests and contributions of the participants. Usually there is no attempt at formal assessment.
5.3.2.1 Key design principles for cMOOCs
Downes (2014) has identified four key design principles for cMOOCs:
• autonomy of the learner: although whoever organises the MOOC will usually choose a main topic and invite participants, there is no formal curriculum; participants decide what to discuss, what to read, and what they wish to contribute towards the topic;
• diversity: in the tools used, the range of participants, their knowledge levels, and the varied content;
• interactivity: in terms of co-operative learning, communication between participants, resulting in ’emergent’ knowledge
• open-ness: in terms of access, content, activities and assessment.
Thus for the proponents of cMOOCs, learning results not from the transmission of information from an expert to novices, as in xMOOCs, but from the sharing and flow of knowledge between participants.
5.3.2.2 From principles to practice
Identifying how these key design features for cMOOCs are turned into practice is somewhat more difficult to pinpoint, because cMOOCs depend on an evolving set of practices. Most cMOOCs to date have in fact made some use of ‘experts’, both in the organization and promotion of the MOOC, and in providing ‘nodes’ of content around which discussion tends to revolve. In other words, the design practices of cMOOCs are still more a work in progress than those of xMOOCs.
Nevertheless, at the moment the following are key design practices in cMOOCs:
• use of social media Partly because most cMOOCs are not institutionally based or supported, they do not at present use a shared platform or platforms but are more loosely supported by a range of openly accessible ‘connected’ tools and media. These may include a simple online registration system, and the use of web conferencing tools such as Blackboard Collaborate or Adobe Connect, streamed video or audio files, blogs, wikis, ‘open’ learning management systems such as Moodle or Canvas, Twitter, LinkedIn or Facebook, all enabling participants to share their contributions. Indeed, as new apps and social media tools develop, they too are likely to be incorporated into cMOOCs. All these tools are connected through web-based hashtags or other web-based linking mechanisms, enabling participants to identify social media contributions from other participants. Thus the use of loosely linked or connected social media is a key design component of cMOOCs;
• participant-driven content In principle, other than a common topic that may be decided by someone wanting to organise a cMOOC, content is decided upon and contributed by the participants themselves. Indeed, there may be no formally identified instructor. In practice though cMOOC organisers (who themselves tend to have some expertise in the topic of the MOOC) are likely to invite potential participants who have expertise or are known already to have a well articulated approach to a topic, to make contributions which form the basis of discussion and debate. Participants choose their own ways to contribute or communicate, the most common being through blog posts, tweets, or comments on other participants’ blog posts, although some cMOOCs use wikis or open source online discussion forums. The key design practice with regard to content is that all participants contribute to and share content;
• distributed communication This is probably the most difficult design practice to understand for those not familiar with cMOOCs – and even for those who have participated. With participants numbering in the hundreds or even thousands, each contributing individually through a variety of social media, there are a myriad different inter-connections between participants that are impossible to track (in total) by any single participant. This results in many sub-conversations, more commonly at a binary level of two people communicating with each other than an integrated group discussion, although all conversations are ‘open’ and all other participants are able to contribute to a conversation if they know it exists. The key design practice then with regard to communication is a self-organising network with many sub-components;
• assessment There is no formal assessment, although participants may seek feedback from other, more knowledgeable participants, on an informal basis. Basically participants decide for themselves whether what they have learned is appropriate to them.
5.3.2.3 cMOOCs summary
cMOOCs therefore primarily use a networked approach to learning based on autonomous learners connecting with each other across open and connected social media and sharing knowledge through their own personal contributions. There is no pre-set curriculum and no formal teacher-student relationship, either for delivery of content or for learner support. Participants learn from the contributions of others, from the meta-level knowledge generated through the community, and from self-reflection on their own contributions, thus reflecting many of the features of communities of interest or practice.
cMOOCs have a very different educational philosophy from xMOOCs. Downes and Siemens have argued that cMOOCs reflect a new theory of learning, ‘connectivism’, based on exploiting online social networks (see Chapter 2.6). cMOOCs certainly reflect a constructivist epistemology.
5.3.3 Other variations of MOOCs
I have deliberately focused on the differences in design between xMOOCs and cMOOCs, and Mackness (2103) and Yousef et al. (2014) also emphasise similar differences in philosophy/theory between cMOOCs and xMOOCs, as well as Downes himself (2012), one of the original designers of cMOOCs.
However, it should be noted that the design of MOOCs continues to evolve, with all kinds of variations. Pilli and Admiraal (2016) have identified 27 types of MOOC, including:
• cMOOCs;
• xMOOCs;
• BOOCs (a big open online course) – a cross between an xMOOC and a cMOOC;
• COOCs (community open online courses) – small-scale, non-profit courses that corporations open online to provide courses for customers and/or employees
• DOCCs (distributed open collaborative course): this involves 17 universities sharing and adapting the same basic MOOC;
• LOOC s(little open online course): as well as 15-20 tuition-paying campus-based students, such courses also allow a limited number of non-registered students to also take the course, but also paying a fee;
• MOORs (massive open online research): a mix of video-based lecturers and student research projects guided by the instructors;
• SPOCs (small, private, online courses): the example given is from Harvard Law School, which pre-selected 500 students from over 4,000 applicants, who take the same video-delivered lectures as on-campus students enrolled at Harvard;
The MOOCs developed by the University of British Columbia and a number of other institutions use volunteers, paid academic assistants or even the instructor to moderate the online discussions and participant comments, making such MOOCs closer in design to regular for-credit online courses – except that they are open to anyone.
5.3.4 What’s going on here?
It is not surprising that over time, the design of MOOCs is evolving. There seem to be three distinct kinds of development:
• some of the newer MOOCs, especially those from institutions with a history of credit-based online learning prior to the introduction of MOOCs, are beginning to apply some of the best practices, such as organised and moderated discussion groups, from online credit courses to MOOCs (see Chapter 4, Section 4);
• others are trying to open up their regular campus classes also, simultaneously, to non-registered students (which in fact is how the first MOOC, from Cormier, Downes and Siemens, originated);
• yet others are trying to blend online MOOC materials or content with their on-campus teaching.
It is likely that innovation in MOOC design and the way MOOCs are used will continue. In particular, different kinds of MOOC come and go. Finding extant examples of some of the types of MOOC listed in Section 5.3.3 has been difficult in revising this chapter.
However, some of these developments also indicate a good deal of confusion around the definition and goals of MOOCs, especially regarding massiveness and open-ness. If participants from outside a university have to pay a hefty fee to participate in an otherwise ‘closed’, on-campus course, or if off-campus participants have to be selected on certain criteria before they can participate, is it really open? Is the term MOOC now being used to describe any unconventional online offering or any online continuing education course? It’s difficult to see how a SPOC for instance differs from a typical online continuing education course, except perhaps in that it uses a recorded lecture rather than a learning management system. There is a danger of having any online course ending up being described as a MOOC, when in fact there are major differences in design and philosophy.
Although each of these individual innovations, often the result of the initiative of an individual instructor, are to be welcomed in principle, the consequences need to be carefully considered in fairness to potential participants. Individual instructors designing MOOCs really need to make sure that the design is consistent in terms of educational philosophy, and be clear as to why they are opting for a MOOC rather than a conventional online course. This is particularly important if there is to be any form of formal assessment. The status of such an assessment for participants who are not formally admitted to or registered as a student in an institution needs to be clear and consistent.
There is even more confusion about mixing MOOCs with on-campus teaching. At the moment the strategy appears to be to first develop a MOOC then see how it can be adapted for on-campus teaching. However, a better strategy might be to develop a conventional, for-credit online course, in terms of design, then see how it could be scaled for open access to other participants. Another strategy might be to use open social media, such as a course wiki and student blogs, to widen access to the teaching of a formal course, rather than develop a full-blown MOOC.
Thinking through the policy implications of incorporating MOOCs or MOOC materials with on-campus teaching does not appear to be happening at the moment in most institutions experimenting with ‘blended’ MOOCs. If MOOC participants are taking exactly the same course and assessment as registered on-campus for-credit students, will the institution award the external MOOC participants who successfully complete the assessment credit for it and/or admit them to the institution? If not, why not? For an excellent discussion of these issues framed for an institution’s Board of Governors, see Green, 2013.
Thus some of these MOOC developments seem to be operating in a policy vacuum regarding open learning in general. At some point, institutions will need to develop a clearer, more consistent strategy for open learning, in terms of how it can best be provided, how it calibrates with formal learning, and how open learning can be accommodated within the fiscal constraints of the institution, and then where MOOCs, other OERs and conventional for-credit online courses might fit with the strategy. For more on this topic, see Chapter 11.
References
Banks, C. and Meinert, E. (2016) The acceptability of MOOC certificates in the workplace International Conference eLearning 2016
Downes, S. (2012) Massively Open Online Courses are here to stay, Stephen’s Web, July 20
Downes, S. (2014) The MOOC of One, Valencia, Spain, March 10
Eradze M., Tammets K. (2017) Learning Analytics in MOOCs: EMMA Case. In: Lauro N., Amaturo E., Grassia M., Aragona B., Marino M. (eds) Data Science and Social Research: Studies in Classification, Data Analysis, and Knowledge Organization Springer: Cham
Gatuguta-Gitau, S. (2017) MOOCs: Employers View, a brief snapshotQS, London, UK
Gordon, A. (2018) ‘Micromasters surge as MOOCs go from education to qualification‘, Forbes, 13 February
Green, K. (2013) Mission, money and MOOCs Association of Governing Boards Trusteeship, No. 1, Volume 21
Laveti, R. et al. (2017) Implementation of learning analytics framework for MOOCs using state-of-the-art in-memory computing, IEEE Xplore, 19 October
Mackness, J. (2013) cMOOCs and xMOOCs – key differences, Jenny Mackness, October 22
Pilli, O. amd Admiraal, W. (2016) A Taxonomy of Massive Open Online Courses Contemporary Educational Technology, Vol. 7, No. 3, pp. 223-240
Yousef, A. et al. (2014) MOOCs: A Review of the State-of-the-Art Proceedings of 6th International Conference on Computer Supported Education – CSEDU 2014, Barcelona, Spain, pp. 9-20
Activity 5.3: Thinking about MOOC design
1. When is a MOOC a MOOC and when is it not a MOOC? Can you identify the common features? Is MOOC still a useful term?
2. If you were to design a MOOC, who would be the target audience? What kind of MOOC would it be? What form of assessment could you use? What would make you think your MOOC was a success, after it was delivered? What criteria would you use?
3. Could you think of other ways to make one or more of your courses more open, other than creating a MOOC from scratch? What would be the advantages and disadvantages of these other methods, compared to a MOOC?
For my comments on these questions click on the podcast below:
An audio element has been excluded from this version of the text. You can listen to it online here: https://pressbooks.bccampus.ca/teachinginadigitalagev2/?p=154 | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/05%3A_MOOCs/05.3%3A_A_Taxonomy_of_MOOCs.txt |
Figure 5.4.1 MOOC users tend to be male, well-educated, with about 40-60% from other countries. Image: Depositphotos, 2019
In-depth analysis by standard academic criteria shows that MOOCs have more academic rigor and are a far more effective teaching methodology than in-house teaching
Benton R. Groves, Ph.D. student
My big concern with xMOOCs is their limitation, as currently designed, for developing the higher order intellectual skills needed in a digital world.
Tony Bates
5.4.1 The research on MOOCs
At the time of writing (2019), MOOCs are still less than ten years old, whereas online courses for credit have been around for more than 20 years. The latter have been subject to much more independent research, although this prior research was largely ignored in the design of the early MOOCs. A lot of the research to date on MOOCs comes from the institutions offering MOOCs, mainly in the form of reports on enrolments, or self-evaluation by instructors. The commercial platform providers such as Coursera and Udacity have provided limited research information overall, which is a pity, because they have access to really big data sets. However, MIT and Harvard, the founding partners in edX, are conducting some research, mainly on their own courses.
In this chapter, I have drawn on available evidence-based research that provides insight into the strengths and weaknesses of MOOCs. At the same time, we should be clear that we are discussing a phenomenon that to date has been marked largely by political, emotional and often irrational discourse, rather than something based on evidence-based research.
Lastly, it should be remembered in this evaluation I am applying the criteria of whether MOOCs are likely to lead to the kinds of learning needed in a digital age: in other words, do they help develop the knowledge and skills defined in Chapter 1?
5.4.2 Open and free education
5.4.2.1 The ‘open-ness’ of MOOCs
MOOCs, particularly xMOOCs, deliver high quality content from some of the world’s best universities to anyone with a computer and an Internet connection. This in itself is an amazing value proposition. In this sense, MOOCs are an incredibly valuable addition to education. Who could argue against this?
However, MOOCs are not the only form of open and free education. Libraries, open textbooks and educational broadcasting are also open and free and have been for some time. There are also lessons we can learn from these earlier forms of open and free education that also apply to MOOCs.
Furthermore, MOOCs are not always open as in the sense of open educational resources. Coursera and Udacity for instance offer limited access to their material for re-use without permission. On other more open platforms, such as edX, individual faculty or institutions may restrict re-use of material. Lastly, many MOOCs exist for only one or two years then disappear, which limits their use as open educational resources for re-use in other courses or programs.
5.4.2.2 A replacement for conventional education?
It is worth noting that these earlier forms of open and free education did not replace the need for formal, credit-based education, but were used to supplement or strengthen it. In other words, MOOCs are a tool for continuing and informal education, which has high value in its own right. As we shall see, though, MOOCs work best when people are already reasonably well educated. There is no reason to believe then that because MOOCs are open and free to end-users, they will inevitably force down the cost of conventional higher education, or eliminate the need for it altogether.
5.4.2.3 The answer for education in developing countries?
There have been many attempts to use educational broadcasting and satellite broadcasting in developing countries to open up education for the masses (see Bates, 1984), and they all substantially failed to increase access or reduce cost for a variety of reasons, the most important being:
• the high cost of ground equipment (including security from theft or damage);
• the need for local face-to-face support for learners without high levels of education;
• the need to adapt content to the culture and needs of the receiving countries;
• the difficulty of covering the operational costs of management and administration, especially for assessment, qualifications and local accreditation.
Also the priority in most developing countries is not for university courses from high-level Stanford University professors, but for low cost, good quality high school education.
Although mobile phones and to a lesser extent tablets are widespread in Africa, they are relatively expensive to use. For instance, it costs US\$2 to download a typical YouTube video – equivalent to a day’s salary for many Africans. Streamed 50 minute video lectures then have limited applicability.
Lastly, it is frankly immoral to allow people in developing countries to believe that successful completion of MOOCs will lead to a recognised degree or to university entrance in the USA or in any other economically advanced country, at least under present circumstances.
This is not to say that MOOCs could not be valuable in developing countries, but this will mean:
• being realistic as to what they can actually deliver;
• working in partnership with educational institutions and systems and other partners in developing countries;
• ensuring that the necessary local support – which costs real money – is put in place;
• adapting the design, content and delivery of MOOCs to the cultural and economic requirements of those countries.
Finally, although MOOCs are in the main free for participants, they are not without substantial cost to MOOC providers, an issue that will be discussed in more detail in Section 5.4.8.
5.4.3 The audience that MOOCs mainly serve
In a research report from Ho et al. (2014), researchers at Harvard University and MIT found that on the first 17 MOOCs offered through edX,
• 66 per cent of all participants, and 74 per cent of all who obtained a certificate, have a bachelor’s degree or above,
• 71 per cent were male, and the average age was 26.
• this and other studies also found that a high proportion of participants came from outside the USA, ranging from 40-60 per cent of all participants, indicating strong interest internationally in open access to high quality university teaching.
In a study based on over 80 interviews in 62 institutions ‘active in the MOOC space’, Hollands and Tirthali (2014), researchers at Columbia University Teachers’ College, found that:
Data from MOOC platforms indicate that MOOCs are providing educational opportunities to millions of individuals across the world. However, most MOOC participants are already well-educated and employed, and only a small fraction of them fully engages with the courses. Overall, the evidence suggests that MOOCs are currently falling far short of “democratizing” education and may, for now, be doing more to increase gaps in access to education than to diminish them.
Thus MOOCs, as is common with most forms of university continuing education, cater to the better educated, older and employed sectors of society.
5.4.4 Persistence and commitment: the onion hypothesis
The edX researchers (Ho et al., 2014) identified different levels of commitment as follows across 17 edX MOOCs:
• only registered: registrants who never access the courseware (35 per cent);
• only viewed: non-certified registrants who access the courseware, accessing less than half of the available chapters (56 per cent);
• only explored: non-certified registrants who access more than half of the available chapters in the courseware, but did not get a certificate (4 per cent);
• certified: registrants who earn a certificate in the course (5 per cent).
Hill (2013) has identified five types of participants in Coursera courses:
Figure 5.4.2 Image: Phil Hill, 2013
Engle (2014) found similar patterns (also replicated in other studies) for the University of British Columbia MOOCs on Coursera :
• of those that initially sign up, between one third and a half do not participate in any other active way;
• of those that participate in at least one activity, between 5-10 per cent go on to successfully complete a certificate.
Those going on to achieve certificates usually are within the 5-10 per cent range of those that sign up and in the 10-20 per cent range for those who actively engaged with the MOOC at least once. Nevertheless, the numbers obtaining certificates are still large in absolute terms: over 43,000 across 17 courses on edX and 8,000 across four courses at UBC (between 2,000-2,500 certificates per course).
Milligan et al. (2013) found a similar pattern of commitment in cMOOCs, from interviewing a small sample of participants (29 out of 2,300 registrants) about halfway through a cMOOC:
• passive participants: in Milligan’s study these were those that felt lost in the MOOC and rarely but occasionally logged in;
• lurkers: they were actively following the course but did not engage in any of the activities (just under half those interviewed);
• active participants (again, just under half those interviewed) who were fully engaged in the course activities.
MOOCs need to be judged for what they are, a somewhat unique – and valuable – form of non-formal education. These results are very similar to research into non-formal educational broadcasts (e.g. the History Channel). One would not expect a viewer to watch every episode of a History Channel series then take an exam at the end. Ho et al. (p.13) produced the following diagram to show the different levels of commitment to xMOOCs:
Figure 5.4.3 Level of participation in MOOCs © Ho et al., 2014
This is remarkably similar to what I wrote in 1984 about the onion hypothesis of educational broadcasting in Britain (Bates, 1984):
(p.99): At the centre of the onion is a small core of fully committed students who work through the whole course, and, where available, take an end-of-course assessment or examination. Around the small core will be a rather larger layer of students who do not take any examination but do enrol with a local class or correspondence school. There may be an even larger layer of students who, as well as watching and listening, also buy the accompanying textbook, but who do not enrol in any courses. Then, by far the largest group, are those that just watch or listen to the programmes. Even within this last group, there will be considerable variations, from those who watch or listen fairly regularly, to those, again a much larger number, who watch or listen to just one programme.
I also wrote (p.100):
A sceptic may say that the only ones who can be said to have learned effectively are the tiny minority that worked right through the course and successfully took the final assessment…A counter argument would be that broadcasting can be considered successful if it merely attracts viewers or listeners who might otherwise have shown no interest in the topic; it is the numbers exposed to the material that matter…the key issue then is whether broadcasting does attract to education those who would not otherwise have been interested, or merely provides yet another opportunity for those who are already well educated…There is a good deal of evidence that it is still the better educated in Britain and Europe that make the most use of non-formal educational broadcasting.
Exactly the same could be said about MOOCs. In a digital age where easy and open access to new knowledge is critical for those working in knowledge-based industries, MOOCs will be one valuable source or means of accessing that knowledge. The issue is though whether there are more effective ways to do this. Thus MOOCs can be considered a useful – but not really revolutionary – contribution to non-formal continuing education.
5.4.5 What do students learn in MOOCs?
This is a much more difficult question to answer, because so little of the research to date (2019) has tried to answer this question. (One reason, as we shall see in the next section, is that assessment of learning in MOOCs remains a major challenge). There are at least two kinds of study: quantitative studies that seek to quantify learning gains; and qualitative studies that describe the experience of learners within MOOCs, which indirectly provide some insight into what they have learned.
5.4.5.1 Conceptual learning
At the time of writing, the most quantitative study of learning in MOOCs has been by Colvin et al.(2014), who investigated ‘conceptual learning’ in an MIT Introductory Physics MOOC. Colvin and colleagues compared learner performance not only between different sub-categories of learners within the MOOC, such as those with no physics or math background with those such as physic teachers who had considerable prior knowledge, but also with on-campus students taking the same curriculum in a traditional campus teaching format. In essence, the study found no significant differences in learning gains between or within the two types of teaching, but it should be noted that the on-campus students were students who had failed an earlier version of the course and were retaking it.
This research is a classic example of the no significant difference in comparative studies in educational technology; other variables, such as differences in the types of students, were as important as the mode of delivery (for more on the ‘no significant difference’ phenomenon in media comparisons, see Chapter 10, Section 2.2). Also, this MOOC design represents a behaviourist-cognitivist approach to learning that places heavy emphasis on correct answers to conceptual questions. It doesn’t attempt to develop the skills needed in a digital age as identified in Chapter 1.
5.4.5.2 The student experience
There have been far more studies of the experience of learners within MOOCs, particularly focusing on the discussions within MOOCs (see for instance, Kop, 2011). In general (although there are exceptions), discussions are unmonitored, and it is left to participants to make connections and respond to other students comments.
However, there are some strong criticisms of the effectiveness of the discussion element of MOOCs for developing the high-level conceptual analysis required for academic learning. There is evidence from studies of credit-based online learning that to develop deep, conceptual learning, there is a need in most cases for intervention by a subject expert to clarify misunderstandings or misconceptions, to provide accurate feedback, to ensure that the criteria for academic learning, such as use of evidence, clarity of argument, and so on, are being met, and to ensure the necessary input and guidance to seek deeper understanding (see in particular Harasim, 2017).
Furthermore, the more massive the course, the more likely participants are to feel ‘overload, anxiety and a sense of loss’, if there is not some instructor intervention or structure imposed (Knox, 2014). Firmin et al. (2014) have shown that when there is some form of instructor ‘encouragement and support of student effort and engagement’, results improve for all participants in MOOCs. Without a structured role for subject experts, participants are faced with a wide variety of quality in terms of comments and feedback from other participants. There is again a great deal of research on the conditions necessary for the successful conduct of collaborative and co-operative group learning (see for instance, Lave and Wenger, 1991, or Barkley, Major and Cross, 2014), and these findings certainly have not been generally applied to the management of MOOC discussions.
5.4.5.3 Networked and collaborative learning
One counter argument is that cMOOCs in particular develop a new form of learning based on networking and collaboration that is essentially different from academic learning, and cMOOCs are thus more appropriate to the needs of learners in a digital age. Adult participants in particular, it is claimed by Downes and Siemens, have the ability to self-manage the development of high level conceptual learning. cMOOCs are ‘demand’ driven, meeting the interests of individual students who seek out others with similar interests and the necessary expertise to support them in their learning, and for many this interest may well not include the need for deep, conceptual learning but more likely the appropriate applications of prior knowledge in new or specific contexts. All MOOCs do appear to work best for those who already have a high level of education and therefore bring many of the conceptual skills developed in formal education with them when they join a MOOC, and therefore contribute to helping those who come without such prior knowledge or skills.
5.4.5.4 The need for learner support
Over time, as more experience is gained, MOOCs are likely to incorporate and adapt some of the findings from research on smaller group work to the much larger numbers in MOOCs. For instance, some MOOCs are using ‘volunteer’ or community tutors. The US State Department organized MOOC camps through US missions and consulates abroad to mentor MOOC participants. The camps included Fulbright scholars and embassy staff who lead discussions on content and topics for MOOC participants in countries abroad (Haynie, 2014).
Some MOOC providers, such as the University of British Columbia, paid a small cohort of academic assistants to monitor and contribute to the MOOC discussion forums (Engle, 2014). Engle reported that the use of academic assistants, as well as limited but effective interventions from the instructors themselves, made the UBC MOOCs more interactive and engaging.
However, paying for people to monitor and support MOOCs will of course increase the cost to providers. Consequently, MOOCs are likely to develop new automated ways to manage discussion effectively in very large groups. For instance, the University of Edinburgh experimented with an automated ‘teacherbot’ that crawled through student and instructor Twitter posts associated with a MOOC and directed predetermined comments to students to prompt discussion and reflection (Bayne, 2015). These results and approaches are consistent with prior research on the importance of instructor presence for successful online learning in credit-based courses (see Chapter 4.4.3).
In the meantime, though, there is much work still to be done if MOOCs are to provide the support and structure needed to ensure deep, conceptual learning where this does not already exist in students. The development of the skills needed in a digital age is likely to be an even greater challenge when dealing with massive numbers. However, we need much more research into what participants actually learn in MOOCs and under what conditions before any firm conclusions can be drawn.
5.4.6 Assessment
Assessment of the massive numbers of participants in MOOCs has proved to be a major challenge. It is a complex topic that can be dealt with only briefly here. However, Chapter 6, Section 8 provides a general analysis of different types of assessment, and Suen (2014) provides a comprehensive and balanced overview of the way assessment has been used in MOOCs to date. This section draws heavily on Suen’s paper.
5.4.6.1 Computer marked assignments
Assessment to date in MOOCs has been primarily of two kinds. The first is based on quantitative multiple-choice tests, or response boxes where formulae or ‘correct code’ can be entered and automatically checked. Usually participants are given immediate automated feedback on their answers, ranging from simple right or wrong answers to more complex responses depending on the type of response checked, but in all cases, the process is usually fully automated.
For straight testing of facts, principles, formulae, equations and other forms of conceptual learning where there are clear, correct answers, this works well. In fact, multiple choice computer marked assignments were used by the UK Open University as long ago as the 1970s, although the means to give immediate online feedback were not available then. However, this method of assessment is limited for testing deep or ‘transformative’ learning, and particularly weak for assessing the intellectual skills needed in a digital age, such as creative or original thinking.
5.4.6.2 Peer assessment
Another type of assessment that has been tried in MOOCs has been peer assessment, where participants assess each other’s work. Peer assessment is not new. It has been successfully used for formative assessment in traditional classrooms and in some online teaching for credit (Falchikov and Goldfinch, 2000; van Zundert et al., 2010). More importantly, peer assessment is seen as a powerful way to improve deep understanding and knowledge through the process of students evaluating the work of others, and at the same time, it can be useful for developing some of the skills needed in a digital age, such as critical thinking, for those participants assessing other participants.
However, a key feature of the successful use of peer assessment has been the close involvement of an instructor or teacher, in providing benchmarks, rubrics or criteria for assessment, and for monitoring and adjusting peer assessments to ensure consistency and a match with the benchmarks set by the instructor. Although an instructor can provide the benchmarks and rubrics in MOOCs, close monitoring of the multiple peer assessments is difficult if not impossible with the very large numbers of participants. As a result, MOOC participants often become incensed at being randomly assessed by other participants who may not and often do not have the knowledge or ability to give a ‘fair’ or accurate assessment of another participant’s work.
Various attempts to get round the limitations of peer assessment in MOOCs have been tried such as calibrated peer reviews, based on averaging all the peer ratings, and Bayesian post hoc stabilization (Piech at al. 2013), but although these statistical techniques reduce the error (or spread) of peer review somewhat they still do not remove the problems of systematic errors of judgement in raters due to misconceptions. This is particularly a problem where a majority of participants fail to understand key concepts in a MOOC, in which case peer assessment becomes the blind leading the blind.
5.4.6.3 Automated essay scoring
This is another area where there have been attempts to automate scoring (Balfour, 2013). Although such methods are increasingly sophisticated they are currently limited in terms of accurate assessment to measuring primarily technical writing skills, such as grammar, spelling and sentence construction. Once again they do not measure accurately longer essays where higher level intellectual skills are demanded.
5.4.6.4 Badges, certificates and microcredentials
Particularly in xMOOCs, participants may be awarded a certificate or a ‘badge’ for successful completion of the MOOC, based on a final test (usually computer-marked) which measures the level of learning in a course. However, most of the institutions offering MOOCs will not accept their own certificates for admission or credit within their own, campus-based programs. Probably nothing says more about the confidence in the quality of the assessment than this failure of MOOC providers to recognize their own teaching.
MOOC-based microcredentials are a more recent development.A microcredential is any one of a number of new certifications that covers more than a single course but is less than a full degree. Pickard (2018) provides an analysis of more than 450 MOOC-based microcredentials. Pickard states:
Microcredentials can be seen as part of a trend toward modularity and stackability in higher education, the idea being that each little piece of an education can be consumed on its own or can be aggregated with other pieces up to something larger. Each course is made of units, each unit is made of lessons; courses can stack up to Specializations or XSeries; these can stack up to partial degrees such as MicroMasters, or all the way up to full degrees (though only some microcredentials are structured as pieces of degrees).
However, in her analysis, Pickard found that in the micro-credentials offered through the main MOOC platforms, such as Coursera, edX, Udacity and FutureLearn.;
• student fees range from US\$250 to US\$17,000;
• some microcredentials, though not all, offer some opportunity to earn credit towards a degree program. Typically, university credit is awarded if and only if a student goes on to enroll in the particular degree program connected with the microcredential;
• they are not accredited, recognized, or evaluated by third party organizations (except insofar as they pertain to university degree programs). This variability and lack of standardization poses a problem for both learners and employers, as it makes it difficult to compare the various microcredentials;
• with so much variability, how would a prospective learner choose among the various options? Furthermore, without a detailed understanding of these options, how would an employer interpret or compare these microcredentials when they come up on a resume?
Nevertheless, in a digital age, both workers and employers will increasingly look for ways to ‘accredit’ smaller units of learning than a degree, but in ways that they can be stacked towards eventually a full degree. The issue is whether tying this to the MOOC movement is the best way to go.
Surely a better way would be to develop microcredentials as part of or in parallel with a regular online masters program. For instance as early as 2003, the University of British Columbia in its online Master of Educational Technology was allowing students to take single courses at a time, or the five foundation courses for a post-graduate certificate, or add four more courses and a project to the certificate for a full Master’s degree. Such microcredentials would not be MOOCs, unless (a) they are open to anyone and (b) they are free or at such a low cost anyone can take them. Then the issue becomes whether the institution will accept such MOOC-like credentials as part of a full degree. If not, employers are unlikely to recognise such microcredentials, because they will not know what they are worth.
5.4.6.5 The intent behind assessment
To evaluate assessment in MOOCs requires an examination of the intent behind assessment. There are many different purposes behind assessment (see Chapter 6, Section 8). Peer assessment and immediate feedback on computer-marked tests can be extremely valuable for formative assessment or feedback, enabling participants to see what they have understood and to help develop further their understanding of key concepts. In cMOOCs, as Suen points out, learning is measured as the communication that takes place between MOOC participants, resulting in crowdsourced validation of knowledge – it’s what the sum of all the participants come to believe to be true as a result of participating in the MOOC, so formal assessment is unnecessary. However, what is learned in this way is not necessarily academically validated knowledge, which to be fair, is not the concern of cMOOC proponents.
Academic assessment is a form of currency, related not only to measuring student achievement but also affecting student mobility (for example, entrance to graduate school) and perhaps more importantly employment opportunities and promotion. From a learner’s perspective, the validity of the currency – the recognition and transferability of the qualification – is essential. To date, MOOCs have been unable to demonstrate that they are able to assess accurately the learning achievements of participants beyond comprehension and knowledge of ideas, principles and processes (recognizing that there is some value in this alone). What MOOCs have not been able to demonstrate is that they can either develop or assess deep understanding or the intellectual skills required in a digital age. Indeed, this may not be possible within the constraints of massiveness, which is their major distinguishing feature from other forms of online learning.
5.4.7 Branding
Hollands and Tirthali (2014) in their survey on institutional expectations for MOOCs, found that building and maintaining brand was the second most important reason for institutions launching MOOCs (the most important was extending reach, which can also be seen as partly a branding exercise). Institutional branding through the use of MOOCs has been helped by elite Ivy League universities such as Stanford, MIT and Harvard leading the charge, and by Coursera limiting access to its platform to only ‘top tier’ universities. This of course has led to a bandwagon effect, especially since many of the universities launching MOOCs had previously disdained to move into credit-based online learning. MOOCs provided a way for these elite institutions to jump to the head of the queue in terms of status as ‘innovators’ of online learning, even though they arrived late to the party.
It obviously makes sense for institutions to use MOOCs to bring their areas of specialist expertise to a much wider public, such as the University of Alberta offering a MOOC on dinosaurs, MIT on electronics, and Harvard on Ancient Greek Heroes. MOOCs certainly help to widen knowledge of the quality of an individual professor (who is usually delighted to reach more students in one MOOC than in a lifetime of on-campus teaching). MOOCs are also a good way to give a glimpse of the quality of courses and programs offered by an institution.
However, it is difficult to measure the real impact of MOOCs on branding. As Hollands and Tirthali put it:
While many institutions have received significant media attention as a result of their MOOC activities, isolating and measuring impact of any new initiative on brand is a difficult exercise. Most institutions are only just beginning to think about how to capture and quantify branding-related benefits.
In particular, these elite institutions do not need MOOCs to boost the number of applicants for their campus-based programs (none to date is willing to accept successful completion of a MOOC for admission to credit programs), since elite institutions have no difficulty in attracting already highly qualified students.
Furthermore, once every other institution starts offering MOOCs, the branding effect gets lost to some extent. Indeed, exposing poor quality teaching or course planning to many thousands can have a negative impact on an institution’s brand, as Georgia Institute of Technology found when one of its MOOCs crashed and burned (Jaschik, 2013). However, by and large, most MOOCs succeed in the sense of bringing an institution’s reputation in terms of knowledge and expertise to many more people than it would through any other form of teaching or publicity.
5.4.8 Costs and economies of scale
Figure 5.4.8 The MOOC value proposition is that MOOCs can eliminate the variable costs of course delivery. Image: © OpenTuition.com, 2014
One main strength claimed for MOOCs is that they are free to participants. Once again this is more true in principle than in practice, because MOOC providers may charge a range of fees, especially for assessment. Furthermore, although MOOCs may be free for participants, they are not without substantial cost to the provider institutions. Also, there are large differences in the costs of xMOOCs and cMOOCs, the latter being generally much cheaper to develop, although there are still some opportunity or actual costs even for cMOOCs.
5.4.8.1 The cost of MOOC production and delivery
There is still very little information to date on the actual costs of designing and delivering a MOOC as there are not enough published studies to draw firm conclusions about the costs of MOOCs. However we do have some data. The University of Ottawa (2013) estimated the cost of developing an xMOOC, based on figures provided to the university by Coursera, and on their own knowledge of the cost of developing online courses for credit, at around \$100,000.
Engle (2014) has reported on the actual cost of five MOOCs from the University of British Columbia. There are two important features concerning the UBC MOOCs that do not necessarily apply to other MOOCs. First, the UBC MOOCs used a wide variety of video production methods, from full studio production to desktop recording, so development costs varied considerably, depending on the sophistication of the video production technique. Second, the UBC MOOCs made extensive use of paid academic assistants, who monitored discussions and adapted or changed course materials as a result of student feedback, so there were substantial delivery costs as well.
Appendix B of the UBC report gives a pilot total of \$217,657, but this excludes academic assistance or, perhaps the most significant cost, instructor time. Academic assistance came to 25 per cent of the overall cost in the first year (excluding the cost of faculty). Working from the video production costs (\$95,350) and the proportion of costs (44 per cent) devoted to video production in Figure 1 in the report, I estimate the direct cost at \$216,700, or approximately \$54,000 per MOOC, excluding faculty time and co-ordination support (that is, excluding program administration and overheads), but including academic assistance. However, the range of cost is almost as important. The video production costs for the MOOC which used intensive studio production were more than six times the video production costs of one of the other MOOCs.
5.4.8.2 The comparative costs of credit-based online courses
The main cost factors or variables in credit-based online and distance learning are relatively well understood, from previous research by Rumble (2001) and Hülsmann (2003). Using a similar costing methodology, I tracked and analysed the cost of an online master’s program at the University of British Columbia over a seven year period (Bates and Sangrà, 2011). This program used mainly a learning management system as the core technology, with instructors both developing the course and providing online learner support and assessment, assisted where necessary by extra adjunct faculty for handling larger class enrolments.
I found in my analysis of the costs of the UBC program that in 2003, development costs were approximately \$20,000 to \$25,000 per course. However, over a seven year period, course development constituted less than 15 per cent of the total cost, and occurred mainly in the first year or so of the program. Delivery costs, which included providing online learner support and student assessment, constituted more than a third of the total cost, and of course continued each year the course was offered. Thus in credit-based online learning, delivery costs tend to be more than double the development costs over the life of a program.
The main difference then between MOOCs, credit-based online teaching, and campus-based teaching is that in principle MOOCs eliminate all delivery costs, because MOOCs do not provide learner support or instructor-delivered assessment, although again in practice this is not always true.
5.4.8.3 Opportunity costs
There is also clearly a large opportunity cost involved in offering xMOOCs. By definition, the most highly valued faculty are involved in offering MOOCs. In a large research university, such faculty are likely to have, at a maximum, a teaching load of four to six courses a year. Although most instructors volunteer to do MOOCs, their time is limited. Either it means dropping one credit course for at least one semester, equivalent to 25 per cent or more of their teaching load, or xMOOC development and delivery replaces time spent doing research. Furthermore, unlike credit-based courses, which run from anywhere between five to seven years, MOOCs are often offered only once or twice.
5.4.8.4 Comparing the cost of MOOCs with online credit courses
However one looks at it, the cost of xMOOC development, without including the time of the MOOC instructor, tends to be almost double the cost of developing an online credit course using a learning management system, because of the use of video in MOOCs. If the cost of the instructor is included, xMOOC production costs come closer to three times that of a similar length online credit course, especially given the extra time faculty tend put in for such a public demonstration of their teaching in a MOOC. xMOOCs could (and some do) use cheaper production methods, such as an LMS instead of video, for content delivery, or using and re-editing video recordings of classroom lectures via lecture capture.
Without learner support or academic assistance, though, delivery costs for MOOCs are zero, and this is where the huge potential for savings exist. If the cost per participant is calculated the MOOC unit costs are very low, combining both production and delivery costs. Even if the cost per student successfully obtaining an end of course certificate is calculated it will be many times lower than the cost of an online or campus-based successful student. If we take a MOOC costing roughly \$100,000 to develop, and 5,000 participants complete the end of course certificate, the average cost per successful participant is \$20. However, this assumes that the same type of knowledge and skills is being assessed for both a MOOC and for a graduate masters program; usually this not the case.
5.4.8.5 Costs versus outputs
The issue then is whether MOOCs can succeed without the cost of learner support and human assessment, or more likely, whether MOOCs can substantially reduce delivery costs through automation without loss of quality in learner performance. There is no evidence to date though that they can do this in terms of higher order learning skills and ‘deep’ knowledge. To assess this kind of learning requires setting assignments that test such knowledge, and such assessments usually need human marking, which then adds to cost. We also know from prior research from successful online credit programs that active instructor online presence is a critical factor for successful online learning. Thus adequate learner support and assessment remains a major challenge for MOOCs. MOOCs then are a good way to teach certain levels of knowledge but will have major structural problems in teaching other types of knowledge. Unfortunately, it is the type of knowledge most needed in a digital world that MOOCs struggle to teach.
5.4.8.6 MOOC business models and cost-benefits
In terms of sustainable business models, Baker and Passmore (2016) examined several different possible business models to support MOOCs (but do not offer any actual costing). The elite universities have been able to move into xMOOCs because of generous donations from private foundations and use of endowment funds, but these forms of funding are limited for most institutions. Coursera and Udacity have the opportunity to develop successful business models through various means, such as charging MOOC provider institutions for use of their platform, by collecting fees for badges or certificates, through the sale of participant data, through corporate sponsorship, or through direct advertising.
However, particularly for publicly funded universities or colleges, most of these sources of income are not available or permitted, so it is hard to see how they can begin to recover the cost of a substantial investment in MOOCs, even with ‘cannibalising’ MOOC material for or from on-campus use. Every time a MOOC is offered, this takes away resources that could be used for online credit programs. Thus institutions are faced with some hard decisions about where to invest their resources for online learning. The case for putting scarce resources into MOOCs is far from clear, unless some way can be found to give credit for successful MOOC completion.
5.4.9 Summary of strengths and weaknesses
The main points of this analysis of the strengths and weaknesses of MOOCs can be summarised as follows:
5.4.9.1 Strengths
• MOOCs, particularly xMOOCs, deliver high quality content from some of the world’s best universities for free or at little cost to anyone with a computer and an Internet connection;
• MOOCs can be useful for opening access to high quality content, particularly in developing countries, but to do so successfully will require a good deal of adaptation, and substantial investment in local support and partnerships;
• MOOCs are valuable for developing basic conceptual learning, and for creating large online communities of interest or practice;
• MOOCs are an extremely valuable form of lifelong learning and continuing education;
• MOOCs have forced conventional and especially elite institutions to reappraise their strategies towards online and open learning;
• institutions have been able to extend their brand and status by making public their expertise and excellence in certain academic areas;
• MOOCs main value proposition is to eliminate through computer automation and/or peer-to-peer communication the very large variable costs in higher education associated with providing learner support and quality assessment.
5.4.9.2 Weaknesses
• the high registration numbers for MOOCs are misleading; less than half of registrants actively participate, and of these, only a small proportion successfully complete the course; nevertheless, absolute numbers completing are still higher than for conventional courses;
• MOOCs are expensive to develop, and although commercial organisations offering MOOC platforms have opportunities for sustainable business models, it is difficult to see how publicly funded higher education institutions can develop sustainable business models for MOOCs;
• MOOCs tend to attract those with already a high level of education, rather than widen access;
• MOOCs so far have been limited in the ability to develop high level academic learning, or the high level intellectual skills needed in a digital society;
• assessment of the higher levels of learning remains a challenge for MOOCs, to the extent that most MOOC providers will not recognise their own MOOCs for credit;
• MOOC materials may be limited by copyright or time restrictions for re-use as open educational resources.
References
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Bates, A. (1984) Broadcasting in Education: An Evaluation London: Constables (out of print)
Bates, A. and Sangrà, A. (2011) Managing Technology in Higher Education San Francisco: Jossey-Bass/John Wiley and Co
Bayne, S. (2015) Teacherbot: interventions in automated teaching, Teaching in Higher Education, Vol. 20, No.4, pp. 455-467
Colvin, K. et al. (2014) Learning an Introductory Physics MOOC: All Cohorts Learn Equally, Including On-Campus Class, IRRODL, Vol. 15, No. 4
Engle, W. (2104) UBC MOOC Pilot: Design and Delivery Vancouver BC: University of British Columbia
Falchikov, N. and Goldfinch, J. (2000) Student Peer Assessment in Higher Education: A Meta-Analysis Comparing Peer and Teacher MarksReview of Educational Research, Vol. 70, No. 3
Firmin, R. et al. (2014) Case study: using MOOCs for conventional college coursework Distance Education, Vol. 35, No. 2
Harasim, L. (2017) Learning Theory and Online Technologies 2nd edition New York/London: Routledge
Haynie, D. (2014). State Department hosts ‘MOOC Camp’ for online learners. US News, January 20
Hill, P. (2013) Some validation of MOOC student patterns graphic, e-Literate, August 30
Ho, A. et al. (2014) HarvardX and MITx: The First Year of Open Online Courses Fall 2012-Summer 2013 (HarvardX and MITx Working Paper No. 1), January 21
Hollands, F. and Tirthali, D. (2014) MOOCs: Expectations and Reality New York: Columbia University Teachers’ College, Center for Benefit-Cost Studies of Education
Hülsmann, T. (2003) Costs without camouflage: a cost analysis of Oldenburg University’s two graduate certificate programs offered as part of the online Master of Distance Education (MDE): a case study, in Bernath, U. and Rubin, E., (eds.) Reflections on Teaching in an Online Program: A Case Study Oldenburg, Germany: Bibliothecks-und Informationssystem der Carl von Ossietsky Universität Oldenburg
Jaschik, S. (2013) MOOC Mess, Inside Higher Education, February 4
Knox, J. (2014) Digital culture clash: ‘massive’ education in the e-Learning and Digital Cultures Distance Education, Vol. 35, No. 2
Kop, R. (2011) The Challenges to Connectivist Learning on Open Online Networks: Learning Experiences during a Massive Open Online Course International Review of Research into Open and Distance Learning, Vol. 12, No. 3
Lave, J. and Wenger, E. (1991). Situated Learning: Legitimate Peripheral Participation. Cambridge: Cambridge University Press
Milligan, C., Littlejohn, A. and Margaryan, A. (2013) Patterns of engagement in connectivist MOOCs, Merlot Journal of Online Learning and Teaching, Vol. 9, No. 2
Pickard, L. (2018) Analysis of 450 MOOC-Based Microcredentials Reveals Many Options But Little Consistency, Class Central, July 18
Piech, C., Huang, J., Chen, Z., Do, C., Ng, A., & Koller, D. (2013) Tuned models of peer assessment in MOOCs. Palo Alto, CA: Stanford University
Rumble, G. (2001) The costs and costing of networked learning, Journal of Asynchronous Learning Networks, Vol. 5, No. 2
Suen, H. (2104) Peer assessment for massive open online courses (MOOCs) International Review of Research into Open and Distance Learning, Vol. 15, No. 3
University of Ottawa (2013) Report of the e-Learning Working Group Ottawa ON: The University of Ottawa
van Zundert, M., Sluijsmans, D., van Merriënboer, J. (2010). Effective peer assessment processes: Research findings and future directions Learning and Instruction, 20, 270-279
Activity 5.4 Assessing the strengths and weaknesses of MOOCs
1. Do you agree that MOOCs are just another form of educational broadcasting? What are your reasons?
2. Is it reasonable to compare the costs of xMOOCs to the costs of online credit courses? Are they competing for the same funds, or are they categorically different in their funding source and goals? If so, how?
3. Could you make the case that cMOOCs are a better value proposition than xMOOCs – or are they again too different to compare?
4. MOOCs are clearly cheaper than either face-to-face or online credit courses if judged on the cost per participant successfully completing a course. Is this a fair comparison, and if not, why not?
5. Do you think institutions should give credit for students successfully completing MOOCs? If so, why, and what are the implications?
I give my own personal views on these questions in the podcast below, but I’d like you to come to your own conclusions before listening to my response, because there are no right or wrong answers here:
An audio element has been excluded from this version of the text. You can listen to it online here: https://pressbooks.bccampus.ca/teachinginadigitalagev2/?p=159 | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/05%3A_MOOCs/05.4%3A_Strengths_and_weaknesses_of_MOOCs.txt |
Figure 5.5 MOOC mania
Image: © Park Ridge Underground, 2010
5.5.1 Why the fuss about MOOCs?
It can be seen from the previous section that the pros and cons of MOOCs are finely balanced. Given though the obvious questions about the value of MOOCs, and the fact that before MOOCs arrived, there had been substantial but quiet progress for over ten years in the use of online learning for undergraduate and graduate programs, you might be wondering why MOOCs have commanded so much media interest, and especially why a large number of government policy makers, economists, and computer scientists have become so ardently supportive of MOOCs, and why there has been such a strong, negative reaction, not only from many university and college instructors, who understandably feel threatened by the implications of MOOCs, but also from many professionals in online learning (see for instance, Hill, 2012; Bates, 2012; Daniel, 2012; Watters, 2012), who might be expected to be more supportive of MOOCs.
It needs to be recognised that the discourse around MOOCs is not usually based on a cool, rational, evidence-based analysis of the pros and cons of MOOCs, but is more likely to be driven by emotion, self-interest, fear, or ignorance of what education is actually about. Thus it is important to explore the political, social and economic factors that have driven MOOC mania.
5.5.2 Massive, free and Made in America!
This is what I will call the intrinsic reason for MOOC mania. It is not surprising that, since the first MOOC from Stanford professors Sebastian Thrun, Andrew Ng and Daphne Koller each attracted over 200,000 sign-ups from around the world, since the courses were free, and since it came from professors at one of the most prestigious private universities in the USA, the American media were all over it. It was big news in its own right, however you look at it.
5.5.3 It’s the Ivy Leagues!
Until MOOCs came along, the major Ivy League universities in the USA, such as Stanford, MIT, Harvard and UC Berkeley, as well as many of the most prestigious universities in Canada, such as the University of Toronto and McGill, and elsewhere, had largely ignored online learning in any form (the exception was MIT, which made much of its teaching material available for free via the OpenCourseWare project.).
However, by 2011, online learning, in the form of for credit undergraduate and graduate courses, was making big inroads at many other, very respectable universities, such as Carnegie Mellon, Penn State, and the University of Maryland in the USA, and also in many of the top tier public universities in Canada and elsewhere, to the extent that one in three students in the USA were were taking online courses (Allen and Seaman, 2014). Furthermore, at least in Canada, the online courses were often getting good completion rates and matching on-campus courses for quality (Ontario, 2011).
The Ivy League and other highly prestigious universities that had ignored online learning were beginning to look increasingly out of touch by 2011. By launching into MOOCs, these prestigious universities could jump to the head of the queue in terms of technology innovation, while at the same time protecting their selective and highly personal and high cost campus programs from direct contact with online learning. In other words, MOOCs gave these prestigious universities a safe sandbox in which to explore online learning. At the same time, the involvement of the Ivy League universities in online learning for the first time gave credibility to MOOCs, and, inadvertently, online learning as a whole.
5.5.4 It’s disruptive!
For years before 2011, various economists, philosophers and industrial gurus had been predicting that education was the next big area for disruptive change due to the march of new technologies (see for instance Lyotard, 1979; Tapscott (undated); Christensen, 2016).
However, although online learning in credit courses had been quietly absorbed into the mainstream of university teaching, without any signs of major disruption, MOOCs were a potentially massive change, evidence at long last for the theory of disruption in the education sector.
5.5.5 It’s Silicon Valley!
It is no coincidence that the first MOOCs were all developed by entrepreneurial computer scientists. Ng and Koller very quickly went on to create Coursera as a private, commercial company, followed shortly by Thrun, who created Udacity. Anant Agarwal, a computer scientist at MIT, went on to head up edX.
The first MOOCs were very typical of Silicon Valley start-ups: a bright idea (massive, open online courses with cloud-based, relatively simple software to handle the numbers), thrown out into the market to see how it might work, supported by more technology and ideas (in this case, learning analytics, automated marking, peer assessment) to deal with any snags or problems. Building a sustainable business model would come later, when some of the dust had settled.
As a result it is not surprising that almost all the early MOOCs completely ignored any pedagogical theory about best practices in teaching online, or any prior research on factors associated with success or failure in online learning. It is also not surprising as a result that a very low percentage of participants actually successfully completed MOOCs.
5.5.6 It’s the economy, stupid!
Of all the reasons for MOOC mania, Bill Clinton’s famous election slogan resonates the most. It should be remembered that by 2011, the consequences of the disastrous financial collapse of 2008 were working their way through the economy, and particularly were impacting on the finances of state governments in the USA.
The recession meant that states were suddenly desperately short of tax revenues, and were unable to meet the financial demands of state higher education systems. For instance, California’s community college system, the nation’s largest, suffered about \$809 million in state funding cuts between 2008-2012, resulting in a shortfall of 500,000 places in its campus-based colleges (Rivera, 2012). Free MOOCs were seen as manna from heaven by the state governor, Jerry Brown (see for instance, To, 2014).
One consequence of rapid cuts to government funding was a sharp spike in tuition fees, bringing the real cost of higher education sharply into focus. Tuition fees in the USA have increased by 7 per cent per annum over the last 10 years, compared with an inflation rate of 4 per cent per annum. Here at last was a possible way to rein in the high cost of higher education. By 2015 though the economy in the USA had picked up and revenues were flowing back into state coffers, and so the immediate pressure for more radical solutions to the cost of higher education began to ease.
5.5.7 The future of MOOCs
It will be interesting to see if MOOC mania continues as the economy grows. Class Central provides ongoing monitoring of developments in MOOCs around the world. The overall numbers up to 2019 are impressive but the number of learners added in 2018 was just 20 million, which was less than 23 million for the previous two years (Shah, 2019). So the rate at which new users are coming into the MOOC space is decreasing.
However, MOOCs continue to evolve. For a start, there has been a slow growth in complete degrees that can be offered through MOOCs. In 2018 there were 45 degrees on offer. While this is a significant development, though, the numbers are still quite small, given the number of conventional degrees available worldwide. The other main market is corporate training. Business models are also evolving with revenues continuing to increase into 2018, with Coursera alone recording \$140 million in revenues. However, although the number of MOOC courses offered continues to increase, the average number of students is decreasing as more choices become available.
The rate of adoption also varies considerably by country. For instance in 2017, only 18% of Canadian post-secondary institutions were offering MOOCs, compared with 82% that were offering fully online courses for credit (Donovan et al., 2018). However, the growth of MOOCs in China, India and Europe continues apace. What is not clear is whether the institutions providing MOOCs are getting any direct financial returns for their investments as distinct from the platform providers.
5.5.8 Don’t panic!
These are all very powerful drivers of MOOC mania, which makes it all the more important to try to be clear and cool headed about the strengths and weaknesses of MOOCs. The real test is whether MOOCs can help develop the knowledge and skills that learners need in a knowledge-based society. The answer of course is yes and no.
As a low-cost supplement to formal education, they can be quite valuable, but not as a complete replacement. They can at present teach basic conceptual learning, comprehension and in a narrow range of activities, application of knowledge. They can be useful for building communities of practice, where already well educated people or people with a deep, shared passion for a topic can learn from one another, another form of continuing education.
However, certainly to date, MOOCs have not been able to demonstrate that they can lead to transformative learning, deep intellectual understanding, evaluation of complex alternatives, and evidence-based decision-making, and without greater emphasis on expert-based learner support and more qualitative forms of assessment, they probably never will, at least without substantial increases in their costs.
At the end of the day, there is a choice for institutions between throwing more resources into MOOCs and hoping that some of their fundamental flaws can be overcome without too dramatic an increase in costs, or investing in other forms of online learning and educational technology that could lead to more cost-effective learning outcomes in terms of the needs of learners in a digital age.
References
Allen, I. and Seaman, J. (2014) Grade Change: Tracking Online learning in the United States Babson Survey Research Group/Pearson/Sloan
Bates, T. (2012) What’s right and what’s wrong with Coursera-style MOOCs Online Learning and Distance Education Resources, August 5
Christensen, C. (2016) Disrupting Class, Expanded Edition: How Disruptive Innovation Will Change the Way the World Learns: Expanded Edition New York: McGraw-Hill
Daniel, J. (2012) Making sense of MOOCs: Musings in a maze of myth, paradox and possibilityJournal of Interactive Media, No. 18
Donovan, T. et al. (2018) Tracking Online and Distance Education in Canadian Universities and Colleges: 2018 Halifax NS: Canadian Digital Learning Research Association
Hill, P. (2012) Four Barriers that MOOCs Must Overcome to Build a Sustainable Model e-Literate, July 24
Lyotard, J-J. (1979) La Condition postmoderne: rapport sur le savoir: Paris: Minuit
Ontario (2011) Fact Sheet: Summary of Ontario eLearning Surveys of Publicly Assisted PSE Institutions, Toronto: Government of Ontario
Rivera, C. (2012) Survey offers dire picture of California’s two-year collegesLos Angeles Times, 29 August
Tapscott, D. (undated) The transformation of education dontapscott.com
To, K. (2014) UC Regents announce online course expansion, The Guardian, UC San Diego, undated, but probably February 5
Shah, D. (2019) Year of MOOC-based degrees: A Review of MOOC Stats and Trends in 2018Class Central, 6 January
Watters, A. (2012) Top 10 Ed-Tech Trends of 2012: MOOCsInside Higher Education, 18 December
For a more light-hearted look at MOOC mania see:
NOTE: Both the two blog posts above are satirical: they are fictional!
Activity 5.5 Assessing the importance of MOOCs
1. Do you think MOOCs have improved or weakened public acceptance of online learning? Why?
2. On a scale of 1 to 10, where 1 is no importance and 10 is extremely important, where would you rank MOOCs in terms of their importance for the future of higher education? Why?
3. Do you think MOOCs will improve to the point where they are a serious alternative to other forms of higher education, or do you think they will never be a real challenge to conventional university teaching? What are your reasons?
Once again, my views should carry no more weight than yours on these questions, as they are value rather than fact based, but here are my thoughts, for what they are worth:
An audio element has been excluded from this version of the text. You can listen to it online here: https://pressbooks.bccampus.ca/teachinginadigitalagev2/?p=162 | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/05%3A_MOOCs/05.5%3A_Political_social_and_economic_drivers_of_MOOCs.txt |
Image: Your Training Edge, 2015
5.6.1 The importance of context and design
I am frequently labelled as a major critic of MOOCs, which is somewhat surprising since I have been a longtime advocate of online learning. In fact I do believe MOOCs are an important development, and under certain circumstances they can be of tremendous value in education.
But as always, context is important. There is not one but many different markets and needs for education. A student leaving high school at eighteen has very different needs and will want to learn in a very different context from a 35 year old employed engineer with a family who needs some management education. Similarly a 65 year old man struggling to cope with his wife’s early onset of Alzheimer’s and desperate for help is in a totally different situation to either the high school student or the engineer. When designing educational programs, it has to be horses for courses. There is no single silver bullet or solution for every one of these various contexts.
Secondly, as with all forms of education, how MOOCs are designed matters a great deal. If they are designed inappropriately, in the sense of not developing the knowledge and skills needed by a particular learner in a particular context, then they have little or no value for that learner. However, designed differently and a MOOC may well meet that learner’s needs.
5.6.2 The limitations of xMOOCs
The real threat of xMOOCs is to the very large face-to-face lecture classes found in many universities at the undergraduate level. MOOCs are a more effective way of replacing such lectures. They are more interactive and permanent so students can go over the materials many times. I have heard MOOC instructors argue that their MOOCs are better than their classroom lectures. They put more care and effort into them.
However, we should question why we are teaching in this way on campus. Content is now freely available anywhere on the Internet – including MOOCs. What is needed is information management: how to identify the knowledge you need, how to evaluate it, how to apply it. xMOOCs do not do that. They pre-select and package the information. My big concern with xMOOCs is their limitation, as currently designed, for developing the higher order intellectual skills needed in a digital world. Unfortunately, xMOOCs are taking the least appropriate design model for developing 21st century skills from on-campus teaching, and moving this inappropriate design model online. Just because the lectures come from elite universities does not necessarily mean that learners will develop high level intellectual skills, even though the content is of the highest quality. More importantly, with MOOCs, relatively few students succeed, in terms of assessment, and those that do are tested mainly on comprehension and limited application of knowledge.
We can and have done much better in terms of skills for a digital age with other pedagogical approaches, both on campus, such as problem- or inquiry-based learning, and online using more constructivist approaches in credit courses, such as online collaborative learning. However these alternative methods to lectures do not scale so easily. The interaction between an expert and a novice still remains critical for developing deep understanding, transformative learning resulting in the learner seeing the world differently, and for developing high levels of evidence-based critical thinking, evaluation of complex alternatives, and high level decision-making. Computer technology to date is extremely poor at enabling this kind of learning to develop. This is why credit-based classroom and online learning still aim to have a relatively low instructor:student ratio and still need to focus a great deal on interaction between instructor and students.
However xMOOCs are valuable as a form of continuing education, or as a source of open educational materials that can be part of a broader educational offering. They can be a valuable supplement to campus-based education. They are not a replacement though for either conventional education or the current design of online credit programs. As a form of continuing education, low completion rates and the lack of formal credit is not of great significance. However, completion rates and quality assessment DO matter if MOOCs are being seen as a substitute or a replacement for formal education, even classroom lectures.
5.6.3 Undermining the public higher education system?
The real danger is that xMOOCs may be used to undermine what is admittedly an expensive public higher education system. If elite universities can deliver MOOCs for free, why do we need low quality and high cost state universities? The risk is a sharply divided two tier system, with a relatively small number of campus-based elite universities catering to the rich and privileged, and developing the knowledge and skills that will provide rich rewards, and the masses being fed xMOOC-delivered courses, with state universities providing minimal and low cost learner support for such courses. This would be both a social and economic disaster, because it would fail to produce enough learners with the high-level skills that are going to be needed for good jobs in the the coming years – unless you believe that automation will remove all decently paid jobs except for a tiny elite (bring on the Hunger Games).
Content accounts for less than 15 per cent of the total cost over five years for credit-based online programs; the main costs required to ensure high quality outcomes and high rates of completion are spent on learner support, providing the learning that matters most. The kind of MOOCs being promoted by politicians and the media fail spectacularly to do this. We do need to be careful that the open education movement in general, and MOOCs in particular, are not used as a stick by those in the United States and elsewhere who are deliberately trying to undermine public education for ideological and commercial reasons. On their own, open content, OERs and MOOCs do not automatically lead to open access to high quality credentials for everyone. In the end, a well-funded public higher education system remains the best way to assure access to higher education for all.
5.6.3 The potential of cMOOCs
cMOOCs have the most potential, because lifelong learning will become increasingly important, and the power of bringing a mix of already well educated and knowledgeable people from around the world to work with other committed and enthusiastic learners on common problems or areas of interest could truly revolutionise not just education, but the world in general.
However, cMOOCs at present are unable to do this, because they lack organisation and do not apply what is already known about how online groups work best. Once we learn these lessons and apply them, though, cMOOCs can be a tremendous tool for tackling some of the great challenges we face in the areas of global health, climate change, civil rights, and other ‘good civil ventures’. The beauty of cMOOCs is that they every participant has the power to define and solve the problems being tackled.
Scenario F that ends this chapter is an example of how cMOOCs could be used for such ‘good civil ventures.’ In Scenario F, the MOOC is not a replacement for formal education, but a rocket that needs formal education as its launch pad. Behind this MOOC are the resources of a very powerful institution, that provides the initial impetus, simple to use software, overall structure, organization and co-ordination within the MOOC, and some essential human resources for supporting the MOOC when running. At the same time, it does not have to be an educational institution. It could be a public health authority, or a broadcasting organization, or an international charity, or a consortium of organisations with a common interest. Also, of course, there is the danger that even cMOOCs could be manipulated by corporate or government interests.
5.6.5 In conclusion
Having said that, there is enormous scope for improvements within the public higher education system. MOOCs, open education and new media offer promising ways to bring about some much needed improvements. Scenario F (next) is one possible way in which MOOCs could bring about much needed social change.
However, MOOCs must build on what we already know from the use of credit based online learning, from prior experience in open and distance learning, and designing courses and programs in a variety of ways appropriate to the wide range of learning needs. MOOCs can be one important part of that environment, but not a replacement for other forms of educational provision that meet different needs.
Activity 5.6: Strategising about MOOCs
You are the Vice President Academic of a middle sized research university, which is under financial pressure. The President has been asked by the Board to come forward with a strategy for innovation in teaching and learning, with the university facing a cut of approximately 5 per cent in next year’s operating budget.
One powerful Board member is pushing really hard for the university to develop MOOCs as a solution to the economic pressure..
The President has asked for a briefing paper from you for the Board on what the university’s strategy should be regarding MOOCs, and how they would fit into the overall strategy for teaching and learning. How would you respond?
Since there are many pros and cons regarding MOOCs, I am not going to give direct feedback on this activity, because the ‘best’ briefing will take account of local contexts, such as existing online provision for credit courses, learning technology support and enrolment goals, for instance.
Chapter 5: Key Takeaways
1. MOOCs are forcing every higher education institution to think carefully both about its strategy for online teaching and its approach to open education.
2. MOOCs are not the only form of online learning nor of open educational resources. It is important to look at the strengths and weaknesses of MOOCs within the overall context of online learning and open-ness.
3. There are considerable differences in the design of MOOCs, reflecting different purposes and philosophies.
4. There are currently major structural limitations in MOOCs for developing deep or transformative learning, or for developing the high level knowledge and skills needed in a digital age.
5. MOOCs are at still a relatively early stage of maturity. As their strengths and weaknesses become clearer, and as experience in improving their design grows, they are likely to occupy a significant niche within the higher education learning environment
6. MOOCs could well replace some forms of traditional teaching (such as large lecture classes). However, MOOCs are more likely to remain an important supplement or alternative to other conventional education methods. They are not on their own a solution to the high cost of higher education, although MOOCs are and will continue to be an important factor in forcing change.
7. Perhaps the greatest value of MOOCs in the future will be for providing a means for tackling large global problems through community action.
5.6.6 Next
This completes the discussion about different design models for teaching and learning. The next chapter looks at the importance of building an effective learning environment in which these different design models can best operate.
But first, Scenario F, which envisions what MOOCs could look like in the future. | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/05%3A_MOOCs/05.6%3A_Why_MOOCs_are_only_part_of_the_answer.txt |
Figure F 1. Image: WhatSheSaidradio.com
Beth Carter Good evening, everyone. This is Beth Carter, for BBC Radio. The Open University yesterday announced that it had signed up half a million participants in what they claim is now the world’s largest online course. The OU’s MOOC is about something many of you will be familiar with – getting old, and the many challenges and opportunities that come with that.
In the studio with me is Jane Dyson, who is the course co-ordinator. Jane: at 55, and coming from a social services background, you seem to be the least likely person to be running such a massive, technology-based program. How did that happen?
Jane Dyson: (laughing). Well, it’s all my own fault! I’ve been an OU graduate for many years, and they have an online alumni forum, where they ask former students for ideas about what are the most pressing issues we see in the world, and what the OU could do to address some of these issues. I do a lot of work advising elderly people, their families and even employers these days about the many different kinds of issues that arise with aging.
The OU has many courses and online materials that deal with lots of these issues, but you have to sign up for a degree or diploma or you can just get the materials online but without any support. Also, there are just too many different issues for even the OU to cover in its formal courses. So I suggested that they should do a MOOC where all the different people involved – health care workers, social workers, care givers, family, and most important of all, old people themselves – could talk about their problems and challenges, and what services are available, what people can do for themselves and so on.
Beth Carter. So what happened then?
Jane Dyson. The OU asked me to come in to my local OU regional office, and I met with several people from the OU, and after that meeting, they asked me if I would be willing to co-ordinate such a course.
Beth Carter. Now tell me more about MOOCs. I remember they were big about 10 years ago, then they went all quiet, and we haven’t heard much about them since. So what’s made this MOOC so popular?
Jane Dyson. The problem with the earlier MOOCs was that participants just got lost in them. Many of the MOOCs were just lectures and then it was up to the participants to help each other out. There was no organization.
What the OU did was to ask those who signed up for the ‘Aging’ MOOC to fill in a very simple online questionnaire that asked for just a few details such as where they lived, whether they were professionals in aging, or family, or elderly people themselves, and then used that data to automatically allocate participants into groups, so that there was a mix of participants in each group.
Beth Carter. Why was that important?
Jane Dyson. Well, at the OU, the Institute of Educational Technology had done some research on the early MOOCs, and had identified this problem of how to get groups to work in large online classes. They worked with another research group in the OU called the KMI, who developed the software we are using that allocates participants into groups so that there is enough expertise and support in each group to help with the issues raised in the group discussions.
Beth Carter. And how does that work?
Jane Dyson. You wouldn’t believe the range of issues or problems that come up. For instance, we have family members desperate because their father or mother is suffering from dementia, but don’t know what to do to help them. We have some seniors who feel that their family are trying to force them out of their homes, while they feel they are quite capable of looking after themselves. We have social workers who feel that they are liable to get fired or even prosecuted because they can’t handle their case load. And we have some participants who are just old and lonely, and want someone to talk to.
When we put all these participants into an online discussion forum, the results are amazing. What’s really critical is getting the right mix of people in the same group, with enough expertise to provide help, and having someone in that group who knows how to moderate the discussions. We have a huge list of services available not just in Britain but in many of the other countries from which we have students. So the course is a kind of self-help, support service within a broader community of practice.
Beth Carter. Let’s talk about the international students. As I understand it, almost half the participants are from outside the U.K..
Jane Dyson. That’s right. The problems of an aging population aren’t just British. The OU is part of a very powerful network of open universities around the world. When we were talking about starting this course, the OU went to several other open universities and asked them if they were interested in participating. So we have participants from the Netherlands, Germany, France, Spain, Japan, Canada, the USA, and many other countries, who participate in the English language version.
In Spain, though, we have a ‘mirror’ site, with materials in Spanish, Basque and Catalan, and the discussion forums are managed by the Open University of Catalonia. That brings in not only participants from Spain, but also from Latin America. We are about to develop a similar agreement with the Open University of China, which we expect will bring in another half million participants. What’s really neat is that because we have so many participants, there are always enough dual language participants to move stuff from one language discussion forum to another.
Beth Carter. So what’s next?
Jane Dyson. One of the big issues that keeps coming up in the Aging course is the issue of mental health. This of course is not just about elderly people. The Aging course has already resulted in petitions to parliament about better services for isolated elderly people, and I think we will see some positive developments on this front over the next couple of years. So I think the OU is thinking about a similar MOOC on mental health, and I’d really like to be part of that initiative.
Beth Carter. Well, thank you, Jane. Next week we will be discussing online gambling, with an addiction counsellor.
[This was developed as a ‘what if?’ scenario for the U.K. Open University as part of its planning for teaching and learning in 2014.] | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/05%3A_MOOCs/05.7%3A_Scenario_F%3A_How_to_cope_with_being_old.txt |
Figure 6.10 What kind of learning environment do you want to create? Image: Vidyo.com
There is no one way to build an effective learning environment. The learning environment needs to be appropriate for the context in which students will learn. However, before even beginning to design a course or program, we should be thinking of what this learning environment could look like. Whatever the learning environment, though, the learners must do the learning. We need to make sure that learners are able to work within an environment that helps them do this. In other words, our job as teachers is to create the conditions for success.
One component within an effective learning environment that I have not discussed is the actual teacher (although in Figure 6.9.2 you will see that she is at the centre of the learning environment). In some sense the importance of a teacher or instructor within a learning environment is a given, but really the rest of the book is about the role of the teacher within this environment. Also by concentrating on the other components, this chapter enables the possibility of a learning environment without an actual teacher, although someone such as a teacher or educator or even an individual learner (but definitely not a computer scientist) may need to be responsible for the design and maintenance of such a learning environment.
Technology now enables us to build a wide variety of effective learning environments that can differ significantly from the traditional classroom. But technology alone is not enough. Many technology-based learning environments are bereft of some of the key components that make an effective learning environment. An effective learning environment needs to include the other components for learner success. This is not to say that self-managing learners cannot build their own effective, personal learning environments, but they need to consider the other components as well as the technology.Activity 6.10 Dsigning your own learning environment
1. Describe the current learning environment in which you are teaching a particular course or program.
2. What are the main components to which you give the most attention?
3. Would you make changes to that learning environment as a result of reading this chapter? Why?
4. Now: can you design a completely different learning environment that would better fit the needs of the course and your students? For instance if you moved your course from classroom to online, or from fully online to blended, how would you accommodate the main components of this learning environment? Or could you re-design the learning environment within the current mode of delivery? If so, what elements would you change, and what would you keep?
I provide no feedback for this activity. It is for your own reflection.
This concludes Part 1 of the book, which focuses on the fundamentals of teaching and learning in a digital age. Part 2 of the book (Chapters 7-13) pays special attention to the impact of digital technologies on teaching and learning, starting with Chapter 7, which examines the nature and role of media and technologies in education.
06.1: Integrating design principles within a rich learning environment
Nature as a learning environment
6.1.1 The importance of creating an effective learning environment
Chapters 1 to 5 provide a set of methods for teaching in a digital age. These methods though will not operate in a vacuum. Both teachers and learners are faced with a rapidly changing world, with new technology, new teaching approaches and external pressures from government, employers, parents, and the media. It is easy to be tossed around in such a stormy environment. Learning always takes place within a context that can influence how and what we learn. Good teachers and instructors try to shape the environment in which they are teaching to create the right conditions for learning. This becomes even more important in a volatile, uncertain, complex and ambiguous world.
6.1.2 Learning environments and epistemology
First though we need to examine two very different approaches to teaching and learning. One approach starts with an objectivist view of the world. Knowledge is like coal. It is there to be mined by the teacher and transported to the learner. The learner’s job is to acquire that coal or knowledge and then use it as necessary, either with or without the help of the teacher. This seems to me to be the approach of most xMOOCs and most classroom lectures. There is little attention if any paid to the conditions in which such learning will best take place.
Another approach starts from the assumption that learning is a fundamental human activity. Humans have become the dominant species because they have a need and above all an inherited ability to learn. If we had not been reasonably good at learning, we would have been killed off early in the earth’s history by faster, bigger and more ferocious animals. The ability not only to learn, but to learn in abstract and conscious ways, is therefore part of human nature.
If that is the case, a teacher’s job is not to do the learning for the student, but to build a rich environment that facilitates the kind of learning that will benefit the learner. It is not a question of pouring knowledge into a student’s head, but enabling the learner to develop concepts, think critically, and apply and evaluate what they have learned, by providing opportunities and experiences that are relevant to such goals.
The analogy here is gardening. Humans are like plants: all we need to do is to provide the right conditions for them to grow: the right soil, sufficient sunshine and water, and help eliminating pests and weeds. In terms of humans, this means providing security, and the best conditions for learning. This is a very constructivist view of the world. This seems to me to be the approach of most cMOOCs and most early childhood education. However, there is little attention paid to priorities or to efficiency in learning.
A second premise is that knowledge is not fixed or static, but is continually developing. Our concept of heat changes and becomes richer as we grow older and become more educated, from understanding heat through touch, to providing a quantitative way of measuring it, to understanding its physical properties, to being able to apply that knowledge to solving problems, such as designing refrigerators. In a knowledge-based society, knowledge is constantly developing and growing, and our understanding is always developing.
6.1.3 What learning environments do we want?
Why thinking about effective learning environments is important is because most teachers currently inherit a teaching environment, usually based on a campus, physical classrooms, regularly scheduled lessons, with the expectation of the teacher in control at the front of the class. However, new technologies provide us with the opportunity to design other kinds of learning environments. What do we want to be: coal miners – or gardeners? Or something else? My own view is that the ideal learning environment is somewhere in between coal mining and gardening. Most learners require structure and guidance, but within an environment that enables freedom and exploration.
In developing an effective learning environment, there are another two issues that need to be addressed:
• First, it is the learner who has to do the learning.
• Second, any learning environment is much more than the technology used to support it.
With regard to the first, teachers cannot do the learning for the learner. All teachers or instructors can do is to create and manage an environment that enables and encourages learning. My focus then in terms of building an effective learning environment is on what the teacher or instructor can do, because in the end that is all they can control. However, the focus of what the teacher does should be on the learner, and what the learner needs. That of course will require good communication between the learners and the teacher.
For this reason, I want to examine some of the fundamental components of most effective teaching environments. Not only will this provide some general guidance for the design of teaching, it will also allow consideration of technology-based learning environments that can fundamentally differ from traditional campus-based environments, while at the same time ensuring conditions for successful learning. I set out these components or conditions in the following sections.
Activity 6.1 Your current students’ learning environment
1. If you are currently teaching, describe briefly the student learning environment within which they are learning. What are the restrictions, if any, on their learning as a result of this environment?
2. What do you think are the most important components for effective learning within this environment (as well as your teaching)?
3. Are you more of a coal miner or a gardener in your approach to teaching?
There is no feedback from me on this activity. It is for your own reflection. | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/06%3A_Building_an_effective_learning_environment/06.1%3A0_Conclusions.txt |
Figure 6.2.1 A technology-based personal learning environment
Image: Jason Hews, Flikr
6.2.1 Definition
‘Learning environment refers to the diverse physical locations, contexts, and cultures in which students learn. Since students may learn in a wide variety of settings, such as outside-of-school locations and outdoor environments, the term is often used as a more accurate or preferred alternative to classroom, which has more limited and traditional connotations—a room with rows of desks and a chalkboard, for example.
The term also encompasses the culture of a school or class—its presiding ethos and characteristics, including how individuals interact with and treat one another—as well as the ways in which teachers may organize an educational setting to facilitate learning…..’
The Glossary of Educational Reform, 29 August, 2014
This definition recognises that students learn in many different ways in very different contexts. Since learners must do the learning, the aim is to create a total environment for learning that optimises the ability of students to learn. There is of course no single optimum learning environment. There is an infinite number of possible learning environments, which is what makes teaching so interesting.
6.2.2 Types of learning environments
Here are some examples of different learning environments:
• a school or college campus
• an online course
• military training
• friends, family and work
• nature
• personal, technology-based, learning environments
Nevertheless I will argue that despite the differences in context, there are certain elements or components that will be found in most effective learning environments.
6.2.3 Components of an effective learning environment
Developing a total learning environment for students in a particular course or program is probably the most creative part of teaching. Although there is a tendency to focus on either physical institutional learning environments (such as classrooms, lecture theatres and labs), or on the technologies used to to create online learning environments such as learning management systems, learning environments are broader than just these physical components. They will also include:
• the characteristics of the learners and their means of inter-communication;
• the goals for teaching and learning;
• the activities that support learning;
• the resources that are available, such as textbooks, technology, or learning spaces;
• the assessment strategies that will best measure and drive learning;
• the culture that infuses the learning environment.
Figure 6.2.2 An example of a learning environment
Figure 6.2.2 illustrates one possible learning environment from the perspective of a teacher or instructor. A teacher may have little or no control over some components, such as learner characteristics or resources, but may have full control over other components such as choice of content and how learners will be supported. Within each of the main components there are a set of sub-components that will need to be considered. In fact, it is in the sub-components (content structure, practical activities, feedback, use of technology, assessment methods, and so on) where the real decisions need to be made.
I have listed just a few components in Figure 6.2.2 and the set is not meant to be comprehensive. For instance it could have included other components, such as developing ethical behaviour, institutional factors, or external accreditation, each of which might also affect the learning environment in which a teacher or instructor has to work. Creating a model of a learning environment then is a heuristic device that aims to provide a comprehensive view of the whole teaching context for a particular course or program, by a particular instructor or teacher with a particular view of learning. Once again, the choice of components and their perceived importance will be driven to some extent by personal epistemologies and beliefs about knowledge, learning and teaching methods.
Lastly, I have deliberately suggested a learning environment from the perspective of a teacher, as the teacher has the main responsibility for creating an appropriate learning environment, but it is also important to consider learning environments from the learners’ perspectives. Indeed, adult or mature learners are often capable of creating their own, personal, relatively autonomous learning environments.
The significant point is that it is important to identify those components that need to be considered in teaching a course or program. In particular that there are other components besides content or curriculum. Each of the key components of the learning environment I have chosen as an example are discussed briefly in the following sections, with a focus on the components of a learning environment that are particularly relevant for a digital age.
Activity 6.2 Influencing a learning environment
1. Why do you think I focused on learning environments from a teacher’s perspective rather than a learner’s perspective? Could you design a similar model of a learning environment from the perspective of a learner? What would be the main differences?
2. In order to create the learning environment for HIST 305 in Scenario D, Ralph Goodyear carefully considered the learning environment he wanted to create and ones he had little or no control over. What components do you think he had little or no control over?
3. What would you add (or remove) from the learning environment in Figure 6.2.2?
4. What is missing in Figure 6.2.1 – the technology-based personal learning environment? For what kind of purpose would it work really well?
5. Does thinking about the whole learning environment overly complicate the teaching endeavour? Why not just get on with it?
For my feedback on this activity, click on the podcast below.
An audio element has been excluded from this version of the text. You can listen to it online here: https://pressbooks.bccampus.ca/teachinginadigitalagev2/?p=360 | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/06%3A_Building_an_effective_learning_environment/06.2%3A_What_is_a_learning_environment.txt |
Figure 6.3 Learner characteristics
Probably nothing more reflects teaching in a digital age than the change in learner characteristics from the industrial age.
6.3.1 Increased diversity
I noted in Chapter 1 (Section 6) that in developed countries such as Canada:
public post-secondary institutions are expected to represent the same kind of socio-economic and cultural diversity as in society at large, rather than being institutions reserved for an elite minority.
In an age where economic development is tightly associated with higher levels of education, the goal now is to bring as many students as possible to the standards required, rather than focus on just the needs of the most able students. This means finding ways of helping a very wide range of students with very different levels of ability and/or prior knowledge to succeed. One size clearly does not fit all today. Dealing with an increasingly diverse student population is perhaps the greatest of all challenges then that teachers and instructors face in a digital age, particularly but not exclusively at a post-secondary level. This is not something for which instructors primarily qualified in subject matter expertise are well prepared.
A combination of good design and an appropriate use of technology will greatly facilitate the personalization of learning, allowing for instance for different students to work at different speeds, and to focus learning on students’ specific interests and needs, thus ensuring engagement and motivation for a diverse range of students. However, the first and perhaps most important step is for instructors to know their students, and in particular, to identify from the vast range of information regarding students and their differences, which are the most important for the design of teaching and learning in a digital age. I list some of the characteristics that I think are important from the perspective of designing teaching.
6.3.2 The work and home context
Two factors make the work and home context an important consideration in the design of teaching and learning: students are increasingly working while studying (about half of all Canadian post-secondary students also work, and those that do work average 16 hours a week – Marshall, 2010); and the age range of students continues to spread, with the average age of students slowly increasing (in 2016, at the University of British Columbia Vancouver, the average age of undergraduates was 21, and the mean age for graduate students was 31 – UBC, 2017.)
There are several reasons for the average age of students increasing, at least in North America:
• students are taking longer to graduate (partly because they tend to take a smaller study load when working);
• increasing numbers of students are going on to graduate school;
• more students are coming back for additional courses and programs after graduating (lifelong learners), mainly for economic reasons.
Partly or fully employed students, or students with families, increasingly need more flexibility in their studying, and especially avoiding long commutes between home, work and college. These students increasingly want hybrid or fully online courses, and smaller modules, certificates or programs that they can fit around their work and family life.
6.3.3 Learners’ goals
Understanding the motivation of students and what they expect to get out of a course or program should also influence the design of a course or program. For academic learning, it is often necessary to find ways to move students whose approach to learning is initially driven by extrinsic rewards such as grades or qualifications to an approach that engages and motivates students in the subject matter itself. Potential students already with a post-secondary qualification and a good job may not want to work through a pre-determined set of courses but may want just specific areas of content from existing courses, tailored to meet their needs (for instance, on demand and delivered online). Thus it is important to have some kind of knowledge or understanding of why learners are likely to take your course or program, and what they are hoping to get out of it.
6.3.4 Prior knowledge or skills
Future learning often depends on students having prior knowledge or an ability to do things at a certain level. Teachers aim to bridge the difference between what a learner can do without help and what he or she can do with help, what Vygotsky (1978) termed the zone of proximal development. If the difficulty level of the teaching is aimed too far beyond the capability or prior knowledge and skills of a learner, then learning fails to occur.
However, the more diverse the students in a program, the more diverse the knowledge and skill levels they are likely to bring with them. Indeed, lifelong learners, or new immigrants repeating a subject because their foreign qualifications are not recognised, may bring specialist or advanced knowledge that can be drawn on to enrich the learning experience for everyone. At the same time, some students may not have the same basic knowledge as others in a course and will need more help. In such a context it is important to design the learning experience so that it is flexible enough to accommodate students with a wide range of prior knowledge and skills.
6.3.5 Digital natives
Most students today have grown up with digital technologies such as mobile phones, tablets and social media, including Facebook, Twitter, blogs and wikis. Prensky (2010) and others (e.g. Tapscott, 2008) argue that not only are such students more proficient in using such technologies than previous generations, but that they also think differently (Tapscott, 2008).
However, it is particularly important to understand that students themselves vary a great deal in their use of social media and new technologies, that their use is largely driven by social and personal demands, and their use of digital technologies does not naturally flow across into educational use. They will use new technologies and social media for learning though where instructors make a good case for it and when students can see that the use of digital media will directly help them in their studies. For this to happen though deliberate design choices are required on the part of the instructor. (For more on the issue of digital natives, see Chapter 9, Section 2.3)
6.3.6 In conclusion
The work and home context, learners’ goals, and students’ prior knowledge and skills (including their competence with digital media) are some of the critical factors that should influence the design of teaching. For some instructors, other characteristics of learners, such as learning styles, gender differences or cultural background, may be more important, depending on the context. Whatever the context, good design in teaching requires good information about the learners we are going to teach, and in particular good design needs to address the increasing diversity of our students.
References
Marshall, K. (2011) Employment patterns of post-secondary students, Ottawa: Statistics Canada
Prensky, M. (2001) ‘Digital natives, Digital Immigrants’ On the Horizon Vol. 9, No. 5
Tapscott, D. (2008) Grown Up Digital New York: McGraw Hill
University of British Columbia (2017) 2016/2017 Annual Report on Enrolment Vancouver BC: University of British Columbia
Vygotsky, L. (1978) Mind in Society: Development of Higher Psychological Processes Cambridge MA: Harvard University Press
Activity 6.3 Who are your students?
1. How would you characterise the students you are teaching: full-time students from high school; students who are working part-time; or students working full-time? How would a typical class of yours break down between these three groups? Do you have the information necessary to do this analysis?
2. Do you think students think or study differently these days because of social media? How does that affect their studying? Do you feel you need to respond in some way to this?
3. How much variance is there between your students in prior knowledge and/or language ability? How does this affect the way you teach?
You may want to read Chapter 9, Section 2 and Chapter 10, Section 3 before you answer these questions.
This exercise is mainly for your reflection, but I do have a few comments on these issues in the podcast below:
An audio element has been excluded from this version of the text. You can listen to it online here: https://pressbooks.bccampus.ca/teachinginadigitalagev2/?p=363 | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/06%3A_Building_an_effective_learning_environment/06.3%3A_Learner_characteristics.txt |
Figure 6.4.1 Managing content
6.4.1 The importance of content
For most teachers and instructors, content is often the key focus when designing courses. Content includes facts, ideas, principles, evidence, and descriptions of processes or procedures. A great deal of time is spent on discussing what content should be included in the curriculum, what needs to be covered in a course or a program, what content sources such as text-books students should access, and so on. Teachers and instructors often feel pressured to cover the whole curriculum in the time available. In particular, lecturing or face-to-face classes remain a prime means for organising and delivering content.
The case for balancing content with skills development is made several times through this book, but issues around content remain critically important in teaching. In particular, instructors need to ask themselves these two questions:
• ‘What specific content will add value to the overall goals of this course or program?’
• ‘What content is essential for meeting the learning outcomes for this course, and what desirable but not necessarily obligatory?’
6.4.2 Goals for content
Especially in post-secondary education, instructors tend to take content for granted – this is what we teach. However, it is important, when designing teaching for a digital age, to be clear in our goals for teaching content. Why do we require students to know facts, ideas, principles, evidence, and descriptions of processes or procedures? Is learning specific content a goal in itself, or is it a means to an end? For instance, is there an intrinsic value in knowing the periodic table, or the dates of battles, or are they means to an end, such as designing experiments, or understanding why French is an official language in Canada?
The question is important, because in a digital age, some would argue that learning or memorising content becomes less important or even irrelevant when it is easy just to look up facts or definitions or equations. Cognitivists will argue that content needs to be framed or put in context for it to have meaning. As content is now so easy to access, do we need only to draw on content as and when needed, such as to solve problems, or make decisions? In many cases, of course, skills depend essentially on prior knowledge, so it is not an either/or question.
Probably more important than the teacher or instructor being clear on why content is being taught is for the students to understand this. One way of stating this is to ask: what value is added to the overall goals of this course or program by teaching this specific content? Do students need to memorise this content, or know where to find it, and when it is important to use it? This depends of course on having very clear goals for the course or program as a whole.
6.4.3 Quantity and depth
Figure A.4.2 Is there too much content in your course? Image: © handyguyspodcast.com
In many contexts, instructors have little choice over content. External bodies, such as accreditation agencies, state or provincial governments, or professional licensing boards, may well dictate what content a particular course or program needs to cover. However, the rapid growth of scientific and technological knowledge increasingly challenges the idea of a fixed body of content that students must learn. Engineering and medical programs struggle to cover even in six or eight years of formal education all the knowledge that professionals need to know to practice effectively. Professionals will need to go on learning well past graduation if they are to keep up with new developments in the field.
In particular, covering content quickly or overloading students with content are not effective teaching strategies, because even working harder all waking hours will not enable students in these subject domains to master all the information they need in their professions. Specialization has been a traditional way of handling the growth of knowledge, but that does not help in dealing with complex problems or issues in the real world, which often require inter-disciplinary and broader based approaches. Thus instructors need to develop strategies that enable students to cope with the massive and growing amounts of knowledge in their field.
One way to handle the problem of knowledge explosion is to focus on the development of skills, such as knowledge management, problem-solving and decision-making. However, these skills are not content-free. In order to solve problems or make decisions, you need access to facts, principles, ideas, concepts and data. To manage knowledge, you need to know what content is important and why, where to find it, and how to evaluate it. In particular there may be core or basic knowledge or content that needs to be mastered for many if not most of their professional activities. One teaching skill then will be the ability to differentiate between essential and desirable areas of content, and to ensure that whatever is done to develop skills, in the process core content is covered.
6.4.4 Sources
Another critical decision for teachers in a digital age is where students should source or find content. In medieval times, books were scarce, and the library was an essential source of content not only for students but also for professors. Professors had to select, mediate and filter content because the sources of content were extremely scarce. We are not in that situation today. Content is literally everywhere: on the Internet, in social media, on mass media, in libraries and books, as well as in the lecture theatre.
Often, a great deal of time is spent in departmental or program meetings on discussing what textbooks or articles students should be required to read. Part of the reason for selecting or limiting content is to limit the cost to students, as well as the need to focus on a limited range of material within a course or program. But today, content is increasingly open, free and available on demand over the Internet. Most students will need to continue learning after graduation. They will increasingly resort to digital media for their sources of knowledge. Therefore when deciding on content we should be considering:
(a) to what extent does the instructor need to choose the content for a program (other than a broad set of curriculum topics) and to what extent should students be free to choose both content and the source of that content?
(b) to what extent does the instructor need to deliver content themselves, such as through a lecture or Powerpoint slides, when content is so freely available elsewhere? What is the added value you are providing by delivering the content yourself? Could your time be better used in other ways?
(c) to what extent do we need to provide criteria or guidelines to students for choosing and using openly accessible content, and what is the best way to do that?
When answering such questions, we should also be asking whether our decisions will help students manage content better themselves after graduating.
6.4.5 Structure
One of the most critical supports that teachers and instructors provide is to structure the sequence and inter-relationship of different content elements. I include within structure:
• the selection and sequencing of content,
• developing a particular focus or approach to specific content areas,
• helping students with the analysis, interpretation or application of content
• integrating and relating different content areas.
Traditionally, content has been structured by breaking a course into a number of topic-related classes delivered in a particular sequence, and within the classes, by instructors ‘framing’ and interpreting content. (You can see how this mirrors an industrial manufacturing process). However, new technologies provide alternative means to structure content. Learning management systems such as Blackboard or Moodle still enable instructors to select and sequence content material, but students can access this – and other – content anywhere, at any time – and in any order. The availability of a wide range of content over the Internet, and the ability to collect and sort content through blogs, wikis, and e-portfolios, enable students increasingly to impose their own structures on content.
Students need some form of structure within content areas, partly because some things need to be learned in ‘the right order’, partly because without structure content becomes a jumble of unrelated topics, and partly because students can’t know or work out what is important and what is not within a total content domain, at least until they have started studying it. Novice students in particular need to know what they must study each week. There is a good deal of research evidence to suggest that novice students benefit a great deal from tightly structured, sequential approaches to content, but as they become more knowledgeable or experienced in the domain, they seek to develop their own approaches to the selection, ordering and interpretation of content.
Therefore in deciding on the structure of the content in a course or program instructors need to ask:
(a) how much structure should I provide in managing content, and how much should I leave to the students?
(b) how do new technologies affect the way I should structure the content? Will they enable me to provide more flexible structures that will suit a diverse range of student needs?
Similarly, when answering these questions we should ask how important it is for students themselves to be able to structure content, and whether our answers to the two questions above will further help them to do this.
6.4.6 Learner activities
Lastly, what activities do we need to ask students to do to help them learn content? To answer this question will mean returning to the goals for learning content and the overall goals of the course:
• if memorization is important, then automated tests such as computer-marked assignments with correct answers being provided can be used;
• if the aim is to enable students to draw on content such as facts, principles, data or evidence to construct an argument, to solve equations, or to design an experiment, then opportunities for practising such skills will be needed;
• if the aim is to help students to manage knowledge, then we may need to set tasks that require them to select, evaluate, analyse and apply content.
We shall see that technology enables us to widen considerably the range of activities that students can use to master content, but these need to be related to the learning goals set for the course of program. Without a planned set of activities, though, content may just enter the brain one day and leave it the next.
6.4.7 In conclusion
Even or especially in a digital age, content, in terms of things to know, remains critically important, but in a digital age the role of content is subtly changing, in some ways becoming a means to other ends, such as skills development, rather than an end in itself. Because of the rapid growth in knowledge in nearly all subject areas, being clear about the role and purpose of content in a course, and communicating that effectively to students, becomes particularly important.
Activity 6.4 Managing content
1. Look at the overall content in one of the courses or classes you are teaching.
• How much choice do you have over the content in this course? (In at least two ways: the choice of topics; the way content is approached. For instance often in high schools in many economically advanced countries, the curriculum is decided at a state or provincial level, but within that, teachers have a good deal of freedom about how to teach that curriculum.)
• What purpose does this content serve? Does it have value in its own right or is it there to serve other purposes (such as skills development)?
• What would be the best source of this content for students: textbook, lecture, online search, other, all of these? Why?
• What activities are provided to enable students to learn or apply the content in this course? Given the goals of this course, are the activities appropriate?
• How does the content in this course link to content in related courses (both prior and subsequent to this course)? Is it essential to what follows, does it duplicate what students have covered elsewhere? How do you know this? (e.g. is there a curriculum development process?)
• Given the goals or learning outcomes for this course, what content could be removed without compromising the achievement of these goals?
There is no feedback on this activity. | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/06%3A_Building_an_effective_learning_environment/06.4%3A_Managing_content.txt |
Figure 6.5 Skills
6.5.1 Skills in a digital age
In Chapter 1, Section 1.2, I listed some of the skills that graduates need in a digital age, and argued that consequently a greater focus is now needed on developing such skills, at all levels of education, but particularly at a post-secondary level, where the focus is often on specialised content. Although skills such as critical thinking, problem solving and creative thinking have always been valued in higher education, the identification and development of such skills is often implicit and almost accidental, as if students will somehow pick up these skills from observing faculty themselves demonstrating such skills or through some form of osmosis resulting from the study of content.
It is of course somewhat artificial to separate content from skills, because content is the fuel that drives the development of intellectual skills. My aim here is not to downplay the importance of content, but to ensure that skills development receives as much focus and attention from instructors, and that we approach intellectual skills development in the same rigorous and explicit way as apprentices are trained in manual skills.
6.5.2 Setting goals for skills development
Thus a critical step is to be explicit about what skills a particular course or program is trying to develop, and to define these goals in such a way that they can be implemented and assessed. In other words it is not enough to say that a course aims to develop critical thinking, but to state clearly what this would look like in the context of the particular course or content area, in ways that are clear to students. In particular skills should be defined in such a way that they can be assessed, and students should be aware of the criteria or rubrics that will be used for assessment. Skills development is discussed throughout the book, but particularly in:
6.5.3 Thinking activities
These include activities that enable students to practice a range of skills, such as critical thinking, problem solving, and decision-making. A skill is not binary, in the sense that you either have it or you don’t. There is a tendency to talk about skills and competencies in terms of novice, intermediate, expert, and master, but in reality skills require constant practice and application and there is, at least with regard to intellectual skills, no final destination. With practice and experience, for instance, our critical thinking skills should be much better at 65 than at 25 (although some might call that ‘wisdom’).
A major challenge over a full program is to ensure a steady progression in the level of a skill, so, for instance, a student’s critical thinking skills are better when they graduate than when they started the program. This means identifying what level of skill they have before entering a course, as well as measuring it when they leave. So it is critically important when designing a course or program to design activities that require students to develop, practice and apply thinking skills on a continuous basis, preferably in a way that starts with small steps and leads eventually to larger ones.
There are many ways in which intellectual skills can be developed and assessed, such as written assignments, project work, and focused discussion, but these thinking activities need to be designed, then implemented, on a consistent basis by the instructor.
6.5.4 Practical activities
It is a given in vocational programs that students need lots of practical activities to develop their manual skills. This though is equally true for intellectual skills. Students need to be able to demonstrate where they are along the road to mastery, get feedback on it, and retry as a result. This means doing work that enables them to practice specific skills.
In the history scenario (Scenario D), students had to cover and understand the essential content in the first three weeks, do research in a group, develop an agreed project report, in the form of an e-portfolio, share it with other students and the instructor for comments, feedback and assessment, and present their report orally and online. Ideally, they will have the opportunity to carry over many of these skills into other courses where the skills can be further refined and developed. Thus, with skills development, a longer term horizon than a single course will be necessary, so integrated program as well as course planning is important.
6.5.5 Discussion as a tool for developing intellectual skills
Discussion is a very important tool for developing thinking skills. However, not any kind of discussion. It was argued in Chapter 2 that academic knowledge requires a different kind of thinking to everyday thinking. It usually requires students to see the world differently, in terms of underlying principles, abstractions and ideas.
Thus discussion needs to be carefully managed by the instructor, so that it focuses on the development of skills in thinking that are integral to the area of study. This requires the instructor to plan, structure and support discussion within the class, keeping the discussions in focus, and providing opportunities to demonstrate how experts in the field approach topics under discussion, and comparing students’ efforts. The role of discussion is covered more fully in Chapter 3, Section 4, Chapter 4, Section 4 and Chapter 12, Section 10.
6.5.6 In conclusion
There are many opportunities in even the most academic courses to develop intellectual and practical skills that will carry over into work and life activities in a digital age, without corrupting the values or standards of academia. Even in vocational courses, students need opportunities to practice intellectual or conceptual skills such as problem-solving, communication skills, and collaborative learning. However, this won’t happen merely through the delivery of content. Instructors need to:
• think carefully about exactly what skills their students need;
• how this fits with the nature of the subject matter;
• the kind of activities that will allow students to develop and improve their intellectual skills;
• how to give feedback and to assess those skills, within the time and resources available.
This is a very brief discussion of how and why skills development should be an integral part of any learning environment.
Activity 6.5 Developing skills
1. Returning to the HIST 305 scenario, what specific skills was Ralph Goodyear trying to develop in his course?
2. Are the skills being developed by students in the history scenario relevant to a digital age?
3. Is this section likely to change the way you think about teaching your subject, or do you already cover skills development adequately? If you feel you do cover skills development well, does your approach differ from mine?
For feedback in the first two questions, click on the podcast below.
An audio element has been excluded from this version of the text. You can listen to it online here: https://pressbooks.bccampus.ca/teachinginadigitalagev2/?p=370 | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/06%3A_Building_an_effective_learning_environment/06.5%3A_Developing_skills.txt |
Figure 6.6.1 Learner support
6.6.1 Learner support within a learning environment
Learner support focuses on forms of assistance to learners beyond the delivery of content, skills development, or formal assessment. Learner support covers a wide range of functions, and is discussed throughout the book, but particularly in:
• Chapter 3, Section 7
• Chapter 4, Section 4
• Chapter 9, Section 6
• Chapter 12, Section 10.
Brindley et al. (2004) provide an extensive overview of the full range of activities in providing learner support for online and distance education learners. Here though my focus is limited to indicating why it is an essential element of an effective learning environment, and to describe briefly some of the main sub-components of learner support.
6.6.2 Scaffolding
Figure 6.6.2 Learner support
I use the term scaffolding to cover the many functions in diagnosing and responding to learners’ difficulties, including:
• helping students when they struggle with new concepts or ideas;
• helping students to gain a deeper understanding of a topic or subject;
• helping students to evaluate a range of different ideas or practices;
• helping students to understand the limits of knowledge;
• above all challenging students to go beyond their current level of thinking or practice to acquire deeper understanding or a higher level of competency.
These activities normally take the form of personal interventions and communication between an instructor and an individual or a group of students, in face-to-face contexts or online. These activities tend not to be pre-planned, requiring a good deal of spontaneity and responsiveness on the part of the teacher or instructor.
However, more recently there have been examples of automated learner support, such as virtual assistants or chatbots (for a review of research on chatbots in education, see Winkler and Söllner, 2018). Also learning analytics have been used to determine a student’s performance and where necessary to direct them to further readings or work (see for instance, Vesin et al., 2018).
Scaffolding is usually a means of individualising the learning, enabling student differences in learning to be better accommodated as they occur.
6.6.3 Feedback
This could be seen as a sub-category of scaffolding, but it covers the role of providing feedback on student performance of activities such as writing assignments, project work, creative activities, and other student activities beyond the current and perhaps future scope of automated computer feedback. Again, the instructor’s role here is to provide more individualisation of feedback to deal with more qualitatively assessed student activities, and may or may not be associated with formal assessment or grading.
6.6.4 Counselling
As well as direct support within their academic studying, learners often need help and guidance on administrative or personal issues, such as financial difficulties, or whether to repeat a course, delay an assignment because of sickness in the family, or cancel enrollment in a course and postpone it to another date. Although such services may be available outside the provision of a particular course, this potential source of help needs to be considered in the design of an effective learning environment, with the aim of doing all that can be done to ensure that students can manage external pressures while meeting the academic standards of a program.
6.6.5 Other students
Other students can be a great support for learners. Much of this will happen informally, through students talking after class, through social media, or helping each other with assignments. However, instructors can make more formal use of other students by designing collaborative learning activities, group work, and designing online discussions so that students need to work together rather than individually.
6.6.6 Why learner support is so important
We shall see in Chapter 12 that good design can substantially reduce demand for learner support, by ensuring clarity and by building in appropriate learning activities. Students also vary enormously in their need for support in learning. Many lifelong learners, who have already been through a post-secondary education, have families, careers and a great deal of life experience, can be self-managed, autonomous learners, identifying what they need to learn and how best to do this. At the other extreme, there are students for whom the formal school system was a disaster, who lack basic learning skills or foundations, such as reading, writing and mathematical skills, and therefore lack confidence in learning. These will need a lot of support to succeed.
However the vast majority of learners are somewhere in the middle of the spectrum, occasionally running into problems, unsure what standards are expected, and needing to know how they are doing in their studying. Indeed, there is a good deal of research that indicates that ‘instructor presence’ is associated with student success or failure in a course, at least in online learning (see, for instance, Shea et al., 2010). Where students feel the instructor is not present, both learner performance and completion rates decline. For such students, good, timely learner support is the difference between success and failure.
It should be noted that the need for good learner support, and the ability to provide it, is not dependent on the medium of instruction. The kind of credit online courses that have been designed and delivered long before MOOCs came along often provided high levels of learner support, through having a strong instructor presence and careful design to ensure students were supported.
At the same time, although computer programs can go some way to providing learner support, many of the most important functions of learner support associated with high-level conceptual learning and skills development still need to be provided by an expert teacher or instructor, whether present or at a distance. Furthermore, this kind of learner support is difficult to scale up, as it tends to be relatively labour intensive and requires instructors with a deep level of knowledge within the subject area. Thus, the need to provide adequate levels of learner support cannot just be wished away, if we are to achieve successful learning on a large scale.
This may seem obvious to teachers, but the importance of learner support for student success is not always recognised or appreciated, as can be seen from the design of many MOOCs, and the reaction of politicians and the media to the cost savings promised by the kind of MOOCs that focus on eliminating learner support. There are also different attitudes from instructors and institutions towards the need for learner support. Some faculty may believe that ‘It’s my job to instruct and yours to learn’; in other words, once students are presented with the necessary content through lectures or reading, the rest is up to them.
Nevertheless, the reality is that in any system with a wide diversity of students, as is so common today, effective learner support is essential for student success.
References
Brindley, J., Walti, C. and Zawacki-Richter, O. (eds.) (2004) Learner Support in Open, Distance and Online Learning Environments Oldenburg, Germany: Biblioteks- und informationssystem der Universität Oldenburg
Shea, P. et al. (2010) Online Instructional Effort Measured through the Lens of Teaching Presence in the Community of Inquiry Framework: A Re-Examination of Measures and ApproachInternational Review of Research in Open and Distributed Learning, Vol. 11, No. 3
Vesin, B. at al. (2018) Learning in smart environments: user-centered design and analytics of an adaptive learning systemSmart Learning Environments, Vol. 5, No. 24
Winkler, R. & Söllner, M. (2018): Unleashing the Potential of Chatbots in Education: A State-Of-The-Art Analysis. Academy of Management Annual Meeting (AOM) Chicago: Illinois
Activity 6.6 Building learner support
1. Do you think it is possible to design an effective course or program without the need for high levels of learner support? If so, what would it look like? A development of MOOCs or something completely different?
2. Do you share my views about the limitations of computers for providing the kind of high-level learner support needed for conceptual learning in a digital age? What do computers or AI do well in terms of supporting learners?
3. Is ‘scaffolding’ the best term to describe the kind of learning support I described in that section? If not is there a better term for this?
For my feedback on these questions click on the podcast below:
An audio element has been excluded from this version of the text. You can listen to it online here: https://pressbooks.bccampus.ca/teachinginadigitalagev2/?p=374 | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/06%3A_Building_an_effective_learning_environment/06.6%3A_Learner_support.txt |
Figure 6.7.1 Resources
As in the case of learner characteristics, you may not have a lot of control over the resources available, but resources (or the lack of them) will impact a great deal on the design of teaching. Securing appropriate resources is often one of the most challenging tasks for many teachers and instructors. The role of resources in the design of learning is also discussed throughout the book, but particularly in:
• Chapter 1, Section 5
• Chapter 9, Section 7
• Chapter 10, Section 4.2
• Chapter 12, Section 6
• Chapter 13, Section 3
• Chapter 13, Section 4
Here the focus is just on outlining the overall role of resources in creating an effective learning environment.
6.7.1 Teaching assistance
Teaching assistance is the equivalent for instructors to what learner support is for students. Adjunct or sessional instructors, teaching assistants, librarians, faculty development workshops, and technical support staff, including instructional designers, media producers and IT technical support are all forms of teaching assistance.
It is important to think about the best way to use supporting staff. In universities, the tendency is to chop a large class into sections, with each section with its own sessional instructor or teaching assistant, which then operate relatively independently, with often large differences in the quality of the teaching in different sections, depending on the ability of the teaching assistants. However, new technologies enable the teaching to be organised differently and more consistently.
For instance, a senior professor may determine the overall curriculum and assessment strategy, and working with an instructional designer, provide the overall design of a course. Sessionals and/or teaching assistants then are hired to deliver the course either face-to-face or online or more often a mix of both, under the supervision of the senior professor (see the National Center for Academic Transformation for examples). Flipped classrooms are another way to organise resources differently (see Blended Learning in Introductory Psychology as an example.) One model is for the senior professor to record lectures which students view in their own time, then for students to meet in sub-groups with a teaching assistant or assistants to clarify concepts, discuss topics, or other class activities. These sub-groups may meet either face-to-face or online.
There are also opportunities to increase resources through the use of technology. Online learning may bring in more new students (for instance from outside the normal catchment area) and hence more revenues through government grants for the extra students and/or direct tuition revenue, so there may be economies of scale which would enable the institution to hire more core faculty or sessionals from the extra revenues generated by the additional online students.
Indeed, there are now examples of fully online masters’ programs more than covering their full cost, including the hiring of research professors to teach the program, from tuition revenues alone (the University of British Columbia’s online Master in Educational Technology is one example, even though its tuition fees are the same as those for masters’ programs offered on campus – see Bates and Sangra, 2011).
Thus resources (or the lack of them) can have a profound influence on the effectiveness of a learning environment.
6.7.2 Facilities
Physical facilities available to an instructor and students include classrooms, labs, and the library. These are the more traditional components of a learning environment. However, physical facilities also can constrain the design of learning, because for example the physical set-up of a lecture hall or classroom may limit opportunities for discussion or project work, or an instructor may be forced to organise the teaching around three hours of lecturing and six hours of labs per week, to ‘fit’ with broader institutional requirements for classroom allocations (see How Online Learning is Going to Affect Classroom Design regarding attempts to re-design classrooms for the digital age.)
Online learning can free instructors and students from such rigid physical constraints, but there is still a need for structure and organization of units or modules of teaching, even or especially when teaching online. For instance learning management systems such as Blackboard or Moodle provide a structured online environment, but they too come with their own constraints.
6.7.3 Technology
Classroom technology such as whiteboards, projectors and computers for presentation are traditional technology support. I would also include textbooks here because we will see in Chapter 8 that they are a form of technology. However, the development of new technologies, and especially learning management systems, lecture capture, video streaming, and social media, have radical implications for the design of teaching and learning. This is discussed in much more depth in Chapters 7, 8 and 9, but for the purpose of describing an effective learning environment, the technologies available to an instructor can contribute immensely to creating interactive and engaging learning environments for students. However, it is important to emphasise that technology is just one component within any effective learning environment, and needs to be balanced and integrated with all the other components.
6.7.4 The instructor’s time
This is the greatest and most precious resource of all! Building an effective learning environment is an iterative process, but in the end, the teaching design, and to some extent the learning environment as a whole, will be dependent on the time available from the instructor (and his or her team) for teaching. The less time available, the more restrictive the learning environment is likely to be, unless the instructor’s time is very carefully managed. Again, though, good design takes into account the time available for teaching (see Chapter 12, Section 9 in particular).
6.7.5 Resources, class size and control
Nothing drives an instructor to distraction more than trying to manage with inadequate resources. Certainly, if a teacher or instructor is allocated a class of 200 students, in a large lecture hall, with no additional teaching support, then the instructor is going to have difficulty creating a rich and effective learning environment, because the lack of resources limits the options. On the other hand, an instructor with 30 students, access to a wide range of technology, freedom to organise and structure the curriculum, and with support from an instructional designer and a web designer, has the luxury of exploring a range of different designs and possible learning environments.
Nevertheless it is probably when resources are most scarce that the most creativity is needed to break out of traditional teaching models. New technology, if properly used and available, does enable even large classes with otherwise few resources to be designed with a relatively rich learning environment. This is discussed in more detail in Chapter 13, Section 5. At the same time, expectations need to be realistic. Providing adequate learner support with an instructor:student ratio of 1:200 or more will always be a challenge. Improvements are possible through re-design – but not miracles. (For more on increasing productivity through online teaching, see Productivity and Online Learning Redux.)
References
Bates, A. and Sangrà, A. (2011) Managing Technology in Higher Education: Strategies for Transforming Teaching and Learning San Francisco: Jossey Bass
Activity 6.7 What resources matter?
1. Are there other resources that influence the design of an effective learning environment that I should have included?
2. Winston Churchill once said ‘We shape our buildings and in turn our buildings shape us.’ To what extent do you think online learning can free us of some of the constraints that buildings impose on the design of teaching and learning? What new constraints does online learning bring in terms of design?
3. How do you feel about the whole issue of teaching assistance? I have grave reservations myself about the use of students as teaching assistants in universities, in terms of the quality of the teaching (not so much the principle, but the practice.). I also believe that sessionals and adjunct instructors are badly treated in terms of how they are managed. In British Columbia we have had two Supreme Court cases and a major teachers’ strike over class size and composition in schools, and in particular how much help school teachers should receive for coping with students with learning disabilities. But by bringing in less qualified (and cheaper) support for instructors, do we strengthen or weaken the learning environment for students?
No podcast from me – this activity is for your personal reflection – my views are stated above. | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/06%3A_Building_an_effective_learning_environment/06.7%3A_Resources.txt |
Figure 6.8.1 Assessment
‘I was struck by the way assessment always came at the end, not only in the unit of work but also in teachers’ planning….Assessment was almost an afterthought…
Teachers…are being caught between competing purposes of …assessment and are often confused and frustrated by the difficulties that they experience as they try to reconcile the demands.’
Earle, 2003
6.8.1 Learner assessment in a digital age
Because assessment is a huge topic, it is important to be clear that the purpose of this section is:
(a) to look at one of the components that constitute an effective and comprehensive learning environment, and;
(b) briefly to examine the extent to which assessment is or should be changing in a digital age.
Assessment again is discussed throughout the book, but particularly in:
• Scenario C
• Chapter 5, Section 4.6
• Chapter 8.7 c
• Chapter 11, Section 4.3
• Chapter 12, Section 11.
However, assessment requires a section on its own. Probably nothing drives the behaviour of students more than how they will be assessed. Not all students are instrumental in their learning, but given the competing pressures on students’ time in a digital age, most ‘successful’ learners focus on what will be examined and how they can most effectively meet the assessment requirements (which for most students means in as little time as possible). Therefore decisions about methods of assessment will in most contexts be fundamental to building an effective learning environment.
6.8.2 The purpose of assessment
There are many different reasons for assessing learners. It is important to be clear about the purpose of the assessment, because it is unlikely that one single assessment instrument will meet all assessment needs. Here are some reasons (you can probably think of many more):
• to improve and extend students’ learning;
• to assess students’ knowledge and competence in terms of desired learning goals or outcomes;
• to provide the teacher/instructor with feedback on the effectiveness of their teaching and how it might be improved;
• to provide information for employers about what the student knows and/or can do;
• to filter students for further study, jobs or professional advancement;
• for institutional accountability and/or financial purposes.
I leave it to you to decide the order of importance of these reasons for creating an effective learning environment.
6.8.3 Methods of assessment
The form the assessment takes, as well as the purpose, will be influenced by the instructors’ or examiners’ underlying epistemology: what they believe constitutes knowledge, and therefore how students need to demonstrate their knowledge. The form of assessment should also be influenced by the knowledge and skills that students need in a digital age, which means focusing as much on assessing skills as on assessing knowledge of content. Thus continuous or formative assessment will be as important a consideration as summative or ‘end-of-course’ assessment.
There is a wide range of possible assessment methods. I have selected just a few to illustrate how technology can change the way we assess learners in ways that are relevant to a digital age:
6.8.3.1 No assessment
A question to be considered is whether there is a need for assessment of learning in the first place. There may be contexts, such as a community of practice, where learning is informal, and the learners themselves decide what they wish to learn, and whether they are satisfied with what they have learned. In other cases, learners may not want or need to be formally evaluated or graded, but do want or need feedback on how they are doing with their learning. ‘Do I really understand this?’ or ‘How am I doing compared to other learners?’
However, even in these contexts, some informal methods of assessment by experts, specialists or more experienced participants could help other participants extend their learning by providing feedback and indicating the level of competence or understanding that a participant has achieved or has yet to accomplish. Lastly, students themselves can extend their learning by participating in both self-assessment and peer assessment, preferably with guidance and monitoring from a more knowledgeable or skilled instructor.
6.8.3.2 Computer-based multiple-choice tests
This method is good for testing ‘objective’ knowledge of facts, ideas, principles, laws, and quantitative procedures in mathematics, science and engineering etc., and is cost-effective for these purposes. This form of testing though tends to be limited for assessing higher-level intellectual skills, such as complex problem-solving, creativity, and evaluation, and therefore less likely to be useful for developing or assessing many of the skills needed in a digital age.
6.8.3.3 Written essays or short answers
This method is good for assessing comprehension and some of the more advanced intellectual skills, such as critical thinking, but it is labour intensive, open to subjectivity, and not good for assessing practical skills.
Experiments are taking place with automated essay marking, using developments in artificial intelligence, but so far automated essay marking still struggles to identify valid semantic meaning, especially at a higher education level. For more discussion of automated essay marking, see Chapter 8.7c.4.4
6.8.3.4 Peer assessment
This is a very large and specialised topic, which I touched on in Chapter 5, Section 4.6.2. There are three main advantages of peer assessment:
• if conducted properly, it can be an excellent pedagogical benefit to student learning as it requires students to think critically about what they have learned in order to judge other students’ work. It enables them to see other students’ perspectives on the concepts and ideas, thus widening and deepening their understanding;
• it enables learner support to be scaled up, allowing instructors to handle larger numbers of students;
• it develops a core 21st century skill of peer evaluation that will be critical when working in a digital society.
However, if not done properly, peer assessment can have disastrous consequences. I am not a specialist in this area but I have used peer assessment in online learning, but only at a graduate level. These are some of the lessons I learned:
• There must be an intrinsic benefit to students doing the assessment. They must see how this will be useful to their own learning.
• The instructor must give clear criteria or rubrics for assessment, preferably with examples of good or poor answers.
• Students should be rewarded either with marks or praise by the instructor for excellent peer reviews.
• Students must know that the instructor will not only monitor the peer assessments but also will take responsibility for final decisions on student-awarded grades or marks and will over-rule poor assessments by students.
• Don’t put all your eggs in one basket. It is wise to have a parallel or independent method of assessment, such as multiple-choice tests or having half the total course assessment done in more traditional ways.
Thus there are best practices that must be followed. Anyone intending to use peer assessment should prepare themselves properly by looking carefully into the literature. Macdonald (2015) or Topping (2018) offer guides for teachers. For an example of the successful use of peer assessment at a post-secondary level, see Peer Evaluation as a Learning and Assessment Strategy at the School of Business at Simon Fraser University
6.8.3.5 Project work
Project work encourages the development of authentic skills that require understanding of content, knowledge management, problem-solving, collaborative learning, evaluation, creativity and practical outcomes. Designing valid and practical project work needs a high level of skill and imagination from the instructor, and the assessment process can be labour-intensive, but project work is one of the best ways to assess the high level skills needed in a digital age.
Assessing student project work‘ by Melinda Kolk on The Creative Educator web site provides an excellent guideline on assessing student project work. Although intended for k-12 teachers, it is also very appropriate for post-secondary educators.
6.8.3.6 e-Portfolios (an online compendium of student work)
E-portfolios enable self-assessment through reflection, knowledge management, recording and evaluation of learning activities, such as teaching or nursing practice, and recording of an individual’s contribution to project work (as an example, see the use of e-portfolios in Visual Arts and Built Environment at the University of Windsor.); e-portfolios are usually self-managed by the learner but can be made available or adapted for formal assessment purposes or job interviews.
6.8.3.7 Simulations, educational games (usually online) and virtual worlds
These enable the practice and evaluation of skills, such as:
Figure 6.8.2 Virtual world border crossing, Loyalist College, Ontario
Simulations and serious or educational games (discussed more extensively in Chapter 13) are currently expensive to develop, but cost-effective with multiple use, where they replace the use of extremely expensive equipment, where operational activities cannot be halted for training purposes, or where available as open educational resources. Because students’ actions and decision-making are recorded, authentic assessment is embedded in the process.
6.8.4 In conclusion
Nothing is likely to drive student learning more than the method of assessment. At the same time, assessment methods are rapidly changing and are likely to continue to change. It can be seen that some of these assessment methods are both formative, in helping students to develop and increase their competence and knowledge, as well as summative, in assessing knowledge and skill levels at the end of a course or program. In a digital age, assessment and teaching will become even more closely integrated and contiguous.There is an increasing range of digitally based tools that can enrich the quality and range of student assessment. Therefore the choice of assessment methods, and their relevance to other components, are vital elements of any effective learning environment.
References
Earle, L. (2003) Assessment as Learning Thousand Oaks CA: Corwin Press
Macdonald, B. (2015) Peer assessment that works: A guide for teachers Lanham MD: Rowan and Littlefield
Topping, K. (2108) Using Peer Assessment to Inspire Reflection and Learning London UK: Routledge
Activity 6. 8 What assessments work in a digital age?
1. Are there other methods of assessment relevant to a digital age that I should have included?
2. There is still a heavy reliance on computer-based multiple-choice tests in much teaching, mainly for cost reasons. However, although there are exceptions, I would argue in general that these really don’t assess the high level conceptual skills needed in a digital age. Do you agree?
3. Are there other methods that are equally as economical, particularly in terms of instructor time, that are more suitable for assessment in a digital age? For instance, do you think automated essay grading is a viable alternative?
4. Would it be helpful to think about assessment right at the start of course planning, rather than at the end? Is this feasible?
5. In Scenario D, ‘Developing historical thinking‘, did the instructor use assessment to help develop and assess the skills needed in a digital age in an effective manner? If so, how and if not, why not?
For my comments on this activity, click on the podcast below:
An audio element has been excluded from this version of the text. You can listen to it online here: https://pressbooks.bccampus.ca/teachinginadigitalagev2/?p=380 | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/06%3A_Building_an_effective_learning_environment/06.8%3A_Assessment_of_learning.txt |
Figure 6.9.1 Old Sun Anglican Aboriginal School, Southern Alberta: note the Union Jack on the board at the back.
6.9.1 The importance of culture
Within every learning environment there is a prevailing culture that influences all the other components. In most learning environments, culture is often taken for granted or may be even beyond the consciousness of learners or even teachers. I will try to show why faculty, instructors and teachers should pay special attention to cultural factors, so that they can make conscious decisions about how the different components of a learning environment are implemented. Although the concept of culture may seem a little abstract at this stage, I will show how critical it is for designing an effective online learning environment,
6.9.2 Defining culture
I define culture as
the dominant values and beliefs that influence decision-making.
The choice of content, the skills and attitudes that are promoted, the relationship between instructors and students, and many other aspects of a learning environment, will all be deeply influenced by the prevailing culture of an institution or class (used to mean any grouping of students and a teacher). Thus in a learning environment, every one of the components I described will be influenced by the dominant culture.
For instance, parents tend to place their children in schools that reflect their owns values and beliefs, and so the characteristics of learners in that school will also often be influenced by the culture not only of their parents but also of their school. This is one of the many ways that culture can be self-reinforcing.
6.9.3 Identifying cultures
I first noticed the impact of different cultures many years ago, when I was doing research in the U.K. on the administration of large comprehensive (high) schools. Given that these schools had deliberately been created by a left-of-centre government in Britain in the 1960s to provide equal access to secondary education for all, and that these schools had many things in common (their large size – often with 1,500 students or more, their curricula, the idea that every student should have the same educational opportunities) one would have expected that they all would have had a similar prevailing culture. However, I visited over 50 such schools to collect information on the how they were managed and the key issues they faced, and every one was different.
Some were created from formerly highly selective grammar schools, and operated on a strict system of sorting students by tests, so that each year successful students would go up a level and the ‘weakest’ students would drop down a level, in order to identify the best prospects for university. Here the dominant value was academic excellence.
Some schools were single sex (I am still puzzled by how a school segregated by sex could be considered ‘comprehensive’). One of the key objectives of a girls’ school I visited was to teach girls about ‘poise’. (This led to a very confused miscommunication between me and the headmistress, as I initially thought she had said ‘boys’.) Here the dominant value was on developing ‘ladylike qualities’.
Others were inner city schools, where the focus was often on bringing the best out of each child, whatever their abilities. In such schools, each class would contain children with as wide a range of abilities as possible, but they were often rowdy, raucous places in comparison to the more elite-oriented institutions. Here the emphasis was on inclusiveness and equal opportunity.
The differing cultures of each of these schools was so strong I could sometimes detect it just by walking in the door, by the way students reacted with staff and each other in the corridors, or even by the way the students walked (or ran).
6.9.4 Culture and learning environments
Whether you consider culture to be a good or bad influence in a learning environment will depend on whether you share or reject the underlying values and beliefs of the dominant culture.
Residential schools in Canada into which aboriginal children were often forcibly placed are a prime example of how culture drives the way schools operate. The main purpose of such schools was deliberately to destroy aboriginal cultures and replace them with a religious-influenced Western culture. In these schools children were punished for being what they were. In such schools, all the other components of their learning environment were used to reinforce the dominant culture that was being imposed.
Although the outcomes for most children that attended these schools have turned out to be disastrous, those responsible (state and church working together) truly believed they were doing the right thing. We are still struggling in Canada to ‘do the right thing’ for aboriginal education, but any successful solution must take into account aboriginal cultures, as well as the surrounding predominant ‘Western’ culture.
Culture is perhaps more nebulous in higher education institutions, but it is still a powerful influence, differing not just between institutions but often between academic departments within the same institution.
6.9.5 Culture and new learning environments
Because prevailing cultures are often so dominant, they are very difficult to change. It is particularly difficult for a single individual to change a dominant culture. Even charismatic leaders will struggle, as many university presidents have found.
However, as new technologies allow us to develop new learning environments, instructors now have a rare opportunity consciously to create a culture that can support those values and beliefs that they consider to be important for today’s learners.
For instance, in an online learning environment, I consciously attempt to create a culture that reflects the following:
• mutual respect (between instructor and students, and especially between students)
• open-ness to differing views and opinions; respect for diversity
• evidence-based argument and reasoning
• making learning engaging and fun
• making explicit and encouraging the underlying values and epistemology of a subject discipline
• transparency in assessment (e.g. rubrics and criteria)
• recognition of and respect for the personalities of each student in the class
• collaboration and mutual support.
The above cultural elements of course reflect my beliefs and values; yours may well be different. However, it is important that you are aware of your beliefs and values, so that you can design the learning environment in a way that best supports them.
You may also consider these cultural elements to be more like learning outcomes but I disagree. These cultural elements are broader and more general, and reflect what I believe are really necessary conditions for building an effective learning environment in a digital age.
Lastly you may question the right of an instructor to impose their personal cultural conditions on a learning environment. For myself, I have no problems with this. As a subject expert or professional in teaching, you are usually in a better position than learners to know the learning requirements and the cultural elements that will best achieve these. In any case, if you believe that learners should have more say in determining the culture in which they learn, that too is your choice and could be accommodated within the culture.
6.9.6 Summary
Culture is a critical component of any learning environment. It is important to be aware of the influence of culture within any particular learning context, and to try and shape that culture as much as possible towards supporting the kind of learning environment that you believe will be most effective. However, changing a pre-existing, dominant culture is very difficult. Nevertheless, new technologies enable new learning environments to be developed, and thus provide an opportunity to develop the kind of culture within that learning environment that will best serve your learners.
However, in every learning environment there will be cultural elements that prevail through all components, which is why I have added culture as a background to all the components of a learning environment in the graphic below.
Figure 6.9.2: All the components of an effective learning environment
6.9.7 Next
Section 6.10 provides a brief conclusion to this chapter on building effective learning environments.
Activity 6.9 Considering culture in a learning environment
1. Do you agree with my definition of ‘culture’ as used in describing an effective learning environment? If not, how would you define it? Would you use another term for what I am discussing?
2. Can you describe the culture of the institution in which you work? What are its prime characteristics or goals? Or are there many cultures?
3. Can you describe the culture within your own class or classes? What do you ‘inherit’ and what can you create or change?
4. Do you share my views on the importance of understanding the culture within a learning environment? Or is culture something a teacher should/can ignore?
5. What would be the ideal culture for your classes/teaching? How could you foster or create such a culture?
These questions are for your reflection. There is no feedback provided for this activity. | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/06%3A_Building_an_effective_learning_environment/06.9%3A_Culture_and_learning_environments.txt |
Figure 7.1 How many technologies can you identify in this home entertainment system? Image: Tony Bates, 2014
7.1 Defining the role of technology in education
Even an electronics engineer will be hard pressed to identify all the technologies in the photo of a not untypical home entertainment system in a North American home in 2014. The answer will depend on what you mean by technology:
• hardware? (e.g. TV monitor, laptop computer)
• software? (e.g. computer operating system, channel selection)
• networks? (e.g. Internet, cable)
• services? (e.g. television, telephone)
The answer of course is all these, plus the systems that enable everything to be integrated. Indeed, the technologies represented in just this one photograph are too many to list (although I make an attempt in the feedback on Activity 7.1 at the end of the book. Nevertheless it is a futile exercise as I was forced to change the whole system a couple of years later due to technological ‘upgrades’ by the service provider.)
In a digital age we are immersed in technology. Education, although often a laggard in technology adoption, is nevertheless no exception today. Yet learning is also a fundamental human activity that can function quite well (some would say better) without any technological intervention. So in an age immersed in technology, what is its role in education? What are the strengths (or affordances) and what are the limitations of technology in education? When should we use technology, and which technologies should we use for what purposes?
7.2 The need for decision models
The aim of this and the next two chapters is to provide some frameworks or models for decision-making that are both soundly based on theory and research and are also pragmatic within the context of education. This will not be an easy exercise. There are deep philosophical, technical and pragmatic challenges in trying to provide a model or set of models flexible but practical enough to handle the complexity.
For instance, theories and beliefs about education will influence strongly the choice and use of different technologies. On the technical side, it is becoming increasingly difficult to classify or categorize technologies, not just because they are changing so quickly, but also because technologies have many different qualities and affordances that change according to the contexts in which they are used. On the pragmatic side, it would be a mistake to focus solely on the pedagogical characteristics of technologies. There are social, organizational, cost and accessibility issues also to be considered.
The selection and use of technologies for teaching and learning is driven as much by context and values and beliefs as by hard scientific evidence or rigorous theory. So there will not be one ‘best’ framework or model. On the other hand, given the rapidly escalating range of technologies, educators are increasingly caught between technological determinism (inappropriate applications of artificial intelligence, for instance)or the total rejection of technology for teaching because it is so complex.Thus we need some models to guide their selection and use.
We shall also see though that even with such models or frameworks for decision-making, there are in fact still some fundamental, unanswered questions regarding the use of technology for teaching, including:
• what is best done face-to-face and what online, and in what contexts?
• what is the role of the human teacher, and can/should/will the human teacher be replaced by technology?
Nevertheless, if we consider a teacher facing a group of students and a curriculum to teach, or a learner seeking to develop their own learning, there is need for practical guidance now about when to use one technology or another. In this and the next two chapters I will provide some theoretical models or frameworks that will enable such questions to be answered effectively and pragmatically so that the learning experience is optimized.
In the meantime let’s start with what your views are at the moment about choosing technology for teaching and learning.
Activity 6.1 How do you currently make decisions about what technology to use for teaching?
1. How do you decide at the moment about what technologies to use for teaching?
• Use what’s in the room?
• Ask the IT support people?
• Use a theory or set of principles for making such a decision? If so, what are these?
2. Is justifying your use of technology (or lack of it) in teaching easy to do? What are the reasons for your answer?
3. How many technologies can you see in Figure 7.1? List them
For my answer to question 3, see Feedback on Activity 7.1 at the end of the book. There is no feedback on questions 1 and 2. | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/07%3A_Understanding_technology_in_education/07.1%3A_Choosing_technologies_for_teaching_and_learning%3A_the_challenge.txt |
Figure 7.2.1 Charlton Heston as Moses. Are the tablets of stone an educational technology? (See Selwood, 2014, for a discussion of the possible language of the Ten Commandments)
Image: Allstar/Cinetext/Paramount
Arguments about the role of technology in education go back at least 2,500 years. To understand better the role and influence of technology on teaching, we need a little history, because as always there are lessons to be learned from history. Paul Saettler’s ‘The Evolution of American Educational Technology‘ (1990) is one of the most extensive historical accounts, but only goes up to 1989. A lot has happened since then. What I’m giving you here is the postage stamp version of ed tech history, and a personal one at that.
7.2.1 Oral communication
One of the earliest means of formal teaching was oral – through human speech – although over time, technology has been increasingly used to facilitate or ‘back-up’ oral communication. In ancient times, stories, folklore, histories and news were transmitted and maintained through oral communication, making accurate memorization a critical skill, and the oral tradition is still the case in many aboriginal cultures. For the ancient Greeks, oratory and speech were the means by which people learned and passed on learning. Homer’s Iliad and the Odyssey were recitative poems, intended for public performance. To be learned, they had to be memorized by listening, not by reading, and transmitted by recitation, not by writing. Lectures go back at least as far as the ancient Greeks. Demosthenes (384-322 BC) was an outstanding orator whose speeches influenced the politics of Athens.
Nevertheless, by the fifth century B.C, written documents existed in considerable numbers in ancient Greece. If we believe Plato, education has been on a downward spiral ever since. According to Plato, Socrates caught one of his students (Phaedrus) pretending to recite a speech from memory that in fact he had learned from a written version. Socrates then told Phaedrus the story of how the god Theuth offered the King of Egypt the gift of writing, which would be a ‘recipe for both memory and wisdom’. The king was not impressed. According to the king:
it [writing] will implant forgetfulness in their souls; they will cease to exercise memory because they will rely on what is written, creating memory not from within themselves, but by means of external symbols. What you have discovered is a recipe not for memory, but for reminding. And it is no true wisdom that you offer your disciples, but only its semblance, for by telling them many things without teaching them anything, you will make them seem to know much, while for the most part they will know nothing. And as men filled not with wisdom but the conceit of wisdom, they will be a burden to their fellow men.
Phaedrus, 274c-275, translation adapted from Manguel, 1996
I can just hear some of my former colleagues saying the same thing about social media.
Slate boards were in use in India in the 12th century AD, and blackboards/chalkboards became used in schools around the turn of the 18th century. At the end of World War Two the U.S. Army started using overhead projectors for training, and their use became common for lecturing, until being largely replaced by electronic projectors and presentational software such as Powerpoint around 1990. This may be the place to point out that most technologies used in education were not developed specifically for education but for other purposes (mainly for the military or business.)
Although the telephone dates from the late 1870s, the standard telephone system never became a major educational tool, not even in distance education, because of the high cost of analogue telephone calls for multiple users, although audio-conferencing has been used to supplement other media since the 1970s. Video-conferencing using dedicated cable systems and dedicated conferencing rooms have been in use since the 1980s. The development of video compression technology and relatively low cost video servers in the early 2000s led to the introduction of lecture capture systems for recording and streaming classroom lectures in 2008. Webinars now are used largely for delivering lectures over the Internet.
None of these technologies though changes the oral basis of communication for teaching.
7.2.2 Written communication
The role of text or writing in education also has a long history. According to the Bible, Moses used chiseled stone to convey the ten commandments in a form of writing, probably around the 7th century BC. Even though Socrates is reported to have railed against the use of writing, written forms of communication make analytic, lengthy chains of reasoning and argument much more accessible, reproducible without distortion, and thus more open to analysis and critique than the transient nature of speech.
The invention of the printing press in Europe in the 15th century was a truly disruptive technology, making written knowledge much more freely available, very much in the same way as the Internet has done today. As a result of the explosion of written documents resulting from the mechanization of printing, many more people in government and business were required to become literate and analytical, which led to a rapid expansion of formal education in Europe. There were many reasons for the development of the Renaissance and the Enlightenment, and the triumph of reason and science over superstition and beliefs in Europe, but the technology of printing was a key agent of change.
Improvements in transport infrastructure in the 19th century, and in particular the creation of a cheap and reliable postal system in the 1840s, led to the development of the first formal correspondence education, with the University of London offering an external degree program by correspondence from 1858. This first formal distance degree program still exists today in the form of the University of London Worldwide. In the 1970s, the Open University transformed the use of print for teaching through specially designed, highly illustrated printed course units that integrated learning activities with the print medium, based on advanced instructional design.
With the development of web-based learning management systems in the mid-1990s, textual communication, although digitized, became, at least for a brief time, the main communication medium for Internet-based learning, although lecture capture and video streaming is now changing that.
7.2.3 Broadcasting and video
Figure 7.2.3 BBC television studio and radio transmitter, Alexandra Palace, London
Image: © Copyright Oxyman and licensed for reuse under a Creative Commons Licence
The British Broadcasting Corporation (BBC) began broadcasting educational radio programs for schools in the 1920s. The first adult education radio broadcast from the BBC in 1924 was a talk on Insects in Relation to Man, and in the same year, J.C. Stobart, the new Director of Education at the BBC, mused about ‘a broadcasting university’ in the journal Radio Times (Robinson, 1982). Television was first used in education in the 1960s, for schools and for general adult education (one of the six purposes in the current BBC’s Royal Charter is still ‘promoting education and learning’).
In 1969, the British government established the Open University (OU), which worked in partnership with the BBC to develop university programs open to all, using a combination originally of printed materials specially designed by OU staff, and television and radio programs made by the BBC but integrated with the courses. Although the radio programs involved mainly oral communication, the television programs did not use lectures as such, but focused more on the common formats of general television, such as documentaries, demonstration of processes, and cases/case studies (see Bates, 1984). In other words, the BBC focused on the unique ‘affordances’ of television, a topic that will be discussed in much more detail later. Over time, as new technologies such as audio- and video-cassettes were introduced, live broadcasting, especially radio, was cut back for OU programs, although there are still some general educational channels broadcasting around the world (e.g. TVOntario in Canada; PBS, the History Channel, and the Discovery Channel in the USA).
The use of television for education quickly spread around the world, being seen in the 1970s by some, particularly in international agencies such as the World Bank and UNESCO, as a panacea for education in developing countries, the hopes for which quickly faded when the realities of lack of electricity, cost, security of publicly available equipment, climate, resistance from local teachers, and local language and cultural issues became apparent (see, for instance, Jamison and Klees, 1973). Satellite broadcasting started to become available in the 1980s, and similar hopes were expressed of delivering ‘university lectures from the world’s leading universities to the world’s starving masses’, but these hopes too quickly faded for similar reasons. However, India, which had launched its own satellite, INSAT, in 1983, used it initially for delivering locally produced educational television programs throughout the country, in several indigenous languages, using Indian-designed receivers and television sets in local community centres as well as schools (Bates, 1984).
In the 1990s the cost of creating and distributing video dropped dramatically due to digital compression and high-speed Internet access. This reduction in the costs of recording and distributing video also led to the development of lecture capture systems. The technology allows students to view or review lectures at any time and place with an Internet connection. The Massachusetts Institute of Technology (MIT) started making its recorded lectures available to the public, free of charge, via its OpenCourseWare project, in 2002. YouTube started in 2005 and was bought by Google in 2006. YouTube is increasingly being used for short educational clips that can be downloaded and integrated into online courses. The Khan Academy started using YouTube in 2006 for recorded voice-over lectures using a digital blackboard for equations and illustrations. Apple Inc. in 2007 created iTunesU to became a portal or a site where videos and other digital materials on university teaching could be collected and downloaded free of charge by end users.
Until lecture capture arrived, learning management systems had integrated basic educational design features, but this required instructors to redesign their classroom-based teaching to fit the LMS environment. Lecture capture on the other hand required no changes to the standard lecture model, and in a sense reverted back to primarily oral communication supported by Powerpoint or even writing on a chalkboard. Thus oral communication remains as strong today in education as ever, but has been incorporated into or accommodated by new technologies.
7.2.4 Computer technologies
7.2.4.1 Computer-based learning
In essence the development of programmed learning aims to computerize teaching, by structuring information, testing learners’ knowledge, and providing immediate feedback to learners, without human intervention other than in the design of the hardware and software and the selection and loading of content and assessment questions. B.F. Skinner started experimenting with teaching machines that made use of programmed learning in 1954, based on the theory of behaviourism (see Chapter 2, Section 3). Skinner’s teaching machines were one of the first forms of computer-based learning. There has been a recent revival of programmed learning approaches as a result of MOOCs, since machine based testing scales much more easily than human-based assessment.
PLATO was a generalized computer assisted instruction system originally developed at the University of Illinois, and, by the late 1970s, comprised several thousand terminals worldwide on nearly a dozen different networked mainframe computers. PLATO was a highly successful system, lasting almost 40 years, and incorporated key on-line concepts: forums, message boards, online testing, e-mail, chat rooms, instant messaging, remote screen sharing, and multi-player games.
Attempts to replicate the teaching process through artificial intelligence (AI) began in the mid-1980s, with a focus initially on teaching arithmetic. Despite large investments of research in AI for teaching over the last 30 years, the results generally have been disappointing. It has proved difficult for machines to cope with the extraordinary variety of ways in which students learn (or fail to learn.) Recent developments in cognitive science and neuroscience are being watched closely but at the time of writing the gap is still great between the basic science, and analysing or predicting specific learning behaviours from the science.
More recently we have seen the development of adaptive learning, which analyses learners’ responses then re-directs them to the most appropriate content area, based on their performance. Learning analytics, which also collects data about learner activities and relates them to other data, such as student performance, is a related development. These developments will be discussed in further detail in Section 7.7.
7.2.4.2 Computer networking
Arpanet in the U.S.A was the first network to use the Internet protocol in 1982. In the late 1970s, Murray Turoff and Roxanne Hiltz at the New Jersey Institute of Technology were experimenting with blended learning, using NJIT’s internal computer network. They combined classroom teaching with online discussion forums, and termed this ‘computer-mediated communication’ or CMC (Hiltz and Turoff, 1978). At the University of Guelph in Canada, an off-the-shelf software system called CoSy was developed in the 1980s that allowed for online threaded group discussion forums, a predecessor to today’s forums contained in learning management systems. In 1988, the Open University in the United Kingdom offered a course, DT200, that as well as the OU’s traditional media of printed texts, television programs and audio-cassettes, also included an online discussion component using CoSy. Since this course had 1,200 registered students, it was one of the earliest ‘mass’ open online courses. We see then the emerging division between the use of computers for automated or programmed learning, and the use of computer networks to enable students and instructors to communicate with each other.
The Word Wide Web was formally launched in 1991. The World Wide Web is basically an application running on the Internet that enables ‘end-users’ to create and link documents, videos or other digital media, without the need for the end-user to transcribe everything into some form of computer code. The first web browser, Mosaic, was made available in 1993. Before the Web, it required lengthy and time-consuming methods to load text, and to find material on the Internet. Several Internet search engines have been developed since 1993, with Google, created in 1999, emerging as one of the primary search engines.
7.2.4.3 Online learning environments
In 1995, the Web enabled the development of the first learning management systems (LMSs), such as WebCT (which later became Blackboard). LMSs provide an online teaching environment, where content can be loaded and organized, as well as providing ‘spaces’ for learning objectives, student activities, assignment questions, and discussion forums. The first fully online courses (for credit) started to appear in 1995, some using LMSs, others just loading text as PDFs or slides. The materials were mainly text and graphics. LMSs became the main means by which online learning was offered until lecture capture systems arrived around 2008.
By 2008, George Siemens, Stephen Downes and Dave Cormier in Canada were using web technology to create the first ‘connectivist’ Massive Open Online Course (MOOC), a community of practice that linked webinar presentations and/or blog posts by experts to participants’ blogs and tweets, with just over 2,000 enrollments. The courses were open to anyone and had no formal assessment. In 2012, two Stanford University professors launched a lecture-capture based MOOC on artificial intelligence, attracting more than 100,000 students, and since then MOOCs have expanded rapidly around the world.
7.2.5 Social media
Social media are really a sub-category of computer technology, but their development deserves a section of its own in the history of educational technology. Social media cover a wide range of different technologies, including blogs, wikis, You Tube videos, mobile devices such as phones and tablets, Twitter, Skype and Facebook. Andreas Kaplan and Michael Haenlein (2010) define social media as
a group of Internet-based applications that …allow the creation and exchange of user-generated content, based on interactions among people in which they create, share or exchange information and ideas in virtual communities and networks.
Social media are strongly associated with young people and ‘millenials’ – in other words, many of the students in post-secondary education. At the time of writing social media are only just being integrated into formal education, and to date their main educational value has been in non-formal education, such as fostering online communities of practice, or around the edges of classroom teaching, such as ‘tweets’ during lectures or rating of instructors. It will be argued though in Chapters 8, 9 and 10 that they have much greater potential for learning.
7.2.6 A paradigm shift
It can be seen that education has adopted and adapted technology over a long period of time. There are some useful lessons to be learned from past developments in the use of technology for education, in particular that many claims made for a newly emerging technology are likely to be neither true nor new. Also new technology rarely completely replaces an older technology. Usually the old technology remains, operating within a more specialised ‘niche’, such as radio, or integrated as part of a richer technology environment, such as video in the Internet.
However, what distinguishes the digital age from all previous ages is the rapid pace of technology development and our immersion in technology-based activities in our daily lives. Thus it is fair to describe the impact of the Internet on education as a paradigm shift, at least in terms of educational technology. We are still in the process of absorbing and applying the implications. The next section attempts to pin down more closely the educational significance of different media and technologies.
Activity 7.2 What does history tell us?
1. What constitutes an educational technology? How would you classify a recorded lecture from MIT that is accessed as an open educational resource? When is a technology educational and not just a technology?
2. An early version of the Internet (Arpanet) existed long before 1990, but the combination of Internet protocols and the development of html and the World Wide Web were clearly a turning point in both telecommunications and education (at least for me). What then makes the Internet/the Web a paradigm shift? Or are they just an evolution, an orderly next step in the development of technology?
3. Is writing a technology? Is a lecture a technology? Does it matter to decide this?
4. The more sharp eyed or analytical of you may be asking questions about the categorization or definition of some of the technologies listed above (quite apart from the issue of how to deal with people as a means of communication). For instance computer-mediated communication (CMC) existed before the Internet (from 1978 in fact), but isn’t it an Internet technology? (It is now, but wasn’t then.) How do social media differ from CMC? Does it make sense to distinguish television technologies such as broadcast, cable, satellite, DVDs or video-conferencing, and is this relevant any more? If so, what distinguishes them and what do they have in common from an educational perspective?
These are some of the issues that will become clearer in the following sections. | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/07%3A_Understanding_technology_in_education/07.2%3A_A_short_history_of_educational_technology.txt |
Figure 7.3.1 A book: medium or technology?
7.3.1. Defining media and technology
Philosophers and scientists have argued about the nature of media and technologies over a very long period. The distinction is challenging because in everyday language use, we tend to use these two terms interchangeably. For instance, television is often referred to as both a medium and a technology. Is the Internet a medium or a technology? And does it matter?
I will argue that there are differences, and it does matter to distinguish between media and technology, especially if we are looking for guidelines on when and how to use them. There is a danger, particularly in education, in looking too much at the raw technology, and not enough at the personal, social and cultural contexts in which we use technology. The terms ‘media’ and ‘technology’ represent different ways altogether of thinking about the choice and use of technology in teaching and learning.
7.3.2 Technology
There are many definitions of technology (see Wikipedia for a good discussion of this). Essentially definitions of technology range from the basic notion of tools, to systems which employ or exploit technologies. Thus
• technology refers to tools and machines that may be used to solve real-world problems‘ is a simple definition;
• the current state of humanity’s knowledge of how to combine resources to produce desired products, to solve problems, fulfill needs, or satisfy wants‘ is a more complex and grandiose definition (and has a smugness about it that I think is undeserved – technology often does the opposite of satisfy wants, for instance.).
In terms of educational technology we have to consider a broad definition of technology. The technology of the Internet involves more than just a collection of tools, but a system that combines computers, telecommunications, software and rules and procedures or protocols. However, I baulk at the very broad definition of the ‘current state of humanity’s knowledge‘. Once a definition begins to encompass many different aspects of life it becomes unwieldy and ambiguous.
I tend to think of technology in education as things or tools used to support teaching and learning. Thus computers, software programs such as a learning management system, or a transmission or communications network, are all technologies. A printed book is a technology. Technology often includes a combination of tools with particular technical links that enable them to work as a technology system, such as the telephone network or the Internet.
However, for me, technologies or even technological systems do not of themselves communicate or create meaning. They just sit there until commanded to do something or until they are activated or until a person starts to interact with the technology. At this point, we start to move into media.
Figure 7.3.2 Don’t just sit there – DO something!
Image: © Alex Dawson, Flickr, 2006
7.3.3 Media
Media (plural of medium) is another word that has many definitions.
The word ‘medium’ comes from the Latin, meaning in the middle (a median) and also that which intermediates or interprets. Media require an active act of creation of content and/or communication, and someone who receives and understands the communication, as well as the technologies that carry the medium.
The term ‘media’ has two distinct meanings relevant for teaching and learning, both of which are different from definitions of technology
7.3.3.1 Media linked to senses and ‘meaning’.
We use our senses, such as sound and sight, to interpret media. In this sense, we can consider text, graphics, audio and video as media ‘channels’, in that they intermediate ideas and images that convey meaning. Every interaction we have with media, in this sense, is an interpretation of reality, and again usually involves some form of human intervention, such as writing (for text), drawing or design for graphics, talking, scripting or recording for audio and video. Note that there are two types of intervention in media: by the ‘creator’ who constructs information, and by the ‘receiver’, who must also interpret it.
Media of course depend on technology, but technology is only one element of media. Thus we can think of the Internet as merely a technological system, or as a medium that contains unique formats and symbol systems that help convey meaning and knowledge. These formats, symbol systems and unique characteristics of a particular medium (e.g. the 280 character limit in Twitter) are deliberately created and need to be interpreted by both creators and end users. Furthermore, at least with the Internet, people can be at the same time both creators and interpreters of knowledge.
Computing can also be considered a medium in this context. I use the term computing, not computers, since although computing uses computers, computing involves some kind of intervention, construction and interpretation. Computing as a medium would include coding, animations, online social networking, using a search engine, or designing and using simulations. Thus Google uses a search engine as its primary technology, but I classify Google as a medium, since it needs content and content providers, and an end user who defines the parameters of the search, in addition to the technology of computer algorithms to assist the search. Thus the creation, communication and interpretation of meaning are added features that turn a technology into a medium.
In terms of representing knowledge it is useful to think of the following media for educational purposes within which there are sub-systems (only some examples given):
• Text: textbooks, novels, poems
• Graphics: diagrams, photographs, drawings, posters, graffiti
• Audio: sounds, speech, podcasts, radio programs
• Video and film: television programs, movies, YouTube clips, ‘talking heads’
• Computing: animation, simulations, online discussion forums, virtual worlds.
Furthermore, within these sub-systems there are ways of influencing communication through the use of unique symbol systems, such as story lines and use of characters in novels, composition in photography, voice modulation to create effects in audio, cutting and editing in film and television, and the design of user interfaces or web pages in computing. The study of the relationship between these different symbol systems and the interpretation of meaning is a whole field of study in itself, called semiotics.
In education we could think of classroom teaching as a medium. Technology or tools are used (e.g. chalk and blackboards, or Powerpoint and a projector) but the key component is the intervention of the teacher and the interaction with the learners in real time and in a fixed time and place. We can also then think of online teaching as a different medium, with computers, the Internet (in the sense of the communication network) and a learning management system as core technologies, but it is the interaction between teachers, learners and online resources within the unique context of the Internet that are the essential component of online learning.
From an educational perspective, it is important to understand that media are not neutral or ‘objective’ in how they convey knowledge. They can be designed or used in such a way as to influence (for good or bad) the interpretation of meaning and hence our understanding. Some knowledge therefore of how media work is essential for teaching in a digital age. In particular we need to know how best to design and apply media (rather than technology) to facilitate learning.
Over time, media have become more complex, with newer media (e.g. television) incorporating some of the components of earlier media (e.g. audio) as well as adding another medium (video). Digital media and the Internet increasingly are incorporating and integrating all previous media, such as text, audio, and video, and adding new media components, such as animation, simulation, and interactivity. When digital media incorporate many of these components they become ‘rich media’. Thus one major advantage of the Internet is that it encompasses all the representational media of text, graphics, audio, video and computing.
7.3.3.2 Media as organisations
The second meaning of media is broader and refers to the industries or significant areas of human activity that are organized around particular technologies, for instance film and movies, television, publishing, and the Internet. Within these different media are particular ways of representing, organizing and communicating knowledge.
Thus for instance within television there are different formats, such as news, documentaries, game shows, action programs, while in publishing there are novels, newspapers, comics, biographies, and so on. Sometimes the formats overlap but even then there are symbol systems within a medium that distinguish it from other media. For instance in movies there are cuts, fades, close-ups, and other techniques that are markedly different from those in other media. All these features of media bring with them their own conventions and assist or change the way meaning is extracted or interpreted.
Lastly, there is a strong cultural context to media organisations. For instance, Schramm (1972) found that broadcasters often have a different set of professional criteria and ways of assessing ‘quality’ in an educational broadcast from those of educators (which made my job of evaluating the programs the BBC made for the Open University very interesting). Today, this professional ‘divide’ can be seen between the differences between computer scientists and educators in terms of values and beliefs with regard to the use of technology for teaching. At its crudest, it comes down to issues of control: who is in charge of using technology for teaching? Who makes the decisions about the design of a MOOC or the use of an animation?
7.3.4 The affordances of media
Figure 7.3.3 Graphs can represent, in a different way, the same concepts as written descriptions or formulae. Understanding the same thing in different ways generally leads to deeper understanding.
Image: © Open University 2013
Different media have different educational effects or affordances. If you just transfer the same teaching to a different medium, you fail to exploit the unique characteristics of that medium. Put more positively, you can do different and often better teaching by adapting it to the medium. That way students will learn more deeply and effectively. To illustrate this, let’s look at an example from early on in my career as a researcher in educational media.
7.3.4.1 A personal story
In 1969, I was appointed as a research officer at the Open University in the United Kingdom. At this point the university had just received its royal charter. I was the 20th member of staff appointed. My job was to research into the pilot programs being offered by the National Extension College, which was delivering low cost non-credit distance education programs in partnership with the BBC. The NEC was ‘modelling’ the kind of integrated multimedia courses, consisting of a mix of print and broadcast radio and TV, that were to be offered by the Open University when it started.
My colleague and I sent out questionnaires by mail on a weekly basis to students taking the NEC courses. The questionnaire contained both pre-coded responses, and the opportunity for open-ended comments, and asked students for their responses to the print and broadcast components of the courses. We were looking for what worked and what didn’t work in designing multimedia distance education courses.
When I started analyzing the questionnaires, I was struck particularly by the ‘open-ended’ comments in response to the television and radio broadcasts. Responses to the printed components tended to be ‘cool’: rational, calm, critical, constructive. The responses to the broadcasts were the opposite: ‘hot’, emotional, strongly supportive or strongly critical or even hostile, and rarely critically constructive. Something was going on here.
7.3.4.2 Findings from the research: how media differ
The initial discovery that different media affected students differently came very quickly, but it took longer to discover in what ways media are different, and even longer why, but here are some of the discoveries made by my colleagues and me in the Audio-Visual Media Research Group at the OU (Bates, 1984):
• the BBC producers (all of whom had a degree in the subject area in which they were making programs) thought about knowledge differently from the academics with whom they were working. In particular, they tended to think more visually and more concretely about the subject matter. Thus they tended to make programs that showed concrete examples of concepts or principles in the texts, applications of principles, or how academic concepts worked in real life. Academic learning is about abstraction and higher order levels of thinking. However, abstract concepts are better understood if they can be related to concrete or empirical experiences, from which, indeed, abstract concepts are often drawn. The television programs enabled learners to move backwards and forwards between the abstract and the concrete. Where this was well designed, it really helped a large number of students – but not all;
• students responded very differently to the TV programs in particular. Some loved them, some hated them, and few were indifferent. The ones that hated them wanted the programs to be didactic and repeat or reinforce what was in the printed texts. Interestingly though the TV-haters tended to get lower grades or even fail in the final course exam. The ones that loved the TV programs tended to get higher grades. They were able to see how the programs illustrated the principles in the texts, and the programs ‘stretched’ these students to think more widely or critically about the topics in the course. The exception was math, where borderline students found the TV programs most helpful;
• the BBC producers rarely used talking heads or TV lectures. With radio and later audio-cassettes, some producers and academics integrated the audio with texts, for instance in mathematics, using a radio program and later audio-cassettes to talk the students through equations or formulae in the printed text (similar to Khan Academy lectures today on video);
• using television and radio to develop higher level learning is a skill that can be taught. In the initial foundation (first year) social science course (D100), many of the programs were made in a typical BBC documentary style. Although the programs were accompanied by extensive broadcast notes that attempted to link the broadcasts to the academic texts, many students struggled with these programs. When the course was remade five years later a distinguished academic (Stuart Hall) was used as an ‘anchor’ for all the programs. The first few programs were somewhat like lectures, but in each program Stuart Hall introduced more and more visual clips and helped students analyze each clip. By the end of the course the programs were almost entirely in the documentary format. Students rated the remade programs much higher and used examples from the TV programs much more in their assignments and exams for the remade course.
7.3.4.3 Why are these findings significant?
At the time (and for many years afterwards) researchers such as Richard Clark (1983) argued that ‘proper’, scientific research showed no significant difference between the use of different media. In particular, there were no differences between classroom teaching and other media such as television or radio or satellite. Even today, we are getting similar findings regarding online learning (e.g. Means et al., 2010).
However, this is because the research methodology that is used by researchers for such comparative studies requires the two conditions being compared to be the same, except for the medium being used (called matched comparisons, or sometimes quasi-experimental studies). Typically, for the comparison to be scientifically rigorous, if you gave lectures in class, then you had to compare lectures on television. If you used another television format, such as a documentary, you were not comparing like with like. Since the classroom was used as the base, for comparison, you had to strip out all the affordances of television – what it could do better than a lecture – in order to compare it. Indeed Clark argued that when differences in learning were found between the two conditions, the differences were a result of using a different pedagogy in the non-classroom medium.
The critical point is that different media can be used to assist learners to learn in different ways and achieve different outcomes. In one sense, researchers such as Clark were right: the teaching methods matter, but different media can more easily support different ways of learning than others. In our example, a documentary TV program aims at developing the skills of analysis and the application or recognition of theoretical constructs, whereas a classroom lecture is more focused on getting students to understand and correctly recall the theoretical constructs. Thus requiring the television program to be judged by the same assessment methods as for the classroom lecture unfairly measures the potential value of the TV program. In this example, it may be better to use both methods: didactic teaching to teach understanding, then a documentary approach to apply that understanding. (Note that a television program could do both, but the classroom lecture could not.)
Perhaps even more important is the idea that many media are better than one. This allows learners with different preferences for learning to be accommodated, and to allow subject matter to be taught in different ways through different media, thus leading to deeper understanding or a wider range of skills in using content. On the other hand, this increases costs.
7.3.5 How do these findings apply to digital learning?
Digital learning can incorporate a range of different media: text, graphics, audio, video, animation, simulations. We need to understand better the affordances of each medium within the Internet, and use them differently but in an integrated way so as to develop deeper knowledge, and a wider range of learning outcomes and skills. The use of different media also allows for more individualization and personalization of the learning, better suiting learners with different learning styles and needs. Most of all, we should stop trying merely to move classroom teaching to other media such as MOOCs, and start designing digital learning so its full potential can be exploited.
7.3.6 Implications for education
If we are interested in selecting appropriate technologies for teaching and learning, we should not just look at the technical features of a technology, nor even the wider technology system in which it is located, nor even the educational beliefs we bring as a classroom teacher. We also need to examine the unique features of different media, in terms of their formats, symbols systems, and cultural values. These unique features are increasingly referred to as the affordances of media or technology.
The concept of media is much ‘softer’ and ‘richer’ than that of ‘technology’, more open to interpretation and harder to define, but ‘media’ is a useful concept, in that it can also incorporate the inclusion of face-to-face communication as a medium. Another reason to distinguish between media and technology is to recognise that technology on its own does not of itself lead to the transfer of meaning .
As new technologies are developed, and are incorporated into media systems, old formats and approaches are carried over from older to newer media. Education is no exception. New technology is ‘accommodated’ to old formats, as with clickers and lecture capture, or we try to create the classroom in virtual space, as with learning management systems. However, new formats, symbols systems and organizational structures that exploit the unique characteristics of the Internet as a medium are gradually being discovered. It is sometimes difficult to see these unique characteristics clearly at this point in time. However, e-portfolios, mobile learning, open educational resources such as animations or simulations, and self-managed learning in large, online social groups are all examples of ways in which we are gradually developing the unique ‘affordances’ of the Internet.
More significantly, it is likely to be a major mistake to use computers to replace or substitute for humans in the educational process, given the need to create and interpret meaning when using media, at least until computers have much greater facility to recognize, understand and apply semantics, value systems, and organizational features, which are all important components of ‘reading’ different media. But at the same time it is equally a mistake to rely only on the symbol systems, cultural values and organizational structures of classroom teaching as the means of judging the effectiveness or appropriateness of the Internet as an educational medium.
Thus we need a much better understanding of the strengths and limitations of different media for teaching purposes if we are successfully to select the right medium for the job. However, given the widely different contextual factors influencing learning, the task of media and technology selection becomes infinitely complex. This is why it has proved impossible to develop simple algorithms or decision trees for effective decision making in this area. Nevertheless, there are some guidelines that can be used for identifying the best use of different media within an Internet-dependent society. To develop such guidelines we need to explore in particular the unique educational affordances of text, audio, video and computing, which is the next task of this chapter.
Activity 6.3 Media or technology?
1. Do you find the distinction between media and technology helpful? If so, how would you classify the following (medium or technology):
• newspaper
• printing press
• television program
• Netflix
• classroom
• MOOC
• discussion forum
2. Do you think that knowledge becomes something different when represented by different media? For instance, does an animation of a mathematical function represent something different from a written or printed equation of the same function? Which is the most ‘mathematical’: the formula or the animation?
3. What in your view makes the Internet unique from a teaching perspective, or is it just old wine in new bottles?
4. Text has publishers and newspaper corporations, audio has radio stations, and video has both television companies and YouTube. Is there a comparable organization for the Internet or is it not really a medium in the sense of publishing, radio or television?
For feedback on this activity, click on the podcast below:
An audio element has been excluded from this version of the text. You can listen to it online here: https://pressbooks.bccampus.ca/teachinginadigitalagev2/?p=178
More reading
Bates, A. (1984) Broadcasting in Education: An Evaluation London: Constables
Bates, A. (2012) Pedagogical roles for video in online learning, Online Learning and Distance Education Resources
Clark, R. (1983) ‘Reconsidering research on learning from media’ Review of Educational Research, Vol. 53, pp. 445-459
Kozma, R. (1994) ‘Will Media Influence Learning? Reframing the Debate’, Educational Technology Research and Development, Vol. 42, No. 2, pp. 7-19
Means, B. et al. (2009) Evaluation of Evidence-Based Practices in Online Learning: A Meta-Analysis and Review of Online Learning Studies Washington, DC: US Department of Education (http://www.ed.gov/rschstat/eval/tech...inalreport.pdf)
Russell, T. L. (1999) The No Significant Difference Phenomenon Raleigh, NC: North Carolina State University, Office of Instructional Telecommunication
Schramm, W. (1972) Quality in Instructional Television Honolulu HA: University Press of Hawaii
If you want to go deeper into the definitions of and differences between media and technology, you might want to read any of the following:
Bates, A. (2011) Marshall McLuhan and his relevance to teaching with technology, Online learning and distance education resources, July 20 (for a list of McLuhan references as well as a discussion of his relevance)
Guhlin, M. (2011) Education Experiment Ends,Around the Corner – MGuhlin.org, September 22
LinkedIn: Media and Learning Discussion Group
Salomon, G. (1979) Interaction of Media, Cognition and Learning San Francisco: Jossey Bass | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/07%3A_Understanding_technology_in_education/07.3%3A_Media_or_technology.txt |
Figure 7.4 The SAMR model Image: Ruben Puentedura
7.4.1 Exploiting the affordances of a medium
It was noted in the previous section that video technology can be used as a straight replacement for a face-to-face lecture by merely substituting the face-to-face delivery with online delivery. The mode of delivery has changed but not the pedagogy. The full affordances of the medium of video have not been exploited.
On the other hand, using video to show a documentary can bring powerful examples of situations to which can be applied the ideas and concepts covered in an academic course. A documentary thus has the potential to make better use of the affordances of video than recording a lecture because the learning experience from watching a documentary is different from watching a lecture; at the same time, using a documentary video will require a different approach to teaching than using a lecture and will probably have different outcomes. With the video lecture students will focus on comprehension and understanding; with the documentary the students’ focus will be on analysing and critiquing the material.
7.4.2 The SAMR model
A good way to assess whether a particular application of media or technology is making full use of the affordances of a medium is to apply the SAMR model developed by Dr. Ruben Puentedura, a technology consultant based in the USA.
Puentedura suggests four ‘levels’ of technology application in education:
• substitution: a direct tool substitute, with no functional change, for example, a video recording of a classroom lecture on water quality, made available for downloading by students; students are assessed on the content of the lecture by written exams at the end of the course.
• augmentation: a direct tool substitute, with functional improvement, for example, the video lecture is embedded in an LMS, and edited into four sections, with online multiple-choice questions at the end of each section for students to answer.
• modification: significant task redesign, for example, the instructor provides video recordings of water being tested, and asks students to analyse each of the recordings in terms of the principles taught in the course in the form of essay-type questions that are assessed.
• redefinition: creation of new tasks, inconceivable without the use of technology, for example, the instructor provides readings and online guidance through the LMS, and students are asked to record with their mobile phones how they selected samples of water for testing quality, and integrate their findings and analysis in the form of an e-portfolio of their work.
In the first two levels, substitution and augmentation, video is used to enhance the method of teaching but it is only where video is used in the final two stages, modification and redefinition, that teaching is actually transformed. Significantly, Puentedura links the modification and transformation levels to the development of Bloom’s higher order ’21st century’ skills such as analysis, evaluation and creativity (Puentedura, 2014). For a more detailed description of the model and how it works, see the video: Introduction to the SAMR model.
7.4.3 Strengths and limitations of the model
First, I was unable to find any research that validated this model. It has a powerful feel of common sense behind it, but it would be good to see it more empirically validated, although there are many examples of its actual use, particularly in teacher education in the k-12 sector (you can find some examples collected by Kelly Walsh here. For a more critical response to the SAMR model, see Linderoth, 2013).
Second, while the model is a useful means of evaluating whether a use of technology merely enhances or radically changes teaching, it doesn’t help much with the hard part, and that is imagining the transformative ways in which a technology could be used in the first place. Nevertheless it is a good heuristic device to get you to think about the best way to use technology in teaching.
Third, there will be situations where substitution and augmentation will still be a perfectly justifiable use of technology, for instance for students with disabilities, or to increase accessibility to learning materials.
On balance, it is a very useful model by which an instructor can evaluate a potential or actual use of technology. In particular it focuses on the way students will need to interact with the technology and the ways technology can be used to assist the development of 21st century skills. At the same time, we still need to understand how and why media and technology could be used to transform teaching in the first place. The first step then is to understand better the unique properties of different technologies, which is the subject of the next section.
References
Linderoth, J. (2013) Open letter to Dr. Ruben Puentedura Spelvetenskapliga betraktelser, 17 October
Puentedura, R. (2014) SAMR and Bloom’s Taxonomy: Assembling the Puzzle common sense education, September 24
Activity 7.4: Assessing the SAMR model
1. If you are using any technology in your teaching, where does it fit in the SAMR framework in comparison with in-person teacher-student interaction? What could you change to make the technology ‘move up the ladder’?
2. Do you have to exploit fully the affordances of a medium? If so, why?
For feedback on this activity, click on the podcast below
An audio element has been excluded from this version of the text. You can listen to it online here: https://pressbooks.bccampus.ca/teachinginadigitalagev2/?p=1237 | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/07%3A_Understanding_technology_in_education/07.4%3A_Assessing_media_affordances%3A_the_SAMR_model.txt |
Figure 7.5 The teacher is the lighter-coloured symbol
7.5.1 Key media characteristics
It will help clarify the possible benefits or weaknesses for education of each medium if we understand the characteristics or affordances of each medium. To do this we need to identify where media have common or different features.
There is a wide range of media characteristics or affordances that we could look at, but I will focus on three that are particularly important for education:
• whether media are broadcast (one-way) or communicative (two way);
• whether media are synchronous or asynchronous, including live (transient) or recorded (permanent);
• whether media are single or rich.
We shall see that these characteristics are more dimensional than discrete states, and different media will fit at different points on these dimensions, but the exact point on the continuum will depend to some extent on the way they are designed or used. In this section I will focus on the broadcast/communicative dimension. The other two characteristics will be discussed in subsequent sections
7.5.2 Broadcast or communicative media
A major structural distinction is between ‘broadcast’ media that are primarily one-to-many and one-way, and those media that are primarily many-to-many or ‘communicative’, allowing for two-way or multiple communication connections. Communicative media include those that give equal ‘power’ of communication between multiple end users.
7.5.2.1 Broadcast media
Television, radio and print for example are primarily broadcast or one-way media, as end users or ‘recipients’ cannot change the ‘message’ (although they may interpret it differently or choose to ignore it). Note that it does not matter really what delivery technology (terrestrial broadcast, satellite, cable, DVD, Internet) is used for television, it remains a ‘broadcast’ or one-way medium. Some Internet technologies are also primarily one way. For instance, an institutional web site is primarily a one-way technology.
One advantage of broadcast media and technologies is that they ensure a common standard of learning materials for all students. This is particularly important in countries where teachers are poorly qualified or of variable quality. Also one-way broadcast media enable the organization to control and manage the information that is being transmitted, ensuring quality control over content. Broadcasting media and technologies are more likely to be favoured by those with an ‘objectivist’ approach to teaching and learning, since the ‘correct’ knowledge can be transmitted to everyone receiving the instruction. One disadvantage is that additional resources are needed to provide interaction with teachers or between learners.
7.5.2.2 Communicative media
The telephone, video-conferencing, e-mail, online discussion forums, most social media and the Internet are examples of communicative media or technologies, in that all users can communicate and interact with each other, and in theory at least have equal power in technology terms. The educational significance of communicative media is that they allow for interaction between learners and teachers, and perhaps even more significantly, between a learner and other learners, without the participants needing to be present in the same place.
7.5.2.3 Which is which?
This dimension is not a rigid one, with necessarily clear or unambiguous classifications. Increasingly, technologies are becoming more complex, and able to serve a wide range of functions. In particular the Internet is not so much a single medium as an integrating framework for many different media and technologies with different and often opposite characteristics. Furthermore, most technologies are somewhat flexible in that they can be used in different ways. However, if we stretch a technology too far, for instance trying to make a broadcast medium such as an xMOOC also more communicative, stresses are likely to occur. So I find the dimension still useful, so long as we are not dogmatic about the characteristics of individual media or technologies. This means though looking at each case separately.
Thus I see a learning management system as primarily a broadcast or one-way technology, although it has features such as discussion forums that allow for some forms of multi-way communication. However, it could be argued that the communication functions in an LMS require additional technologies, such as a discussion forum, that just happen to be plugged in to or embedded within the LMS, which is primarily a database with a cool interface. We shall see that in practice we often have to combine technologies if we want the full range of functions required in education, and this adds cost and complexity.
Web sites can vary on where they are placed on this dimension, depending on their design. For instance, an airline web site, while under the full control of the company, has interactive features that allow you to find flights, book flights, reserve seats, and hence, while you may not be able to ‘communicate’ or change the site, you can at least interact with it and to some extent personalize it. However, you cannot change the page showing the choice of flights. This is why I prefer to talk about dimensions. An airline web site that allows end user interaction is less of a broadcast medium. However it is not a ‘pure’ communicative medium either. The power is not equal between the airline and the customer, because the airline controls the site.
It should be noted too that some social media (e.g. YouTube and blogs) are also more of a broadcast than a communicative medium, whereas other social media use mainly communicative technologies with some broadcast features (for example, personal information on a Facebook page). A wiki is clearly more of a ‘communicative’ medium. Again though it needs to be emphasized that intentional intervention by teachers, designers or users of a technology can influence where on the dimension some technologies will be, although there comes a point where the characteristic is so strong that it is difficult to change significantly without introducing other technologies.
The role of the teacher or instructor also tends to be very different when using broadcast or communicative media. In broadcast media, the role of the teacher is central, in that content is chosen and often delivered by the instructor. xMOOCs are an excellent example. However, in communicative media, while the instructor’s role may still be central, as in online collaborative learning or seminars, there are learning contexts where there may be no identified ‘central’ teacher, with contributions coming from all or many members of the community, as in communities of practice or cMOOCs.
Thus it can be seen that ‘power’ is an important aspect of this dimension. What ‘power’ does the end-user or student have in controlling a particular medium or technology? If we look at this from an historical perspective, we have seen a great expansion of technologies in recent years that give increasing power to the end user. The move towards more communicative media and away from broadcast media then has profound implications for education (as for society at large).
7.5.3 Applying the dimension to educational media
We can also apply this analysis to non-technological means of communication, or ‘media’, such as classroom teaching. Lectures have broadcast characteristics, whereas a small seminar group has communicative characteristics. In Figure 7.5.3, I have placed some common technologies, classroom media and online media along the broadcast/communicative continuum.
Figure 7.5.3 The continuum of knowledge dissemination
When doing this exercise, it is important to note that:
• there is no general normative or evaluative judgement about the continuum. Broadcasting is an excellent way of getting information in a consistent form to a large number of people; interactive communication works well when all members of a group have something equal to contribute to the process of knowledge development and dissemination. The judgement of the appropriateness of the medium or technology will very much depend on the context, and in particular the resources available and the general philosophy of teaching to be applied;
• where a particular medium or technology is placed on the continuum will depend to some extent on the actual design, use or application. For instance, if the lecturer talks for 45 minutes and allows 10 minutes for discussion, an interactive lecture might be further towards broadcasting than if the lecture session is more of a question and answer session;
• I have placed ‘computers’ in the middle of the continuum. They can be used as a broadcast medium, such as for programmed learning, or they can be used to support communicative uses, such as online discussion. Their actual placement on the continuum therefore will depend on how we choose to use computers in education;
• the important decision from a teaching perspective is deciding on the desired balance between ‘broadcasting’ and ‘discussion’ or communication. That should then be one factor in driving decisions about the choice of appropriate technologies;
• the continuum is a heuristic device to enable a teacher to think about what medium or technology will be most appropriate within any given context, and not a firm analysis of where different types of educational media or technology belong on the continuum.
Thus where a medium or technology ‘fits’ best on a continuum of broadcast vs communicative is one factor to be considered when making decisions about media or technology for teaching and learning.
Activity 7.5 Broadcast or communicative?
From the list below:
• a blog
• online collaborative learning
• Twitter
• virtual worlds
• a podcast
• an open textbook
1. Determine which is a medium and which a technology, or which could be both, and under what conditions.
2. Decide where, from your experience, each medium or technology should be placed on Figure 7.5.3. Write down why.
3. Which were easy to categorize and which difficult?
4. How useful is this continuum in making decisions about which medium or technology to use in your teaching? What would help you to decide?
My analysis can be accessed by clicking here. | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/07%3A_Understanding_technology_in_education/07.5%3A_Broadcast_vs_communicative_media.txt |
Figure 7.6.1 Audio cassettes are a recorded, asynchronous technology
Different media and technologies operate differently over space and time. These dimensions are important for both facilitating or inhibiting learning, and for limiting or enabling more flexibility for learners. There are actually two closely related dimensions here:
• ‘live’ or recorded
• synchronous or asynchronous
7.6.1 Live or recorded
These are fairly obvious in their meaning. Live media by definition are face-to-face events, such as lectures, seminars, and one-on-one face-to-face tutorials. A ‘live’ event requires everyone to be present at the same place and time as everyone else. This could be a rock concert, a sports event or a lecture. Live events, such as for instance a seminar, work well when personal relations are important, such as building trust, or for challenging attitudes or positions that are emotionally or strongly held (either by students or instructors.) The main educational advantage of a live lecture is that it may have a strong emotive quality that inspires or encourages learners beyond the actual transmission of knowledge, or may provide an emotional ‘charge’ that may help students shift from previously held positions. Live events, by definition, are transient. They may be well remembered, but they cannot be repeated, or if they are, it will be a different experience or a different audience. Thus there is a strong qualitative or affective element about live events.
Recorded media on the other hand are permanently available to those possessing the recording, such as a video-cassette or an audio-cassette. Books and other print formats are also recorded media. The key educational significance of recorded media is that students can access the same learning material an unlimited number of times, and at times that are convenient for the learner.
Live events of course can also be recorded, but as anyone who has watched a live sports event compared to a recording of the same event knows, the experience is different, with usually a lesser emotional charge when watching a recording (especially if you already know the result). Thus one might think of ‘live’ events as ‘hot’ and recorded events as ‘cool.’ Recorded media can of course be emotionally moving, such as a good novel, but the experience is different from actually taking part in the events described.
7.6.2 Synchronous or asynchronous
Synchronous technologies require all those participating in the communication to participate together, at the same time, but not necessarily in the same place.
Thus live events are one example of synchronous media, but unlike live events, technology enables synchronous learning without everyone having to be in the same place, although everyone does have to participate in the event at the same time. A video-conference or a webinar are examples of synchronous technologies which may be broadcast ‘live’, but not with everyone in the same place. Other synchronous technologies are television or radio broadcasts. You have to be ‘there’ at the time of transmission, or you miss them. However, the ‘there’ may be somewhere different from where the teacher is.
Asynchronous technologies enable participants to access information or communicate at different points of time, usually at the time and place of choice of the participant. All recorded media are asynchronous. Books, DVDs, on-demand You Tube videos, lectures recorded through lecture capture and available for streaming on demand, and online discussion forums are all asynchronous media or technologies. Learners can log on or access these technologies at times and the place of their own choosing.
Figure 7.6.2 illustrates the main differences between media in terms of different combinations of time and place.
Figure 7.6.2 The separation of teachers/instructors from learners by time and space
7.6.3 Why does this matter?
Overall there are huge educational benefits associated with asynchronous or recorded media, because the ability to access information or communicate at any time offers the learner more control and flexibility. The educational benefits have been confirmed in a number of studies. For instance, Means et al. (2010) found that students did better on blended learning because they spent more time on task, because the online materials were always available to the students.
Research at the Open University found that students much preferred to listen to radio broadcasts recorded on cassette than to the actual broadcast, even though the content and format was identical (Grundin, 1981; Bates at al., 1981). However, even greater benefits were found when the format of the audio was changed to take advantage of the control characteristics of cassettes (stop, replay). It was found that students learned more from ‘designed’ cassettes than from cassette recordings of broadcasts, especially when the cassettes were co-ordinated or integrated with visual material, such as text or graphics. This was particularly valuable, for instance, in talking students through mathematical formulae (Durbridge, 1983).
This research underlines the importance of changing design as one moves from synchronous to asynchronous technologies. Thus we can predict that although there are benefits in recording live lectures through lecture capture in terms of flexibility and access, or having readings available at any time or place, the learning benefits would be even greater if the lecture or text was redesigned for asynchronous use, with built-in activities such as tests and feedback, and points for students to stop the lecture and do some research or extra reading, then returning to the teaching.
The ability to access learning materials on demand (recorded lectures or webinars, learning management systems, web sites, social media) is particularly important for increasing access and flexibility for learners, especially those working as well as studying, for those with young families, or for students with long commutes. Thus there should be clearly justified pedagogical benefits that could not be provided by the use of technology if students must be present either in the same place or at the same time as an instructor. In particular, what are the social or pedagogical reasons why students should come to the school or campus or be present at a set time when so much teaching and learning can now be done asynchronously?
The ability to access media asynchronously through recorded and streamed materials is one of the biggest changes in the history of teaching, but the dominant paradigm in higher education is still the live lecture or seminar. There are, as we have seen, some advantages in live media, and direct inter-personal contact, but they need to be used more selectively to exploit their unique advantages or affordances.
7.6.4 The significance of the Internet
Broadcast/communicative and synchronous/asynchronous are two separate dimensions. By placing them in a matrix design, we can then assign different technologies to different quadrants, as in Figure 7.6.4 below. (I have included only a few – you may want to place other technologies on this diagram):
Figure 7.6.4 The significance of the Internet in terms of media characteristics
Why the Internet is so important is that it is an encompassing medium that embraces all these other media and technologies, thus offering immense possibilities for teaching and learning. This enables us, if we wish, to be very specific about how we design our teaching, so that we can exploit all the characteristics or dimensions of technology to fit almost any learning context through this one medium.
7.6.5 Conclusion
It should be noted at this stage that although I have identified some strengths and weaknesses of the four characteristics of broadcast/communicative, and synchronous/asynchronous media, we still need an evaluative framework for deciding when to use or combine different technologies. This means developing criteria that will enable us to decide within specific contexts the optimum choice of technologies.
References
Bates, A. (1981) ‘Some unique educational characteristics of television and some implications for teaching or learningJournal of Educational Television Vol. 7, No.3
Durbridge, N. (1983) Design implications of audio and video cassettes Milton Keynes: Open University Institute of Educational Technology (out of print)
Grundin, H. (1981) Open University Broadcasting Times and their Impact on Students’ Viewing/Listening Milton Keynes: The Open University Institute of Educational Technology (out of print)
Means, B. et al. (2009) Evaluation of Evidence-Based Practices in Online Learning: A Meta-Analysis and Review of Online Learning Studies Washington, DC: US Department of Education
Activity 7.6 Time and space dimensions of technology
1. Does this categorization of technologies make sense to you?
2. Can you easily place other media or technologies into Figures 7.6.2 and 7.6.4? What media or technologies don’t fit? Why not?
3. Can you imagine a situation where a podcast might be a better choice for teaching and learning than virtual reality (assuming students have access to both technologies)? And can you imagine the opposite (of where virtual reality would be better than an audio-cassette)? What are the defining criteria or conditions?
For my comments on the last question, click on the podcast below:
An audio element has been excluded from this version of the text. You can listen to it online here: https://pressbooks.bccampus.ca/teachinginadigitalagev2/?p=187 | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/07%3A_Understanding_technology_in_education/07.6%3A_The_time_and_space_dimensions_of_media.txt |
Figure 7.7.1 Making sense of biology: MrExham
7.7.1 The historical development of media richness
In Section 7.2, ‘A short history of educational technology’, the development of different media in education was outlined, beginning with oral teaching and learning, moving on to written or textual communication, then to video, and finally computing. Each of these means of communication has usually been accompanied by an increase in the richness of the medium, in terms of how many senses and interpretative abilities are needed to process information.
Another way of defining the richness of media is by the symbol systems employed to communicate through the medium. Thus textual material from an early stage incorporated graphics and drawings as well as words. Television or video incorporates audio as well as still and moving images. Computing now can incorporate text, audio, video, animations, simulations, computing, and networking, all through the Internet.
7.7.2 The continuum of media richness
Figure 7.7.2 The continuum of media richness
Once again then there is a continuum in terms of media richness, as illustrated in Figure 7.7.2 above. Also once again, design of a particular medium can influence where on the continuum it would be placed. For instance in Figure 7.7.2, different forms of teaching using video are represented in blue. Ted Talks, a televised lecture, and often xMOOCs are usually mainly talking heads. The Khan Academy uses dynamic graphics as well as voice over commentary, and MrExham’s YouTube video on prokaryotic cells uses colour graphics and animation as well as a ‘talking head’ commentary. Educational television broadcasts are likely to use an even wider range of video techniques.
However, although the richness of video can be increased or decreased by the way it is used, video is always going to be richer in media terms than radio or textbooks. Radio is never going to be a rich medium in terms of its symbols systems because it depends on a single medium, audio, and even talking head video is richer symbolically than radio.
There is no normative or evaluative judgment here. Radio can be ‘rich’ in the sense of fully exploiting the characteristics or symbol systems of the medium. A well produced radio program is more likely to be educationally effective than a badly produced video. But in terms of representation of knowledge, the possibilities of radio in terms of media richness will always be less than the possibilities of video.
7.7.3 The educational value of media richness
But how rich should media be for teaching and learning? From a teaching perspective, rich media have advantages over a single medium of communication, because rich media enable the teacher to do more. For example, many activities that previously required learners to be present at a particular time and place to observe processes or procedures such as demonstrating mathematical reasoning, experiments, medical procedures, or stripping a carburetor, can now be recorded and made available to learners to view at any time. Sometimes, phenomena that are too expensive or too difficult to show in a classroom can be shown through animation, simulations, video recordings or virtual reality.
Furthermore, each learner can get the same view as all the other learners, and can view the process many times until they have mastery. Good preparation before recording can ensure that the processes are demonstrated correctly and clearly. The combination of voice over video enables learning through multiple senses. Even simple combinations, such as the use of audio over a sequence of still frames in a text, have been found more effective than learning through a single medium of communication (see for instance, Durbridge, 1984). The Khan Academy videos have exploited very effectively the power of audio combined with dynamic graphics. Computing adds another element of richness, in the ability to network learners or to respond to learner input.
From a learner’s perspective, though, some caution is needed with rich media. Two particularly important concepts are cognitive overload and Vygotsky’s Zone of Proximal Development. Cognitive overload results when students are presented with too much information at too complex a level or too quickly for them to properly absorb it (Sweller, 1988). Vygotsky’s Zone of Proximal Development or ZPD (Vygotsky, 1934) is the difference between what a learner can do without help and what can be done with help. Rich media may contain a great deal of information compressed into a very short time period and its value will depend to a large extent on the learner’s level of preparation for interpreting it.
For instance, a documentary video may be valuable for demonstrating the complexity of human behaviour or complex industrial systems, but learners may need either preparation in terms of what to look for, or to identify concepts or principles that may be illustrated within the documentary. On the other hand, interpretation of rich media is a skill that can be explicitly taught through demonstration and examples (Bates and Gallagher, 1977). Although YouTube videos are limited in length to around eight minutes mainly for technical reasons, they are also more easily absorbed than a continuous video of 50 minutes. Thus again design is important for helping learners to make full educational use of rich media.
7.7.4 Simple or rich media?
It is a natural tendency when choosing media for teaching to opt for the ‘richest’ or most powerful medium. Why would I use a podcast rather than a video? There are in fact several reasons:
• cost and ease of use: it may just be quicker and simpler to use a podcast, especially if it can achieve the same learning objective;
• there may be too many distractions in a rich medium for students to grasp the essential point of the teaching. For instance, video recording a busy intersection to look at traffic flow may include all kinds of distractions for the viewer from the actual observation of traffic patterns. A simple diagram or an animation that focuses only on the phenomenon to be observed might be better;
• the rich medium may be inappropriate for the learning task. For instance, if students are to follow and critique a particular argument or chain of reasoning, text may work better than a video of a lecturer with annoying mannerisms talking about the chain of reasoning.
In general, it is tempting always to look for the simplest medium first then only opt for a more complex or richer medium if the simple medium can’t deliver the learning goals as adequately. However, consideration needs to be given to media richness as a criterion when making choices about media or technology, because rich media may enable learning goals to be achieved that would be difficult with a simple medium.
This is the last of the characteristics of media and technology that can influence decisions about teaching and learning. The next section will provide an overview and summary.
References
Bates, A. and Gallagher, M. (1977) Improving the Effectiveness of Open University Television Case-Studies and Documentaries Milton Keynes: The Open University I.E.T. Papers on Broadcasting, No. 77 (out of print – copies available from [email protected]).
Durbridge, N. (1984) Audio cassettes, in Bates, A. (ed.) The Role of Technology in Distance Education London: Routledge (re-published in 2014)
Sweller, J. (1988) Cognitive load during problem solving: effects on learning, Cognitive Science, Vol. 12
Vygotsky, L.S. (1987). Thinking and speech, in R.W. Rieber & A.S. Carton (eds.), The collected works of L.S. Vygotsky, Volume 1: Problems of general psychology (pp. 39–285). New York: Plenum Press. (Original work published 1934.)
Activity 7.7 How rich is your medium?
1. What media are you using at the moment for teaching? Where would you place these on the ‘richness’ continuum? What benefits might there be to your teaching in changing your media to either increase or decrease the richness of media you are using?
2. Do you agree that: ‘it is a useful guideline always to look for the simplest medium first‘.
3. How important do you think the richness of medium is when making decisions about the use of media and technology?
4. Do you agree with the placement of different media on this continuum in Figure 7.7.2. If not, why not?
I provide no feedback for this activity. | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/07%3A_Understanding_technology_in_education/07.7%3A_Media_richness.txt |
Figure 7.8 Video explanation of a (plastinated) dog’s heart: note the QR code which enables students to access the video from their own phones or tablets. Image: Dr. Sue Dawson, University of Prince Edward Island
I am aware that this chapter may appear somewhat abstract and theoretical, but in any subject domain, it is important to understand the foundations that underpin practice. This applies with even more force to understanding media and technology in education, because it is such a dynamic field that changes all the time. What seem to be the major media developments this year are likely to be eclipsed by new developments in technology next year. In such a shifting sea, it is therefore necessary to look at some guiding concepts or principles that are likely to remain constant, whatever changes take place over the years.
So in summary here are the main points that I have been emphasising throughout this chapter.
Key Takeaways
1. Technologies are merely tools that can be used in a variety of ways. What matters more is how technologies are applied. The same technology can be applied in different ways, even or especially in education. So in judging the value of a technology, we need to look more closely at the ways in which it is being or could be used. In essence this means focusing more on media – which represent the more holistic use of technologies – than on individual tools or technologies themselves, while still recognising that technology is an essential component of almost all media.
2. By focusing on media rather than technologies, we can then include face-to-face teaching as a medium, enabling comparisons with more technology-based media to be made along a number of dimensions or characteristics.
3. Recognising that in education media are usually used in combination, the six key building blocks of media are:
1. face-to-face teaching
2. text
3. (still) graphics
4. audio (including speech)
5. video
6. computing (including animation, simulations and virtual reality)
4. Media differ in terms of their formats, symbols systems, and cultural values. These unique features are increasingly referred to as the affordances of media or technology. Thus different media can be used to assist learners to learn in different ways and achieve different outcomes, thus also individualising learning more.
5. There are many dimensions along which some technologies are similar and others are different. By focusing on these dimensions, we have a basis for analysing new media and technologies, to see where they ‘fit’ within the existing landscape, and to evaluate their potential benefits or limitations for teaching and learning.
6. There are probably other characteristics or dimensions of educational media that might also be identified, but I believe these three key characteristics or dimensions to be the most important:
1. broadcast vs communicative
2. synchronous (live) vs asynchronous (recorded)
3. single vs rich media
7. However, the identification of where a particular medium fits along any specific characteristic or dimension will depend in most cases on how that medium is designed. At the same time, there is usually a limit to how far a technology can be forced along one of these dimensions; there is likely to be a single, ‘natural’ position on each dimension, subject to good design, in terms of exploiting the educational affordances of the medium.
8. These characteristics or dimensions of media then need to be evaluated against the learning goals and outcomes desired, while recognising that a new educational medium or application might enable goals to be achieved that had not been previously considered possible.
9. Over time, media have tended to become more communicative, asynchronous, and ‘rich’, thus offering teachers and learners more powerful tools for teaching and learning.
10. The Internet is an extremely powerful medium because through a combination of tools and media it can encompass all the characteristics and dimensions of educational media.
Activity 7.8 Analysing your current use of technology
1. Take one of the courses you are teaching at the moment. How could you make your teaching more communicative, asynchronous, and rich in media? What media or technologies would help you do this?
2. Write down what you would see as (a) the advantages (b) the disadvantages of changing your teaching in this way.
3. Do you think applying the three dimensions described here will be useful when deciding whether or not to use a new technology? If not, why not?
The next chapter should provide more feedback on your answers. | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/07%3A_Understanding_technology_in_education/07.8%3A_Understanding_the_foundations_of_educational_media.txt |
Figure 8.1.1 Is slow motion a unique characteristic of video?
Image: Pouring mercury into liquid nitrogen: University of Nottingham
Click on image to see video
8.1.1 Identifying the pedagogical differences between media
In the last chapter, I identified three core dimensions of media and technology along which any technology can be placed. In the next two chapters, I will discuss a method for deciding which media to use when teaching. In this chapter I will focus primarily on the pedagogical differences between media. In the following chapter I will provide a model or set of criteria to use when making decisions about media and technology for teaching.
8.1.2 First steps
Embedded within any decision about the use of technology in education and training will be assumptions about the learning process. We have already seen earlier in this book how different epistemological positions and theories of learning affect the design of teaching, and these influences will also determine a teacher’s or an instructor’s choice of appropriate media. Thus, the first step is to decide what and how you want to teach.
This has been covered in depth through Chapters 2-5, but in summary, there are five critical questions that need to be asked about teaching and learning in order to select and use appropriate media/technologies:
• what is my underlying epistemological position about knowledge and teaching?
• what are the desired learning outcomes from the teaching?
• what teaching methods will be employed to facilitate the learning outcomes?
• what are the unique educational characteristics of each medium/technology, and how well do these match the learning and teaching requirements?
• what resources are available?
This chapter focuses on the fourth of these questions, but they are best not asked sequentially, but in a cyclical or iterative manner, as media affordances may suggest alternative teaching methods or even the possibility of learning outcomes that had not been initially considered. When the unique pedagogical characteristics of different media are considered, this may lead to some changes in what content will be covered and what skills will be developed. Therefore, at this stage, decisions on content and learning outcomes should still be tentative.
8.1.3 Identifying the unique educational characteristics of a medium
Different media have different potential or ‘affordances’ for different types of learning. One of the arts of teaching is often finding the best match between media and desired learning outcomes. Before exploring this relationship, first, a summary of the substantial amount of excellent past research on this topic (see, for instance, Trenaman, 1967; Olson and Bruner, 1974; Schramm, 1977; Salomon, 1979, 1981; Clark, 1983; Bates, 1984; Koumi, 2006; Berk, 2009; Mayer, 2009).
This research has indicated that there are three core elements that need to be considered when deciding what media to use:
• content;
• content structure;
• skills.
Olson and Bruner (1974) claim that learning involves two distinct aspects: acquiring knowledge of facts, principles, ideas, concepts, events, relationships, rules and laws; and using or working on that knowledge to develop skills. Again, this is not necessarily a sequential process. Identifying skills then working back to identify the concepts and principles needed to underpin the skills may be another valid way of working. In reality, learning content and skills development will often be integrated in any learning process. Nevertheless, when deciding on media use, it is useful to make a distinction between content and skills.
8.1.3.1. The representation of content
Media differ in the extent to which they can represent different kinds of content, because they vary in the symbol systems (text, sound, still pictures, moving images, etc.) that they use to encode information (Salomon, 1979). We saw in the previous chapter that different media are capable of combining different symbol systems. Differences between media in the way they combine symbol systems influence the way in which different media represent content. Thus there is a difference between a direct experience, a written description, a televised recording, and a computer simulation of the same scientific experiment. Different symbol systems are being used, conveying different kinds of information about the same experiment. For instance, our concept of heat can be derived from touch, mathematical symbols (800 celsius), words (random movement of particles), animation, or observance of experiments. Our ‘knowledge’ of heat is as a result not static, but developmental. A large part of learning requires the mental integration of content acquired through different media and symbol systems. For this reason, deeper understanding of a concept or an idea is often the result of the integration of content derived from a variety of media sources (Mayer, 2009).
Media also differ in their ability to handle concrete or abstract knowledge. Abstract knowledge is handled primarily through language. While all media can handle language, either in written or spoken form, media vary in their ability to represent concrete knowledge. For instance, television can show concrete examples of abstract concepts, the video showing the concrete ‘event’, and the sound track analyzing the event in abstract terms. Well-designed media can help learners move from the concrete to the abstract and back again, once more leading to deeper understanding.
8.1.3.2 Content structure
Media also differ in the way they structure content. Books, the telephone, radio, podcasts and face-to-face teaching all tend to present content linearly or sequentially. While these media can represent parallel activities (for example, in print, different chapters may deal with events that occur simultaneously but from different perspectives) such activities still have to be presented sequentially. Computers and television are more able to present or simulate the inter-relationship of multiple variables simultaneously occurring. Virtual reality is an exceptionally powerful example of this. Computers can also handle branching or alternative routes through information, but usually within closely defined limits.
Subject matter varies a great deal in the way in which information needs to be structured. Subject areas (for example, natural sciences, history) structure content in particular ways determined by the internal logic of the subject discipline. This structure may be very tight or logical, requiring particular sequences or relationships between different concepts, or very open or loose, requiring learners to deal with highly complex material in an open-ended or intuitive way.
If media then vary both in the way they present information symbolically and in the way they handle the structures required within different subject areas, media which best match the required mode of presentation and the dominant structure of the subject matter need to be selected. Consequently, different subject areas will require a different balance of media. This means that subject experts should be deeply involved in decisions about the choice and use of media, to ensure that the chosen media appropriately match the presentational and structural requirements of the subject matter.
8.1.3.3 The development of skills
Media also differ in the extent to which they can help develop different skills. Skills can range from intellectual to psychomotor to affective (emotions, feelings). Koumi (2015) has used Krathwohl’s (2002) revision of Bloom’s Taxonomy of Learning Objectives (1956) to assign affordances of text and video to learning objectives using Krathwold’s classification of learning objectives.
Comprehension is likely to be the minimal level of intellectual learning outcome for most education courses. Some researchers (for example, Marton and Säljö, 1976) make a distinction between surface and deep comprehension. At the highest level of skills comes the application of what one has comprehended to new situations. Here it becomes necessary to develop skills of analysis, evaluation, and problem solving.
Thus a first step is to identify learning objectives or outcomes, in terms of both content and skills, while being aware that the use of some media may result in new possibilities in terms of learning outcomes.
8.1.4 Pedagogical affordances – or unique media characteristics?
‘Affordances’ is a term originally developed by the psychologist James Gibson (1977) to describe the perceived possibilities of an object in relation to its environment (for example, a door knob suggests to a user that it should be turned or pulled, while a flat plate on a door suggests that it should be pushed.). The term has been appropriated by a number of fields, including instructional design and human-machine interaction.
Thus the pedagogical affordances of a medium relate to the possibilities of using that medium for specific teaching purposes. It should be noted that an affordance depends on the subjective interpretation of the user (in this case a teacher or instructor), and it is often possible to use a medium in ways that are not unique to that medium. For instance video can be used for recording and delivering a lecture. In that sense there is a similarity in at least one affordance for a lecture and a video. Also students may choose not to use a medium in the way intended by the instructor. For instance, Bates and Gallagher (1977) found that some social science students objected to documentary-style television programs requiring application of knowledge or analysis rather than presentation of concepts.
Others (such as myself) have used the term ‘unique characteristics’ of a medium rather than affordances, since ‘unique characteristics’ suggest that there are particular uses of a medium that are less easily replicated by other media, and hence act as a better discriminator in selecting and using media. For instance, using video to demonstrate in slow motion a mechanical process is much more difficult (but not impossible) to replicate in other media. In what follows, my focus is more on unique or particular rather than general affordances of each medium, although the subjective and flexible nature of media interpretation makes it difficult to come to any hard and fast conclusions.
I will now attempt in the next sections to identify some of the unique pedagogical characteristics of the following media:
• text;
• audio;
• video;
• computing;
• social media
• emerging technologies, in particular, virtual/augmented reality, serious games and artificial intelligence.
Technically, face-to-face teaching should also be considered a medium, but I will look specifically at the unique characteristics of face-to-face teaching in Chapter 10, where I discuss different modes of delivery.
8.1.5 Purpose of the exercise
Before starting on the analysis of different media, it is important to understand my goals in this chapter. I am NOT trying to provide a definitive list of the unique pedagogical characteristics of each medium. Because context is so important and because the science is not strong enough to identify unequivocally such characteristics, I am suggesting in the following sections a way of thinking about the pedagogical affordances of different media. To do this, I will identify what I think are the most important pedagogical characteristics of each medium.
However, individual readers may well come to different conclusions, depending particularly on the subject area in which they are working. The important point is for teachers and instructors to think about what each medium could contribute educationally within their subject area, and that requires a strong understanding of both the needs of their students and the nature of their subject area, as well as the key pedagogical features of each medium.
Listen to the podcast below for an illustration of the differences between media.
Podcast 8.1 Tony’s shaggy dog story: click play on the above podcast (41 seconds).
An audio element has been excluded from this version of the text. You can listen to it online here: https://pressbooks.bccampus.ca/teachinginadigitalagev2/?p=197
References
Bates, A. (1984) Broadcasting in Education: An Evaluation London: Constables
Bates, A. and Gallagher, M. (1977) Improving the Effectiveness of Open University Television Case-Studies and Documentaries Milton Keynes: The Open University, I.E.T. Papers on Broadcasting, No. 77 (out of print – copies available from [email protected])
Berk, R.A. (2009) Multimedia teaching with video clips: TV, movies, YouTube and mtvU in the college classroom, International Journal of Technology in Teaching and Learning, Vol. 91, No. 5
Bloom, B. S.; Engelhart, M. D.; Furst, E. J.; Hill, W. H.; Krathwohl, D. R. (1956). Taxonomy of educational objectives: The classification of educational goals. Handbook I: Cognitive domain. New York: David McKay Company
Clark, R. (1983) Reconsidering research on learning from mediaReview of Educational Research, Vol. 53. No. 4
Gibson, J.J. (1979) The Ecological Approach to Visual Perception Boston: Houghton Mifflin
Koumi, J. (2006) Designing video and multimedia for open and flexible learning. London: Routledge.
Koumi, J. (2015) Learning outcomes afforded by self-assessed, segmented video-print combinationsCogent Education, Vol. 2, No.1
Krathwohl, D.R. (2002) A Revision of Bloom’s Taxonomy: An Overview in Theory into Practice, Vol. 41, No. 4, College of Education, The Ohio State University.
Marton, F. and Säljö, R. (1997) Approaches to learning, in Marton, F., Hounsell, D. and Entwistle, N. (eds.) The experience of learning: Edinburgh: Scottish Academic Press
Mayer, R. E. (2009) Multimedia learning (2nd ed). New York: Cambridge University Press
Olson, D. and Bruner, J. (1974) ‘Learning through experience and learning through media’, in Olson, D. (ed.) Media and Symbols: the Forms of Expression Chicago: University of Chicago Press (out of print)
Salomon, G. (1979) Interaction of Media, Cognition and Learning San Francisco: Jossey-Bass
Salomon, G. (1981) Communication and Education Beverly Hills CA/London: Sage (out of print)
Schramm, W. (1977) Big Media, Little Media Beverly Hills CA/London: Sage
Trenaman, J. (1967) Communication and Comprehension London: Longmans
Activity 8.1: Thinking about the pedagogical differences between media
1. Examine one of your lessons or courses.
• Can you think of content that would best be presented through video or audio rather than through talking or text? What content is still better offered through talking or a textbook? What are your reasons? Are they pedagogical or for other reasons?
• can you think of a skill that you are teaching that could be better done through the use of media that you are not currently using?
• can you think of new learning outcomes that you could achieve through the use of media?
There is no feedback from me on this activity, but the rest of this and the following chapter may help. | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/08%3A_Pedagogical_differences_between_media/08.1%3A_Thinking_about_the_pedagogical_differences_of_media.txt |
Figure 8.2.1 There’s nothing like a good book – or is there?.
8.2.1 The unique pedagogical features of text
Ever since the invention of the Gutenberg press, print has been a dominant teaching technology, arguably at least as influential as the spoken word of the teacher. Even today, textbooks, mainly in printed format, but increasingly also in digital format, still play a major role in formal education, training and distance education. Many fully online courses still make extensive use of text-based learning management systems and online asynchronous discussion forums.
Why is this? What makes text such a powerful teaching medium, and will it remain so, given the latest developments in information technology?
8.2.1.2 Presentational features
Text can come in many formats, including printed textbooks, text messages, novels, magazines, newspapers, scribbled notes, journal articles, essays, novels, online asynchronous discussions and so on.
The key symbol systems in text are written language (including mathematical symbols) and still graphics, which would include diagrams, tables, and copies of images such as photographs or paintings. Colour is an important attribute for some subject areas, such as chemistry, geography and geology, and art history.
Some of the unique presentational characteristics of text are as follows:
• text is particularly good at handling abstraction and generalisation, mainly through written language;
• text enables the linear sequencing of information in a structured format;
• text can present and separate empirical evidence or data from the abstractions, conclusions or generalisations derived from the empirical evidence;
• text’s linear structure enables the development of coherent, sequential argument or discussion;
• at the same time text can relate evidence to argument and vice versa;
• text’s recorded and permanent nature enables independent analysis and critique of its content;
• still graphics such as graphs or diagrams enable knowledge to be presented differently from written language, either providing concrete examples of abstractions or offering a different way of representing the same knowledge.
There is some overlap of each of these features with other media, but no other medium combines all these characteristics, or is as powerful as text with respect to these characteristics.
Earlier (Chapter 2, Section 2.7.3) I argued that academic knowledge is a specific form of knowledge that has characteristics that differentiate it from other kinds of knowledge, and particularly from knowledge or beliefs based solely on direct personal experience. Academic knowledge is a second-order form of knowledge that seeks abstractions and generalizations based on reasoning and evidence.
Fundamental components of or criteria for academic knowledge are:
• codification: knowledge can be consistently represented in some form (words, symbols, video);
• transparency: the source of the knowledge can be traced and verified;
• reproduction: knowledge can be reproduced or have multiple copies;
• communicability: knowledge must be in a form such that it can be communicated and challenged by others.
Text meets all four criteria above, so it is an essential medium for academic learning.
7.2.1.2 Skills development
Because of text’s ability to handle abstractions, and evidence-based argument, and its suitability for independent analysis and critique, text is particularly useful for developing the higher learning outcomes required at an academic level, such as analysis, critical thinking, and evaluation.
It is less useful for showing processes or developing manual skills, for instance.
8.2.2 The book and knowledge
Figure 8.2.2 What is a book? From scrolls to paperbacks to e-books, this one minute video portrays the history and future of books. Click to see the video from the UK Open University (© Open University, 2014)
Although text can come in many formats, I want to focus particularly on the role of the book, because of its centrality in academic learning. The book has proved to be a remarkably powerful medium for the development and transmission of academic knowledge, since it meets all four of the components required for presenting academic knowledge, but to what extent can new media such as blogs, wikis, multimedia, and social media replace the book in academic knowledge?
New media can in fact handle just as well some of these criteria, and provide indeed added value, such as speed of reproduction and ubiquity, but the book still has some unique qualities. A key advantage of a book is that it allows for the development of a sustained, coherent, and comprehensive argument with evidence to support the argument. Blogs can do this only to a limited extent (otherwise they cease to be blogs and become articles or a digital book).
Quantity is important sometimes and books allow for the collection of a great deal of evidence and supporting argument, and allow for a wider exploration of an issue or theme, within a relatively condensed and portable format. A consistent and well supported argument, with evidence, alternative explanations or even counter positions, requires the extra ‘space’ of a book. Above all, books can provide coherence or a sustained, particular position or approach to a problem or issue, a necessary balance to the chaos and confusion of the many new forms of digital media that constantly compete for our attention, but in much smaller ‘chunks’ that are overall more difficult to integrate and digest.
Another important academic feature of text is that it can be carefully scrutinised, analysed and constantly checked, partly because it is largely linear, and also permanent once published, enabling more rigorous challenge or testing in terms of evidence, rationality, and consistency. Multimedia in recorded format can come close to meeting these criteria, but text can also provide more convenience and in media terms, more simplicity. For instance I repeatedly find analysing video, which incorporates many variables and symbol systems, more complex than analysing a linear text, even if both contain equally rigorous (or equally sloppy) arguments.
8.2.2.1 The form and function of a book
Does the form or technological representation of a book matter any more? Is a book still a book if downloaded and read on an iPad or Kindle, rather than as printed text?
For the purposes of knowledge acquisition, it probably isn’t any different. Indeed, for study purposes, a digital version is probably more convenient because carrying an iPad around with maybe hundreds of books downloaded on it is certainly preferable to carrying around the printed versions of the same books. There are still complaints by students about the difficulties of annotating e-books, but this will almost certainly become a standard feature available in the future.
If the whole book is downloaded, then the function of a book doesn’t change much just because it is available digitally. However, there are some subtle changes. Some would argue that scanning is still easier with a printed version. Have you ever had the difficulty of finding a particular quotation in a digital book compared with the printed version? Sure, you can use the search facility, but that means knowing exactly the correct words or the name of the person being quoted. With a printed book, I can often find a quotation just by flicking the pages, because I am using context and rapid eye scanning to locate the source, even when I don’t know exactly what I am looking for. On the other hand, searching when you do know what you are looking for (e.g. a reference by a particular author) is much easier digitally.
When books are digitally available, users can download only the selected chapters that are of interest to them. This is valuable if you know just what you want, but there are also dangers. For instance in my book on the strategic management of technology (Bates and Sangrà, 2011), the last chapter summarizes the rest of the book. If the book had been digital, the temptation then would be to just download the final chapter. You’d have all the important messages in the book, right? Well, no. What you would be missing is the evidence for the conclusions. Now the book on strategic management is based on case studies, so it would be really important to check back with how the case studies were interpreted to get to the conclusions, as this will affect the confidence you would have as a reader in the conclusions that were drawn. If just the digital version of only the last chapter is downloaded, you also lose the context of the whole book. Having the whole book gives readers more freedom to interpret and add their own conclusions than just having a summary chapter.
In conclusion, then, there are advantages and disadvantages of digitizing a book, but the essence of a book is not greatly changed when it becomes digital rather than printed. I have also written about the advantages of publishing an online academic textbook, based on my own experience of writing the first edition of this book, which is now available in 10 languages and has been downloaded over 500,000 times since 2015. For another perspective on this, see Clive Shepherd’s blog: Weighing up the benefits of traditional book publishing.
8.2.2.2 A new niche for books in academia
We have seen historically that new media often do not entirely replace an older medium, but the old medium finds a new ‘niche’. Thus television did not lead to the complete demise of radio. Similarly, I suspect that there will be a continued role for the book in academic knowledge, enabling the book (whether digital or printed) to thrive alongside new media and formats in academia.
However, books that retain their value academically will likely need to be much more specific in their format and their purpose than has been the case to date. For instance, I see no future for books consisting mainly of a collection of loosely connected but semi-independent chapters from different authors, unless there is a strong cohesion and edited presence that provides an integrated argument or consistent set of data across all the chapters. Most of all, books may need to change some of their features, to allow for more interaction and input from readers, and more links to the outside world. It is much more unlikely though that books will survive in a printed format, because digital publication allows for many more features to be added, reduces the environmental footprint, and makes text much more portable and transferable.
Lastly, this is not an argument for ignoring the academic benefits of new media. The value of graphics, video and animation for representing knowledge, the ability to interact asynchronously with other learners, and the value of social networks, are all under-exploited in academia. But text and books are still important.
8.2.3 Text and other forms of knowledge
I have focused particularly on text and academic knowledge, because of the traditional importance of text and printed knowledge in academia. The unique pedagogical characteristics of text though may be less for other forms of knowledge. Indeed, multimedia may have many more advantages in vocational and technical education.
In the k-12 or school sector, text and print are likely to remain important, because reading and writing are likely to remain essential in a digital age, so the study of text (digital and printed) will remain important if only for developing literacy skills.
Indeed, one of the limitations of text is that it requires a high level of prior literacy skills for it to be used effectively for teaching and learning, and indeed much of teaching and learning is focused on the development of skills that enable rigorous analysis of textual materials. Indeed reading ability is one of the core skills identified for the 21st century. Reading and writing literacy is somewhat under attack with the use of truncated language in text messages, automated spelling correction, and emotive symbols in social media. However, we should be giving as much attention to developing literacy skills in using and interpreting multimedia in a digital age.
8.2.4 Assessment
If text is critical for the presentation of knowledge and development of skills in your subject area, what are the implications for assessment? If students are expected to develop the skills that text appears to develop, then presumably text will be an important medium for assessment. Students will need to demonstrate their own ability to use text to present abstractions, argument and evidence-based reasoning.
In such contexts, composed textual responses, such as essays or written reports, are likely to be necessary, rather than multiple-choice questions or multimedia reports.
8.2.5 More evidence, please
Although there has been extensive research on the pedagogical features of other media such as audio, video and computing, text has generally been treated as the default mode, the base against which other media are compared. As a result print in particular is largely taken for granted in academia. We are now though at the stage where we need to pay much more attention to the unique characteristics of text in its various formats, in relation to other media. Until though we have more empirical studies on the unique characteristics of text and print, text will remain central to at least academic teaching and learning.
References
Koumi, J. (1994) Media comparisons and deployment: a practitioner’s view British Journal of Educational Technology, Vol. 25, No. 1.
Koumi, J. (2006) Designing video and multimedia for open and flexible learning. London: Routledge.
Koumi, J. (2015) Learning outcomes afforded by self-assessed, segmented video-print combinations Cogent Education, Vol. 2, No.1
Manguel, A. (1996) A History of Reading London: Harper Collins
Although there are many publications on text, in terms of typography, structure, and its historical influence on education and culture, I could find no publications where text is compared with other modern media such as audio or video in terms of its pedagogical characteristics, although Koumi (2015) has written about text in combination with audio, and Albert Manguel’s book is also fascinating reading from an historical perspective.
However, I am sure that my lack of references is due to my lack of scholarship in the area. If you have suggestions for readings, please send me an email. Also, a study of the unique pedagogical characteristics of text in a digital age might make for a very interesting and valuable Ph.D. thesis.
Activity 8.2 Identifying the unique pedagogical characteristics of text
1. Take one of the courses you are teaching. What key presentational aspects of text are important for this course? Is text the best medium for representing knowledge in your subject area; if not, what concepts or topics would be best represented through other media?
2. Look at the skills listed in Section 1.2 of this book. Which of these skills would best be developed through the use of text rather than other media? How would you do this using text-based teaching?
3. What do you think about books for learning? Do you think the book is dead or about to become obsolete? If you think books are still valuable for learning, what changes, if any, do you think should be made to academic books? What would be lost if books were entirely replaced by new media? What would be gained?
4. Under what conditions would it be more appropriate for students to be assessed through written essays and under what conditions would multimedia portfolios be more appropriate for assessment?
5. Can you think of any other unique pedagogical characteristics of text?
For feedback on this activity, click on the podcast below:
An audio element has been excluded from this version of the text. You can listen to it online here: https://pressbooks.bccampus.ca/teachinginadigitalagev2/?p=201 | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/08%3A_Pedagogical_differences_between_media/08.2%3A_Text.txt |
Figure 8.3.1 Image: © InnerFidelity, 2012
Sounds, such as the noise of certain machinery, or the background hum of daily life, have an associative as well as a pure meaning, which can be used to evoke images or ideas relevant to the main substance of what is being taught. There are, in other words, instances where audio is essential for efficiently mediating certain kinds of information.
Durbridge, 1984
8.3.1 Audio: the unappreciated medium
We have seen that oral communication has a long history, and continues today in classroom teaching and in general radio programming. In this section though I am focusing primarily on recorded audio, which I will argue is a very powerful educational medium when used well.
There has been a good deal of research on the unique pedagogical characteristics of audio. At the UK Open University course teams had to bid for media resources to supplement specially designed printed materials. Because media resources were developed initially by the BBC, and hence were limited and expensive to produce, course teams (in conjunction with their allocated BBC producer) had to specify how radio or television would be used to support learning. In particular, the course teams were asked to identify what teaching functions television and radio would uniquely contribute to the teaching. After allocation and development of a course, samples of the programs were evaluated in terms of how well they met these functions, as well as how the students responded to the programming. In later years, the same approach was used when production moved to audio and video cassettes.
This process of identifying unique roles then evaluating the programs allowed the OU, over a period of several years, to identify which roles or functions were particularly appropriate to different media (Bates, 1984). Koumi (2006), himself a former BBC/OU producer, followed up on this research and identified several more key functions for audio and video. Over a somewhat similar period, Richard Mayer, at the University of California at Santa Barbara, was conducting his own research into the use of multimedia in education (Mayer, 2009).
Although there have been continuous developments of audio technology, from audio-cassettes to Sony Walkman’s to podcasts, the pedagogical characteristics of audio have remained remarkably constant over a fairly long period.
8.3.2 Presentational features
Although audio can be used on its own, it is often used in combination with other media, particularly text. On its own, it can present:
• spoken language (including foreign languages) for analysis or practice;
• music, either as a performance or for analysis;
• students with a condensed argument that may:
• reinforce points made elsewhere in the course;
• introduce new points not made elsewhere in the course;
• provide an alternative viewpoint to the perspectives in the rest of the course;
• analyse or critique materials elsewhere in the course;
• summarize or condense the main ideas or major points covered in the course;
• provide new evidence in support of or against the arguments or perspectives covered elsewhere in the course;
• interviews with leading researchers or experts;
• discussion between two or more people to provide various views on a topic;
• primary audio sources, such as bird song, children talking, eye witness accounts, or recorded performances (drama, concerts);
• analysis of primary audio sources, by playing the source followed by analysis;
• ‘breaking news’ that emphasizes the relevance or application of concepts within the course;
• the instructor’s personal spin on a topic related to the course.
Audio however has been found to be particularly ‘potent’ when combined with text, because it enables students to use both eyes and ears in conjunction. Audio has been found to be especially useful for:
• explaining or ‘talking through’ materials presented through text, such as mathematical equations, reproductions of paintings, graphs, statistical tables, and even physical rock samples.
This technique was later further developed by Salman Khan, but using video to combine voice-over (audio) explanation with visual presentation of mathematical symbols, formulae, and solutions.
8.3.3 Skills development
Because of the ability of the learner to stop and start recorded audio, it has been found to be particularly useful for:
• enabling students through repetition and practice to master certain auditory skills or techniques (e.g. language pronunciation, analysis of musical structure, mathematical computation);
• getting students to analyse primary audio sources, such as children’s use of language, or attitudes to immigration from recordings of interviewed people;
• changing student attitudes by:
• presenting material in a novel or unfamiliar perspective;
• by presenting material in a dramatized form, enabling students to identify with someone with a different perspective.
8.3.4 Strengths and weaknesses of audio as a teaching medium
First, some advantages:
• it is much easier to make an audio clip or podcast than a video clip or a simulation;
• audio requires far less bandwidth than video or simulations, hence downloads quicker and can be used over relatively low bandwidths;
• it is easily combined with other media such as text, mathematical symbols, and graphics, allowing more than one sense to be used and allowing for ‘integration’;
• some students prefer to learn by listening compared with reading;
• audio combined with text can help develop literacy skills or support students with low levels of literacy;
• audio provides variety and another perspective from text, a ‘break’ in learning that refreshes the learner and maintains interest;
• Nicola Durbridge, in her research at the Open University, found that audio increased distance students’ feelings of personal ‘closeness’ with the instructor compared with video or text, i.e. it is a more intimate medium.
In particular, added flexibility and learner control means that students will often learn better from pre-prepared audio recordings combined with accompanying textual material (such as a web site with slides) than they will from a live classroom lecture.
There are also of course disadvantages of audio:
• audio-based learning is difficult for people with a hearing disability;
• creating audio is extra work for an instructor;
• audio is often best used in conjunction with other media such as text or graphics thus adding complexity to the design of teaching;
• recording audio requires at least a minimal level of technical proficiency;
• spoken language tends to be less precise than text.
Increasingly video is now being used to combine audio over images, such as in the Khan Academy, but there are many instances, such as where students are studying from prescribed texts, where recorded audio works better than a video recording.
So let’s hear it for audio!
References
Bates, A. (1984) Broadcasting in Education: An Evaluation London: Constables
Bates, A. (2005) Technology, e-Learning and Distance Education London/New York: Routledge
Durbridge, N. (1984) Audio-cassettes, in Bates, A. (ed.) The Role of Technology in Distance Education London/New York: Croom Hill/St Martin’s Press
EDUCAUSE Learning Initiative (2005) Seven things you should know about… podcasting Boulder CO: EDUCAUSE, June
Koumi, J. (2006). Designing video and multimedia for open and flexible learning. London: Routledge.
Mayer, R. E. (2009). Multimedia learning (2nd ed). New York: Cambridge University Press.
Postlethwaite, S. N. (1969) The Audio-Tutorial Approach to Learning Minneapolis: Burgess Publishing Company
Salmon, G. and Edirisingha, P. (2008) Podcasting for Learning in Universities Milton Keynes: Open University Press
Activity 8.3 Identifying the unique pedagogical characteristics of audio
1. Take one of the courses you are teaching. What key presentational aspects of audio could be important for this course?
2. Look at the skills listed in Section 1.2 of this book. Which of these skills would best be developed through the use of audio rather than other media? How would you do this using audio-based teaching?
3. Under what conditions would it be more appropriate for students to be assessed by asking them to make an audio recording? How could this be done under assessment conditions?
4. To what extent do you think redundancy or duplication between different media is a good thing? What are the disadvantages of covering the same topic through different media?
5. Can you think of any other unique pedagogical characteristics of audio?
Click on the podcast below for feedback on this activity:
An audio element has been excluded from this version of the text. You can listen to it online here: https://pressbooks.bccampus.ca/teachinginadigitalagev2/?p=203 | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/08%3A_Pedagogical_differences_between_media/08.3%3A_Audio.txt |
Figure 8.4.1 An OpenLearn video from the Open University on communications technologies in developing countries. Click on the image to play the video
8.4.1 More power, more complexity
Although there have been massive changes in video technology over the last 25 years, resulting in dramatic reductions in the costs of both creating and distributing video, the unique educational characteristics are largely unaffected. (More recent computer-generated media such as simulations, will be analysed under ‘Computing’, in Section 8.5).
Video is a much richer medium than either text or audio, as in addition to its ability to offer text and sound, it can also offer dynamic or moving pictures. Thus while it can offer all the affordances of audio, and some of text, it also has unique pedagogical characteristics of its own. Once again, there has been considerable research on the use of video in education, and again I will be drawing on research from the Open University (Bates, 1984; 2005; Koumi, 2006) as well as from Mayer (2009).
Click on the links to see examples for many of the characteristics listed below.
8.4.2 Presentational features
Video can be used to:
Figure 8.4.2 Don’t do this yourself at home! Video on the conservation of momentum
8.4.3 Skills development
This usually requires the video to be integrated with student activities. The ability to stop, rewind and replay video becomes crucial for skills development, as student activity usually takes place separately from the actual viewing of the video. This may mean thinking through carefully activities for students related to the use of video.
If video is not used directly for lecturing, research clearly indicates that students generally need to be guided as to what to look for in video, at least initially in their use of video for learning. There are various techniques for relating concrete events with abstract principles, such as through audio narration over the video, using a still frame to highlight the observation, or repeating a small section of the program. Bates and Gallagher (1977) found that using video for developing higher order analysis or evaluation was a teachable skill that needs to be built into the development of a course or program, to get the best results.
Typical uses of video for skills development include:
There are many ways in which video can be used for skills development. Nevertheless, however video is used for skill development, as well as the demonstration of the skill, attention must be paid to ensuring opportunities for student practice and feedback, probably using other media, although it is now increasingly easy for students to make their own videos to demonstrate their skill.
Figure 8.4.3 Demonstrating teaching strategies: kinesthetic learning
8.4.4 Strengths and weaknesses of video as a teaching medium
One factor that makes video powerful for learning is its ability to show the relationship between concrete examples and abstract principles, with usually the sound track relating the abstract principles to concrete events shown in the video (see, for example: Probability for quantum chemistry, UBC). Video is particularly useful for recording events or situations where it would be too difficult, dangerous, expensive or impractical to bring students to such events.
Thus its main strengths are as follows:
• linking concrete events and phenomena to abstract principles and vice versa;
• the ability of students to stop and start, so they can integrate activities with video;
• providing an alternative approach to the presentation of content that can help students having difficulties in learning abstract concepts;
• adding substantial interest to a course by linking it to real world issues;
• a growing amount of freely available, high quality academic videos;
• good for developing some of the higher level intellectual skills and some of the more practical skills needed in a digital age;
• the use of low cost cameras and free editing software enables some forms of video to be cheaply produced.
It should also be remembered that in addition to the features listed above, video can incorporate many of the features of audio as well.
The main weaknesses of video are:
• many faculty have no knowledge or experience in using video other than for recording lecturing;
• there is currently a limited amount of high quality educational video free for downloading, because the cost of developing high quality educational video that exploits the unique characteristics of the medium is still relatively high. Links also often go dead after a while, affecting the reliability of outsourced video. The availability of free material for educational use is improving all the time, but currently finding appropriate and free videos that meet the specific needs of a teacher or instructor can be time-consuming or such material may just not be available or reliable;
• creating original material that exploits the unique characteristics of video is time-consuming, and still relatively expensive, because it usually needs professional video production;
• to get the most out of educational video, students need specially designed activities that often will have to sit outside the video itself;
• students often reject videos that require them to do analysis or interpretation; they often prefer direct instruction that focuses primarily on comprehension. Such students need to be trained to use video differently, which requires time to be devoted to developing such skills.
For these reasons, video is not being used enough in education. When used it is often an afterthought or an ‘extra’, rather than an integral part of the design, or is used merely to replicate a classroom lecture, rather than exploiting the unique characteristics of video.
8.4.5 Assessment
If video is being used to develop the skills outlined in Section 8.4.3, then it is essential that these skills are assessed and count for grading. Indeed, one possible means of assessment might be to ask students to analyse or interpret a selected video, or even to develop their own media project, using video they themselves have collected or produced, using their own devices.
References
Bates, A. (1984) Broadcasting in Education: An Evaluation London: Constables
Bates, A. (2005) Technology, e-Learning and Distance Education London/New York: Routledge
Bates, A. and Gallagher, M. (1977) Improving the Effectiveness of Open University Television Case-Studies and Documentaries Milton Keynes: The Open University, I.E.T. Papers on Broadcasting, No. 77 (out of print – copies available from [email protected])
Koumi, J. (2006). Designing video and multimedia for open and flexible learning. London: Routledge
Mayer, R. E. (2009). Multimedia learning (2nd ed). New York: Cambridge University Press
The University of British Columbia also provides two annotated bibliographies of digital multimedia research. one collated at UBC and one by the University of Central Florida.
Activity 8.4 Identifying the unique pedagogical characteristics of video
1. Take one of the courses you are teaching. What key presentational aspects of video could be important for this course?
2. Look at the skills listed in Section 1.2 of this book. Which of these skills would best be developed through the use of video rather than other media? How would you do this using video-based teaching?
3. Under what conditions would it be more appropriate for students to be assessed by asking them to analyse or make their own video recording? How could this be done under assessment conditions?
4. Type in the name of your topic + video into Google.
• How many videos come up?
• What’s their quality like?
• Could you use any of them in your teaching?
• If so, how would you integrate them into your course?
• Could you make a better video on the topic?
• What would enable you to do this?
Here are some criteria I would apply to what you find:
• it is relevant to what you want to teach;
• it demonstrates clearly a particular topic or subject and links it to what the student is intended to learn;
• it is short and to the point;
• the example is well produced (clear camera work, good presenter, clear audio);
• it provides something that you could not do easily yourself;
• it is freely available for non-commercial use.
For feedback on this activity, and some further comments on the value of video, click on the podcast below:
An audio element has been excluded from this version of the text. You can listen to it online here: https://pressbooks.bccampus.ca/teachinginadigitalagev2/?p=206 | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/08%3A_Pedagogical_differences_between_media/08.4%3A_Video.txt |
Figure 8.5.1 A computer-marked assignment form (University of Western Australia)
8.5.1 A volatile and comprehensive medium
It is debatable whether computing should be considered a medium, but I am using the term broadly, and not in the technical sense of writing code. I prefer ‘computing’ to ‘ICTs’ (information and communications technologies). Computing is a medium while ICT refers more to the technologies used. The Internet in particular is an all-embracing medium that accommodates text, audio, video and computing, as well as providing other elements such as distributed communication and access to educational opportunities. Computing is also still an area that is fast developing, with new products and services emerging all the time. Indeed, I will treat recent developments in social media and some emerging technologies separately from computing, although technically they are sub-categories of computing. Once again, though, social media and some emerging technologies contain affordances that are not so prevalent in more conventional computing-based learning environments.
In such a volatile medium, it would be foolish to be dogmatic about unique media characteristics, but once again, the purpose of this chapter is not to provide a definitive analysis, but a way of thinking about technology that will facilitate an instructor’s choice and use of technology. The focus is: what are the pedagogical affordances of computing that are different from those of other media (other than the important fact that it can embrace all the other media characteristics)?
Although there has been a great deal of research into computers in education, there has been less focus on the specifics of its pedagogical media characteristics, although a great deal of interesting research and development has taken place and continues in human-machine interaction and to a lesser extent in artificial intelligence. Thus I am relying more on analysis and experience than research on the unique affordances or characteristics of computing as an educational medium in this section.
8.5.2 Presentational features
Figure 8.5.2 Screen size can be a real presentational limitation with smaller, mobile devices
Presentation is not really where the educational strength of computing lies. It can represent text and audio reasonably well, and video less well, because of the limited size of the screen (and video often has to share screen space with text), and the bandwidth/pixels/download time required. Screen size can be a real presentational limitation with smaller, mobile devices, although tablets such as the iPad are a major advance in screen quality.
However, unlike the other media, computing enables the end user to interact directly with the medium, to the extent that the end user (in education, the student) can add to, change or interact with the content, at least to a certain extent. Also, more controversially, computing can automatically collect end-user responses for analytics. In this sense, computing comes closer to a complete, if virtual, learning environment.
Thus in presentational terms computing can be used to:
• create and present original teaching content in a rich and varied way (using a combination of text, audio, video and webinars);
• enable access to other sources of secondary ‘rich’ content through the Internet;
• enable students to communicate both synchronously and asynchronously with the instructor and other students;
• structure and manage content through the use of web sites, learning management systems, video servers, and other similar technologies;
• create virtual worlds or virtual environments/contexts through technology such as animations, simulations, augmented or virtual reality, and serious games;
• set multiple-choice tests, automatically mark such tests, and provide immediate feedback to learners;
• enable learners digitally to submit written (essay-type), or multimedia (project-based) assignments through the use of e-portfolios.
8.5.3 Skills development
Skills development in a computing environment will once again depend very much on the epistemological approach to teaching. Computing can be used to focus on comprehension and understanding, through a behaviourist approach to computer-based learning (present/test/feedback). However, the communications element of computing also enables more constructivist approaches, through online student discussion and student-created multimedia work.
Thus computing can be used (uniquely) to:
• develop and test student comprehension of content through computer-based learning/testing;
• develop computer coding and other computer-based skills;
• develop decision-making skills through the use of digitally-based simulations and/or virtual worlds;
• develop skills of reasoning, evidence-based argument, and collaboration through instructor-moderated online discussion forums;
• enable students to create their own artefacts/online multimedia work through the use of e-portfolios, thus improving their digital communication skills as well as assessing better what they have learned;
• develop skills of experimental design, through the use of simulations, virtual laboratory equipment and remote labs;
• develop skills of knowledge management and problem-solving, by requiring students to find, analyse, evaluate and apply content, accessed through the Internet, to real world problems;
• develop spoken and written language skills through both presentation of language and through communication with other students and/or native language speakers via the Internet
• collect data on end-user/student interactions with computer and associated equipment such as mobile phones and tablets for:
• learning analytics, which can be used to identify weaknesses in the design of the teaching, and student success and failure regarding learning outcomes, including skills development, as well as identifying at-risk students,
• adaptive learning, offering learners alternative routes through learning materials, providing an element of personalisation,
• assessment (including monitoring),
• automated or human feedback.
These affordances are in addition to the affordances that other media can support within a broader computing environment.
8.5.4 Strengths and weaknesses of computing as a teaching medium
Many teachers and instructors avoid the use of computing because they fear it may be used to replace them, or because they believe it results in a very mechanical approach to teaching and learning. This is not helped by misinformed computer scientists, politicians and industry leaders who argue that computers can replace or reduce the need for humans in teaching. Both viewpoints show a misunderstanding of both the sophistication and complexity of teaching and learning, and the flexibility and advantages that computing can bring to teaching.
So here are some of the advantages of computing as a teaching medium:
• it is a very powerful teaching medium in terms of its unique pedagogical characteristics, in that it can combine the pedagogical characteristics of text, audio, video and computing in an integrated manner;
• its unique pedagogical characteristics are useful for teaching many of the skills learners need in a digital age;
• computing can enable learners to have more power and choice in accessing and creating their own learning and learning contexts;
• computing can enable learners to interact directly with learning materials and receive immediate feedback, thus, when well designed, increasing the speed and depth of their learning;
• computing can enable anyone with Internet access and a computing device to study or learn at any time or place;
• computing can enable regular and frequent communication between student, instructors and other students;
• computing is flexible enough to be used to support a wide range of teaching philosophies and approaches;
• computing can help with some of the ‘grunt’ work in assessment and tracking of student performance, freeing up an instructor to focus on the more complex forms of assessment and interaction with students.
On the other hand, the disadvantages of computing are:
• many teachers and instructors often have no training in or awareness of the strengths and weaknesses of computing as a teaching medium;
• computing is too often oversold as a panacea for education; it is a powerful teaching medium, but it needs to be managed and controlled by educators;
• the traditional user interface for computing, such as pull-down menus, cursor screen navigation, touch control, and an algorithmic-based filing or storage system, while all very functional, are not intuitive and can be quite restricting from an educational point of view. Voice recognition and search interfaces such as Siri and Alexa are an advance, and have potential for education, but at present they have not been used extensively as educational tools (at least by instructors);
• there is a tendency for computer scientists and engineers to adopt behaviourist approaches to the use of computing for education, which not only alienates constructivist-oriented teachers and learners, but also underestimates or underuses the true power of computing for teaching and learning;
• despite computing’s power as a teaching medium, there are many aspects of teaching and learning that require direct interaction between a student and teacher – and between students – even or especially in a fully online environment (see Chapter 4, Section 4) . The importance of face-to-face, human-to-human contact is probably greater the younger or the less mature the learner, but there will still be many learning contexts where face-to-face contact is necessary or highly desirable even for older or mature learners (this is discussed more in Chapter 10, Section 4). The importance of frequent face-to-face teacher-student interaction is also probably less than many instructors believe, but more than many advocates of computer learning understand. It is not either/or, but finding the right balance in the right context.
• computing needs the input and management of teachers and educators, and to some extent learners, to determine the conditions under which computing can best operate as a teaching medium; and teachers need to be in control of the decisions on when and how to use computing for teaching and learning;
• to use computing well, teachers need to work closely with other specialists, such as instructional designers and computer scientists.
The issue around the value of computing as a medium for teaching is less about its pedagogical value and more about control. Because of the complexity of teaching and learning, it is essential that the use of computing for teaching and learning is controlled and managed by educators. As long as teachers and instructors have control, and have the necessary knowledge and training about the pedagogical advantages and limitations of computing, then computing is an essential medium for teaching in a digital age.
8.5.5 Assessment
There is a tendency to focus assessment in computing on multiple choice questions and ‘correct’ answers. Although this form of assessment has its value in assessing comprehension and for testing a limited range of mechanical procedures, computing also supports a wider range of assessment techniques, from learner-created blogs and wikis to e-portfolios. These more flexible forms of computer-based assessment are more in alignment with measuring the knowledge and skills that many learners will need in a digital age.
Activity 8.5 Identifying the unique pedagogical characteristics of computing
1. Take one of the courses you are teaching. What key presentational aspects of computing could be important for this course?
2. Look at the skills listed in Section 1.2 of this book. Which of these skills would best be developed through the use of computing rather than other media? How would you do this using computer-based teaching?
3. Under what conditions would it be more appropriate in any of your courses for students to be assessed by asking them to create their own multimedia project portfolios rather than through a written exam? What assessment conditions would be necessary to ensure the authenticity of a student’s work? Would this form of assessment be extra work for you?
4. What are the main barriers to your using computing more in your teaching? Philosophical? Practical? Lack of training or confidence in technology use? Or lack of institutional support? What could be done to remove some of these barriers?
For feedback on some of these questions, click on the podcast below:
An audio element has been excluded from this version of the text. You can listen to it online here: https://pressbooks.bccampus.ca/teachinginadigitalagev2/?p=210 | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/08%3A_Pedagogical_differences_between_media/08.5%3A_Computing.txt |
Figure 8.6.1 The range of social media in 2010
Image: © Abhijit Kadle, Upside Learning, 2010
Although social media are mainly Internet-based and hence a sub-category of computing, there are enough significant differences between educational social media use and computer-based learning or online collaborative learning to justify treating social media as a separate medium, although of course they are dependent and often fully integrated with other forms of computing. The main difference is in the extent of control over learning that social media offer to learners.
8.6.1 What are social media?
Around 2005, a new range of web tools began to find their way into general use, and increasingly into educational use. These can be loosely described as social media, as they reflect a different culture of web use from the former ‘centre-to-periphery’ push of institutional web sites.
Here are some of the tools and their uses (there are many more possible examples: click on each example for an educational application):
Type of tool Example Application
Blogs
Stephen’s Web
Online Learning and Distance Education Resources
Allows an individual to make regular postings to the web, e.g. a personal diary or an analysis of current events
Wikis
Wikipedia
UBC’s Math Exam Resources
An “open” collective publication, allowing people to contribute or create a body of information
Social networking FaceBook
LinkedIn
A social utility that connects people with friends and others who work, study and interact with them
Multi-media archives
Podcasts
You-Tube
Flikr
e-portfolios
MIT Open Course-Ware
Allows end users to access, store, download and share audio recordings, photographs, and videos
Multi-player games
Dragonfly
Propulsive Problematics
Enables players to compete or collaborate against each other or a third party/parties represented by the computer, usually in real time
Mobile learning Mobile phones and apps, e.g. Soil TopARgraphy
Enables users to access multiple information formats (voice, text, video, etc.) at any time, any place
Figure 8.6.2 Examples of social media (adapted from Bates, 2011, p.25)
The main feature of social media is that they empower the end user to access, create, disseminate and share information easily in a user-friendly, open environment. Usually the only direct cost is the time of the end-user. There are often few controls over content, other than those normally imposed by a state or government (such as libel or pornography). One feature of such tools is to empower the end-user – the learner or customer – to self-access and manage data (such as online banking) and to form personal networks (for example through FaceBook). For these reasons, some have called social media the ‘democratization’ of the web, although at the same time one could argue that social media are now heavily commercialised through advertising.
In general, social media tools are based on very simple software, in that they have relatively few lines of code. As a result, new tools and applications (‘apps’) are constantly emerging, and their use is either free or very low cost. For a good broad overview of the use of social media in education, see Lee and McCoughlin (2011).
8.6.2 General affordances of social media
The concept of ‘affordances’ is frequently used in discussions of social media. McLoughlin & Lee (2011) identify the following ‘affordances’ associated with social media (although they use the term web 2.0) in general:
• connectivity and social rapport;
• collaborative information discovery and sharing;
• content creation;
• knowledge and information aggregation and content modification.
However, we need to specify more directly the unique pedagogical characteristics of social media.
8.6.3 Presentational characteristics
Social media enable:
• networked multimedia communication between self-organising groups of learners;
• access to rich, multimedia content available over the Internet at any time or place, as long as there is a suitable Internet connection;
• learner-generated multimedia materials;
• opportunities to expand learning beyond ‘closed’ courses and institutional boundaries.
8.6.4 Skills development
Social media,when well designed within an educational framework, can help with the development of the following skills (click on each to see examples):
8.6.5 Strengths and weaknesses of social media
Some of the advantages of social media are as follows:
• they can be extremely useful for developing some of the key skills needed in a digital age, such as digital communication akills;
• they can enable teachers to set online group work, based on cases or projects, and students can collect data in the field using social media such as mobile phones or iPads;
• learners can post media-rich assignments either individually or as a group;
• these assignments when assessed can be loaded by the learner into their own personal learning environment or e-portfolios for later use when seeking employment or transfer to graduate school;
• learners can take more control over their own learning, as we have seen in connectivist MOOCs in Chapter 5 Section 3.2
• through the use of blogs and wikis, courses and learning can be thrown open to the world, adding richness and wider perspectives to learning.
However, many students are not, at least initially, independent learners (see Candy, 1991). Many students come to a learning task without the necessary skills or confidence to study independently from scratch (Moore and Thompson, 1990). They need structured support, structured and selected content, and recognized accreditation. The advent of new tools that give students more control over their learning will not necessarily change their need for a structured educational experience. However, learners can be taught the skills needed to become independent learners (Moore, 1973; Marshall and Rowland, 1993). Social media can make the learning of how to learn much more effective but still only in most cases within an initially structured environment.
The use of social media raises the inevitable issue of quality. How can learners differentiate between reliable, accurate, authoritative information, and inaccurate, biased or unsubstantiated information, if they are encouraged to roam free? What are the implications for expertise and specialist knowledge, when everyone has a view on everything? As Andrew Keen (2007) has commented, ‘we are replacing the tyranny of experts with the tyranny of idiots.’ Not all information is equal, nor are all opinions.
These are key challenges for the digital age, but as well as being part of the problem, social media can also be part of the solution. Teachers can consciously use social media for the development of knowledge management and the responsible use of social media, but the development of such knowledge and skills through the use of social media will need a teacher-supported environment. Many students look for structure and guidance in their learning, and it is the responsibility of teachers to provide it. We therefore need a middle ground between the total authority and control of the teacher, and the complete anarchy of the children roaming free on a desert island in the novel “Lord of the Flies” (Golding, 1954). Social media allow for such a middle ground, but only if as teachers we have a clear pedagogy or educational philosophy to guide our choices and use of the technology.
For more on social media, see Chapter 9, Section 8.
References
Bates, T. (2011) ‘Understanding Web 2.0 and Its Implications for e-Learning’ in Lee, M. and McCoughlin, C. (eds.) Web 2.0-Based E-Learning Hershey NY: Information Science Reference
Candy, P. (1991) Self-direction for lifelong learning San Francisco: Jossey-Bass
Golding, W. (1954) The Lord of the Flies London: Faber and Faber
Keen, A. (2007) The Cult of the Amateur: How Today’s Internet is Killing our Culture New York/London: Doubleday
Lee, M. and McCoughlin, C. (eds.) Web 2.0-Based E-Learning Hershey NY: Information Science Reference
Marshall, L. and Rowland, F. (1993) A Guide to learning independently Buckingham UK: Open University Press
McCoughlin, C. and Lee, M. (2011) ‘Pedagogy 2.0: Critical Challenges and Responses to Web 2.0 and Social Software in Tertiary Teaching’, in Lee, M. and McCoughlin, C. (eds.) Web 2.0-Based E-Learning Hershey NY: Information Science Reference
Moore,M. (1973) Toward a Theory of Independent Learning and Teaching, Journal of Higher Education, Volume 44, No. 9
Moore, M. and Thompson, M. (1990) The Effects of Distance Education: A Summary of the Literature University Park, PA: American Center for Distance Education, Pennsylvania State University
Activity 8.6 Identifying the unique pedagogical characteristics of social media
1. Take one of your courses, and analyse how social media could be used in your course. In particular:
• what new learning outcomes could the use of social media help develop?
• would it be better just to add social media to the course or to re-design it around social media?
2. I have offered only a cursory list of the unique pedagogical characteristics of social media. Can you think of others that have not already been covered in this section?
3. How does this chapter influence your views on students bringing their own devices to class?
4. Are you (still) skeptical about the value of social media in education? What do you see as its downsides?
For feedback on some of these questions and some more general points about social media in education, click on the podcast below.
An audio element has been excluded from this version of the text. You can listen to it online here: https://pressbooks.bccampus.ca/teachinginadigitalagev2/?p=213 | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/08%3A_Pedagogical_differences_between_media/08.6%3A_Social_media.txt |
Figure 8.7a.1 The Gartner Hype Cycle for Emerging Technologies. Image: Wikimedia Commons, 2019
8.7a.1 The challenge of emerging technologies
It is not uncommon for a school principal, a college VP Education, or a university president to go to a conference and come back thrilled about the potential of the latest technology for teaching and learning. They are victims of what the consulting firm Gartner calls the hype cycle.
A new technology triggers excitement, the media picks up on it, the technology reaches a peak of inflated expectations, it starts to get more widely applied, disillusionment sinks in when faced with the realities of implementation, then the technology starts to find its niche as better understanding of its strengths and weaknesses emerge, eventually reaching a plateau of productivity, where it works well within its limits. MOOCs are an excellent example of this, with most knowledgeable observers in 2019 placing them towards the top of the slope of enlightenment or just emerging on to the plateau of productivity (see, for instance, Web Courseworks, 2018).
New technologies that have educational applications are constantly emerging. For instance in the first edition of this book (written in 2015) there was no extensive discussion of artificial intelligence, virtual reality or serious games, yet four years later they are now at the forefront of many discussions about the future of digital learning, which is why this section has been added. There are several other technologies that could be included, but many of these will be subsumed under artificial intelligence.
I will not be able to go into depth about any of these three technologies (each deserves its own book), but they are significant enough to bring them to your attention. Once again, I will focus on their potential affordances, although it must be recognised that with all emerging technology, it may take time to identify all their advantages and disadvantages.
8.7a.2 Serious games
Gartner’s hype cycle is best considered as a way of thinking about emerging technologies, rather than as a factual representation of their development. For instance, serious games are more of a slow burner. There have never been vastly inflated expectations about their likely impact on education; indeed for a long time they have been written off as too expensive or not appropriate for serious education. However, that view has been changing in recent years.
8.7a.2.1 What are serious games?
There are several different definitions of serious games. I have included two definitions that cover both educational and corporate settings.
The Financial Times Lexicon offers the following definition:
Serious games are games designed for a purpose beyond pure entertainment. They use the motivation levers of game design – such as competition, curiosity, collaboration, individual challenge – and game media, including board games through physical representation or video games, through avatars and 3D immersion, to enhance the motivation of participants to engage in complex or boring tasks. Serious games are therefore used in a variety of professional situations such as education, training, assessment, recruitment, knowledge management, innovation and scientific research .
Zhonggen (2019) provides this definition in his comprehensive review of the research on serious games:
Serious games are referred to as entertaining tools with a purpose of education, where players cultivate their knowledge and practice their skills through overcoming numerous hindrances during gaming.
It is important to distinguish between serious games, game-based learning and gamification because of the differences in their purpose, approach and impact on learning.
• Game-based learning refers to “the pedagogical approach of utilizing games in education” (Anastasiadis, Lampropoulos and Siakas, 2018)
• Gamification is defined as the “use of game design elements in non-game contexts” (Deterding et al., 2011)
Note that serious games are not necessarily digital. However, whether digital or not, they are governed by similar principles of of design, such as mechanics, dynamics and aesthetics (Hunicke et al., 2004).
8.7a.2.2 Why use serious games?
The main reasons offered for using games in education are to:
• improve students’ motivation to learn,
• engage learners more deeply in the learning process,
• improve learning outcomes,
• improve attendance and participation.
However, an extensive review of the literature conducted by Dichev and Dicheva in 2017 found that research remains inconclusive on these assumptions. They also found that:
• the practice of gamifying learning has outpaced researchers’ understanding of its mechanisms and methods;
• insufficient high quality evidence exists to support the long-term benefits of serious games in an educational context;
• a limited understanding that how to gamify an activity depends on the specifics of the educational context.
Dichev and Dicheva do conclude though that their study does not mean that gamification cannot be used successfully in a learning context; rather better designs and more research are needed.
Other research tends to be more positive. Hamari et al. (2016) and Clark et al. (2016) found sufficient evidence that, when well designed, and under the right conditions, serious games significantly enhanced student learning relative to nongame conditions.
Zhonggen (2019) found among the ‘ huge number of findings in serious game assisted learning, most …are supportive, coupled with a few negative results.’ However, the main benefits tended to be in the affective domain (student ‘happiness’ and improved social learning and communication) rather than in immediately improved cognitive learning outcomes, except in science (improved retention and holistic understanding), architecture and medicine/health. In the latter, games helped children with autism to learn. Zhonggen reports:
Generally, … medical science has recently witnessed clearly more studies on serious game assisted learning compared with other fields and most of studies in medical science supported use of serious games.’
8.7a.2.3 Examples of serious games
The Digital Education Strategies team (DES) at Ryerson University has participated in the development of several virtual games simulations including:
Games-based learning: Ryerson University’s Academic Integrity office, in collaboration with DES, developed a digital learning game called Academic Integrity in Space to motivate students to complete self-study training and to learn about the academic integrity, values and behaviours expected of students. The game development team’s objectives were to create a well-designed digital game to meet the learning objectives of making choices, learning by doing, and experiencing situations first-hand, through role-playing.
Figure 8.7a.2 Academic Integrity game, Ryerson University. Click on image to play game
Video Game Simulation: [1] A Home Visit game promotes the application of knowledge and skills related to establishing a therapeutic nurse-client relationship and completing a mental health assessment. Students assume the role of a community health nurse assigned to complete a home visit. Video is used to create an authentic experience, and students have to respond to particularly challenging situations, based on procedures taught elsewhere in the course. Depending on the student response, further video segments are used to provide feedback and to continue to scenarios to test the next appropriate procedure. Professors from Centennial College, Ryerson University and George Brown College are developing a series of open access video game simulations through a virtual healthcare experience portal.
Figure 8.7a.3 Home visit video game, Ryerson University. Click on image to see video.
Gamification: Kyle Geske, an instructor at Red River College, Winnipeg, has developed a games-based approach to teaching web design. In his elective course on Full Stack Development of web sites, students have to design a project according to principles provided by the instructor. At each stage of the design process within the project students gain marks, and compete throughout the course with other students, who can see the marks at each stage for all the other students. A student can ‘level up’ their mark by going back and improving on each of the steps of the design. This approach has resulted in an increase in the average end of course grade compared to the more traditional classroom methods. Note this course involves elements of gaming, such as competition, and ‘levelling up’, without using games themselves.
8.7a.2.4 Designing serious games
Zhonggen’s review of the literature (2018) highlighted the importance of the following in effective games design:
• backstory and production,
• realism,
• artificial intelligence and adaptivity,
• interaction,
• feedback and debriefing,
• ease of use,
• surprises.
As a result of this prior research, and under the leadership of Naza Djafarova, the Digital Education Strategies team (DES) at the G. Raymond Chang School for Continuing Education at Ryerson University in Toronto developed a practical design guide (2018) for serious game-based learning, based on a games research process. This guide is an open educational resource and is designed to serve three purposes:
• provide a conceptual framework to guide game design within multidisciplinary teams in higher education;
• offer a methodological guide to running a participatory workshop focused on the pre-production phase of the game development process;
• share resources by making the guide and the design of the workshop available as open educational resources.
The games design methodology is an adaptation of the Design, Play, and Experience (DPE) Framework, developed by Winn (2009). The game development process consists of three phases:
• the pre-production phase, during which brainstorming among team members takes place, leading to the design of a paper prototype of the game;
• the production phase, when the game is developed; and
• the post-production phase, during which the game is tested and refined before being offered to learners.
The Digital Education Strategies team utilized the Design, Play and Experience model to identify four essential educational game elements:
• Learning refers to the content to be learned by players through the game with specific and measurable learning outcomes;
• Storytelling refers to the background story of the game and includes a description of the character(s), the setting, and the ultimate goal of the game;
• Gameplay refers to the way in which the player interacts with the game, or with other players (if a multiplayer game). It encapsulates the type of activity (e.g., puzzle, trivia, etc.) found in the game;
• User Experience refers to the player’s emotions and attitudes while playing the game, as well as how the player interacts with the game.
Figure 8.7a.4 provides a more detailed representation of the various components of the Ryerson serious game design methodology.
Figure 8.7a.4 Serious game design methodology, from Djafarova er al., 2018
The Digital Education Strategies’ report suggests a workshop approach to serious games design, in which all the key stakeholders (content experts, instructional designers, media producers, and so forth) are involved. Brainstorming in the early stages of design is considered essential. Also built into the design is testing and user feedback before releasing the game.
There are probably other effective design approaches, but the above approach highlights the essential multi-disciplinary approach of serious games design.
8.7a.2.5 Unique educational characteristics of serious games
These still need to be clearly identified and validated, but two rather different claims are made for serious games:
• the first is that they can increase student motivation and engagement;
• the second is that games can be particularly useful for developing the following skills:
• problem solving
• communication skills
• decision-making
within specific contexts that approximate to the real world.
8.7a.2.6 Strengths and weaknesses
In terms of the hype cycle, serious games are somewhere along the slope of enlightenment. There is not the research yet to move them into the plateau of productivity, but there is enough evidence from practice that they are gaining traction in education.
However, there are a number of reasons why serious games have not become more prevalent in education. The first is philosophical. There is resistance to the idea of games because some see serious games as an oxymoron. How can a game be serious? Many instructors fear that learning could easily be trivialised through games or that games can cover only a very limited part of what learning should be about – it can’t all be fun; that is not the purpose of education. Similarly, many professional game designers are not interested in developing serious games because they fear that if the primary goal is learning and not enjoyment, a focus on education risks killing the main element of a game: being fun to play.
A more pragmatic reason is cost and quality. The assumed high cost of video games has so far acted as a deterrent in education. There is no obvious business plan to justify the investment. The best selling video games for entertainment for instance cost millions of dollars to produce, on a scale similar to mainstream movies. If games are produced cheaply, won’t the quality – in terms of production standards, narrative/plot, visuals, and learner engagement – suffer, thus making them unattractive for learners?
However, probably the main reason serious games are not more prevalent in education is that most educators simply do not know enough about serious games: what exists, how they can be used, nor how to design them. Experience suggests that there are many possible and realistic applications for serious games in education. There is some evidence (see for instance, Arnab, 2014) that effective serious games can be developed at very little cost.
Nevertheless, there is always a high degree of risk in serious games design. There is no sure way of predicting in advance that a new game will be successful. Some low-cost simple games can work well; some expensively produced games can easily flop. This means careful testing and feedback during development. So serious games should be more seriously considered for teaching in a digital age – but their application needs to be done carefully and professionally.
Thus serious games are a relatively high risk, high return activity for teaching in a digital age. Success in serious games means building on best practices in games design, both within and outside education, sharing costs and experience, and collaboration between institutions and games development teams. However, as teaching in a digital age moves more and more towards high-level skills development, experiential learning, and problem-solving in real world contexts, serious games are bound to play an increasingly important role.
References
Anastasiadis, T. et al. (2018) Digital Game-based Learning and Serious Games in Education International Journal of Advances in Scientific Research and Engineering, Vol. 4, No. 12
Arnab, S. et al. (2014) Mapping learning and game mechanics for serious games analysis. British Journal of Educational Technology, Vol. 46, No. 2, pp 391–411
Deterding, S. et al. (2011) Gamification: Using Game Design Elements in Non-Gaming Contexts in PART 2-Proceedings of the 2011 annual conference extended abstracts on Human Factors in Computing Systems Vancouver BC: CHI
Dichev, C. and Dicheva, D. (2017) Gamifying education: what is known, what is believed and what remains uncertain: a critical review International Journal of Educational Technology in Higher Education, Vol. 14, No. 9
Djafarov, N. et el. (2018) The Art of Serious Game Design Toronto ON: Chang School of Continuing Studies, Ryerson University
Hunicke, R., LeBlanc, M., & Zubek, R. (2004). MDA: A formal approach to game design and game research, in Proceedings of the Challenges in Game AI Workshop, San Jose CA: Nineteenth National Conference on Artificial Intelligence
Winn, B. (2009) ‘The design, play and experience framework’, in R. Ferdig (Ed.), Handbook of research on effective electronic gaming in education. Hershey, PA: IGI Global (pp. 388–401).
Zhonggen, Y. (2019) A Meta-Analysis of Use of Serious Games in Education over a Decade, International Journal of Computer Games Technology, vol. 2019, Article ID 4797032
Activity 8.7a Using and designing seriou games
1. What are your views on serious games and gamification? Do you think they are useful approaches to teaching in a digital age, or are they just a gimmick that avoids the real challenges of learning, especially at a higher education level?
2. Take a look at the Ryerson University’s ‘Art of Serious Games Design’. Is this a model that could be used at your institution? Who would lead this effort? With what learning goals or outcomes could this process help in your program? What would be the main barrier to doing this?
3. What other approaches could be taken to getting serious games used in your teaching?
Click on the podcast below for feedback on this activity.
An audio element has been excluded from this version of the text. You can listen to it online here: https://pressbooks.bccampus.ca/teachinginadigitalagev2/?p=1313 | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/08%3A_Pedagogical_differences_between_media/08.7%3A.a_Emerging_technologies%3A_serious_games_and_gamification.txt |
Figure 8.7.b.1 Video by Atelier 101. Click on image to see video
As with serious games, virtual and augmented reality are technologies that have been around for some time while making a relatively small impact on education in their earlier development. However more recent technological developments that have moved virtual worlds from two-dimensions (such as Second Life) into three-dimensional, deeply immersive environments have brought more attention to their potential in education (for a good overview of the history and potential of augmented and virtual reality in education, see Elmqadden, 2019).
8.7b.1 What are virtual/augmented/mixed reality?
A simple definition of these technologies is ‘human immersion in a synthetic world‘ (Seidel and Chatelier, 1997). The Franklin Institute provides the following more detailed definitions that attempt to distinguish between the different types of ‘synthetic’ worlds:
Augmented reality (AR) adds digital elements to a live view often by using the camera on a smartphone. Examples of augmented reality experiences include Snapchat lenses and the game Pokémon Go.
Virtual reality (VR) implies a complete immersion experience that shuts out the physical world. Using VR devices such as HTC Vive, Oculus Rift or Google Cardboard, users can be transported into a number of real-world and imagined environments such as the middle of a squawking penguin colony or even the back of a dragon.
In a mixed reality (MR) experience, which combines elements of both AR and VR, real-world and digital objects interact. Mixed reality technology is just now starting to take off, with Microsoft’s HoloLens one of the most notable early mixed reality apparatuses.
I will use the term ‘immersive technologies’ for all these technologies. However, verbal descriptions will always be somewhat inadequate in describing what are essentially multi-sensory experiences, combining vision, hearing and movement. These technologies are something that need to be experienced rather than explained if they are to be better understood.
8.7b.2 Why use immersive technologies?
There are several reasons why these technologies are beginning to be used more in education:
• the recent development of relatively low cost and easily wearable end-user technology (headsets in particular);
• deep immersion into three-dimensional, highly realistic learning environments that are strongly compelling/motivating for the end user;
• the ability for end users to manipulate objects within the three dimensional environment;
• more powerful cloud computing technology that allows for the development of more complex and more realistic learning environments, combined with more advanced developments in mobile technologies and high-speed wireless networks;
• the potential for developing a range of skills and knowledge that would be difficult, impossible or dangerous in real-world environments.
8.7b.3 Examples of immersive environments in education
Looking at the challenges above, it may be wondered why anyone would bother with immersive technologies in education. However, the potential benefits have barely been explored. I provide examples here that demonstrate both the potential benefits and how some immersive environments can be developed relatively easily.
8.7b.3.1 Virtual reality
In the Department of Chemistry at the University of Bristol in England, Dr. David Glowacki and his team in their VR laboratory created an interactive molecular dynamics modelling tool in the form of Nano Simbox VR, which allowed anyone to visit and play within the invisible molecular world (O’Connor et al., 2018). The main aim of this particular project was to provide an intuitive feeling of the way molecules operate in multiple dimensions to enable researchers and students to have a better understanding of how nano worlds operate, leading to better hypotheses for testing within this particular domain.
As the authors state in the article:
From a modeling perspective, the nanoscale represents an interesting domain, because the objects of study (for example, molecules) are invisible to the naked eye, and their behavior is governed by physical forces and interactions significantly different from those forces and interactions that we encounter during our day-to-day phenomenological experience. In domains like this, which are imperceptible to the naked eye, effective models are vital to provide the insight required to make research progress….molecular systems typically have thousands of degrees of freedom. As a result, their motion is characterized by a complicated, highly correlated, and elegant many-body dynamical choreography, which is nonintuitive compared to the more familiar mechanics of objects that we encounter in the everyday physical world. Their combined complexity, unfamiliarity, and importance make molecules particularly interesting candidates for investigating the potential of new digital modeling paradigms.
Glowacki and his team in Science Advances (O’Connor et al., 2018) describe how the VR app enabled researchers to:
• easily “grab” individual C60 atoms and manipulate their real-time dynamics to pass the C60 back and forth between each other.
• take hold of a fully solvated benzylpenicillin ligand and interactively guide it to dock it within the active site of the TEM-1 β-lactamase enzyme (with both molecules fully flexible and dynamic) and generate the correct binding mode (33), a process that is important to understanding antimicrobial resistance
• guide a methane molecule (CH4) through a carbon nanotube, changing the screw sense of an organic helicene molecule,
• tie a knot in a small polypeptide [17-alanine (17-ALA)
Figure 8.7b.2 The use of virtual reality to foster chemical intuition Dr. David Glowacki, University of Bristol. Click on the image to see the video.
Building dynamic models that operate not only in real time but also in three dimensions can require not only specialized virtual reality equipment, but more importantly massive amounts of computing power to handle the visual representation and modelling of highly complex, interactive dynamic molecular processes. However, through the use of cloud computing and faster networks, building such models has now become a reality, enabling not only such models to be represented but allowing some degree of real-time manipulation by researchers in different locations but within the same time-frame. The main advantage of the use of a cloud platform is to allow the scaling up of modelling from simple to much more complex dynamic nano interactions and the synchronous sharing of the virtual reality experience with multiple users.
Not all applications of VR though need massive computing power. Other exploratory uses of virtual reality are
8.7b.3.2 Augmented reality
Augmented reality is a simpler immersive technology than virtual reality, often based on apps for mobile phones. For instance, students in the University of British Columbia’s APBI 200 Introduction to Soil Science learn about the effects of topography on the formation of different soil types. The department has developed the Soil TopARgraphy app, which allows viewing and manipulating a terrain model in the Kamloops region of British Columbia. Students learn how topography impacts the distribution of soil orders through its effects on microclimate (i.e. temperature and water). Students are able to view the terrain model with a color-coded elevation map or a satellite image on their mobile phones. Furthermore, students can tap on flags to read about different soil orders, view images, and take a self-study quiz to reinforce their understanding.
For this project, UBC’s Emerging Media Lab built two mobile apps, an AR viewer for students (Android and iOS) and an editor for the instructor (Android). The AR viewer is the app described above to view a predefined terrain. The instructor can customize contents with the supplementary editor app. They can update soil location on terrain, description, image, and quizzes
Figure 8.7.c.3 Screen images from Soil TopARgraphy
Other examples of AR applications from UBC:
8.7b.4 Designing immersive educational environments
This technology is so recent that there are few or no accepted best practices developed yet for educational use. Most educational applications to date have been deliberately exploratory in nature. However, there are several stages of development required that will apply to all educational applications of these technologies:
• identify start-up costs and possible sources of funding: this is not likely to be a cheap exercise, at least initially; for this reason, several universities, such as the University of British Columbia, and Drexel University, have set up their own emerging technologies research labs to experiment with educational applications;
• define learning outcomes/objectives: what is the learner expected to learn? In the early stages of development this may be both a brainstorming exercise (preferably including students/end-users) and an iterative process, because the full potential of the technology is not always clear in first applications. In particular, the instructor needs to have a clear vision of what might be possible using an immersive technology. Thus some familiarity with the technology is essential before starting design;
• determine where the use of this technology fits within the overall design of a course/program: in other words, what knowledge and skills will be developed within the immersive environment, and how does this integrate with what is being taught in the rest of the course/program?
• decide between using an existing immersive design/learning environment that can be applied or adapted relatively easily for ‘local’ use; or designing a new immersive environment from scratch. The latter is obviously more expensive and time-consuming and will require a high level of expertise; as a result the pay-off from design from scratch (improved learning outcomes/return on investment) needs to be worth the effort;
• choice of appropriate/affordable technology. Headsets or mobile apps are the least expensive part of the use of immersive technologies. The main cost will be in developing or adapting the ‘augmented’ or ‘virtual’ world. However, as with serious games, there can be an intermediary step, where an existing ‘world’ can be licensed and adapted for local use (see for instance, Lightwave). In some cases, open access immersive worlds are available for use or adaptation, although they are not common (see OpenSimulator, Art of Illusion, or MayaVerse, for examples.). Often students can be used to help with programming and design of the environment, as part of their studies, but they will need direction as well as the opportunity to offer creative ideas. Truly interactive virtual worlds where learners/users make decisions and the consequences are ‘programmed’ into the learning environment may require large amounts of computing capacity, such as cloud computing;
• to be effective, the VR environment has to be as authentic or realistic as possible. This means paying as much attention to creating the specific learning context. It will be necessary to decide what parts of the learning will best be done outside the VR/AR experience, and which inside. For instance, the procedures for monitoring the state of a nuclear reactor, for identifying critical incidents, for deciding whether or not or when to shut down the reactor, and for actually shutting down the reactor must also be built in to the learning process. Most of this may be taught outside the VR context, but VR can be used to test or develop the skills of applying this knowledge in a realistic, challenging context. In other words, the VR experience needs to be embedded within a broader learning context or environment;
• testing and adaptation: design, at least initially, needs to be an iterative process, where ideas are developed and tried, and feedback received and incorporated into the design;
• assessment: this can be a particular challenge, particularly if new learning outcomes result from the experience. How can assessment best capture what students have learned? Will assessment take place within the ‘virtual’ world, in the real world, or in some other way (and if so, how authentic will such an assessment be)?
• in what ways could the new immersive environment be scaled up to enable costs to be recovered?
• evaluation: what is the best way to evaluate the success or limitations of the design and application of the immersive world? How best to disseminate the knowledge and experience gained?
These may appear formidable challenges, but the potential benefits could be considerable.
8.7b.5 The unique characteristics of immersive technologies
The development of fully immersive technologies is so recent that it is premature to try to identify all the educational affordances that are unique to this medium. New applications are being explored all the time. Most of the evidence is qualitative, based on people’s personal experience of using the technology. Empirical evidence that validates specific educational affordances of VR/AR in terms of improved learning outcomes is currently lacking. However, the potential of VR/AR in terms of assisting learning can be identified.
First of all, many of the affordances or educational characteristics of other media, and in particular video, will apply to VR and AR, but often more intensely, because of the immersive experience.
Virtual and augmented reality applications can provide students with a deep, intuitive understanding of phenomena that are otherwise difficult if not impossible to achieve in other ways. This enables students who often struggle with the abstract nature of an academic subject to understand in more concrete terms what the abstractions mean or represent. This intuitive understanding is critical not only for deeper understanding but also for breakthroughs in research and applications of science.
Educational applications where the cost of alternative or traditional ways of learning are too expensive or too dangerous, will be particularly suitable for virtual reality applications. Examples might be emergency management, such as shutting down an out-of-control nuclear reactor, or defusing a bomb, or managing a fire on an oil tanker, or exploring inside the physical structure of a human brain. In particular, VR would be appropriate for learning in contexts where real environments are not easily accessible, or where learners need to cope with strong emotions when making decisions or operating under pressure in real time.
AR, which is often easier to design and implement, enables learners to practice applications of knowledge in semi-realistic contexts.
However, at the time of writing we are just beginning to understand the potential of this medium. Over time, the educational affordances of this medium will become much clearer.
8.7b.6 Strengths and weaknesses
VR is not just a fad that will disappear. There are already a large number of commercial applications, mainly in entertainment and public relations, but also increasingly for specific areas of education and training. There is already a lot of excellent, off-the-shelf software for creating VR environments, and the cost of hardware is dropping rapidly (although good quality headsets and other equipment are still probably too expensive for required use by large numbers of students).
The fields of application of this technology are unlimited: training in the use of complex equipment, simulation of surgical procedures, architectural design testing, the reconstruction of sites in archeology, virtual museum visits, treatment of pain and phobias, and many other possibilities.
To enable the more emotional aspects of decision making to be handled, the immersive experience needs to be realistic. This will probably require high quality media production. Thus VR may often need to be combined with simulation design, quality media production and powerful computing to be educationally effective, again pushing up the cost. For these reasons, medicine is a particularly likely area for development, where traditional training costs are really high or where training is difficult to provide with real patients.
Once again, though, applications will tend to be very specific to the needs of a particular subject area. This means designers must include subject specialists with a deep understanding of the field who can combine the power of the technology with the needs of learners in a particular learning context. VR in particular requires instructors with imagination and creativity, working with other professionals such as media producers, learners themselves, as well as specialists in VR design.
What has inhibited widespread educational use of earlier two-dimensional VR developments such as Second Life has been the high cost and difficulty of creating the graphics and contexts for learning. Thus even if the hardware and software costs for VR are low enough for individual student use, the high production costs of creating realistic educational contexts and scenarios are likely to inhibit its general use.
Some caution is also needed in assuming that people will behave the same in real life as they do in VR environments. Gallup et al. (2019) found a major difference in the influence of social factors within real-world and virtual environments: social cues in actual reality appear to dominate and supersede those in VR. One of the authors, Alan Kingstone, concluded:
“Using VR to examine how people think and behave in real life may very well lead to conclusions that are fundamentally wrong. This has profound implications for people who hope to use VR to make accurate projections regarding future behaviours. For example, predicting how pedestrians will behave when walking amongst driverless cars, or the decisions that pilots will make in an emergency situation. Experiences in VR may be a poor proxy for real life.”
Rolfsen, 2019
This means we need more experimentation. This is still a relatively new technology, and there may be very simple ways to use it in education that are not costly and meet needs that cannot be easily met in traditional teaching or with other existing technology. For this to happen, though, educators, software developers, and media producers need to come together to play, experiment, test and evaluate.
Nevertheless, VR and AR are exciting technologies with the potential to change radically conventional learning processes.
References
Brandaõ, G. et al. (2018) Virtual Reality as a Tool for Teaching Architecture in Design, User Experience, and Usability: Designing Interactions Las Vegas NV: Proceedings of 7th International Conference, DUXU 2018, held as Part of HCI International 2018,
Connolly, B. (2018) How virtual reality is transforming learning at the University of Newcastle, CIO, 8 March
Elmqadden, N. (2019) Augmented Reality and Virtual Reality in Education: Myth or Reality? International Journal of Emerging Technologies in Learning, Vol. 14, No. 3
Gallup, A. et al. (2019) Contagious yawning in virtual reality is affected by actual, but not simulated, social presence Nature: Scientific Reports, 22 January
O’Connor, M. et al. (2018) Sampling molecular conformations and dynamics in a multiuser virtual reality framework, Science Advances, Vol. 4, No.6, 29 June
Rolfson, E. (2019) People think and behave differently in virtual reality than they do in real life UBC News, 24 January
Seidel, R. and Chatelier, P. (1997) Virtual Reality, Training’s Future?: Perspectives on Virtual Reality and Related Emerging Technologies Berlin: Springer Science & Business Media
Activity 8.7.b Using and designing VR and AR
• Go to YouTube and type in Virtual Reality in the search box (I found about 20 examples). Do any of these videos suggest a way in which VR could be used in the area in which you are teaching (assuming that the resources were available)?
• What are the advantages of VR over video? What can it do educationally that would be more difficult to do using video?
• Your head of department has just come back from a conference and has seen a demonstration of VR. He is very excited and wants the department to ‘become the leader in the state in the use of VR for teaching.’ What questions would you ask of him? (Assume you will still keep your job afterwards!)
Click on the podcast below for my feedback and my personal views on VR for teaching and learning.
An audio element has been excluded from this version of the text. You can listen to it online here: https://pressbooks.bccampus.ca/teachinginadigitalagev2/?p=1544 | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/08%3A_Pedagogical_differences_between_media/08.7%3A.b._Emerging_technologies%3A_virtual_and_augmented_reality.txt |
Figure 8.7.d.1 Games designer at work
8.7d.1 Comparing the three emerging technologies
Section 8.7 has looked at three very different emerging technologies: serious games; immersive technologies; and artificial intelligence. Each has the potential to influence profoundly teaching and learning in a digital age.
Both serious games and immersive technologies such as virtual and augmented reality will be extremely valuable in ‘niche’ areas of teaching and learning. They both have the potential to develop some of the higher order learning skills of problem solving, analysis, intuitive thinking, and creative thinking, and also can be used to develop affective skills, such as empathy.
However, neither is likely to be a ‘core’ technology that will be extensively used across all forms of teaching. Also both need significant investment of time and possibly money if they are to be of good quality for teaching purposes. In particular, they will need a multi-disciplinary team approach to design and development.
Therefore it will be essential to choose the right kind of project, such as topics that are difficult to teach using other methods, or projects aimed at learners who struggle with more conventional teaching methods. Above all, it will be necessary to identify and exploit the optimum educational affordances of these two technologies.
Artificial intelligence is somewhat different to the other two emerging technologies. Artificial intelligence to date manages well the presentation and testing of content acquisition, comprehension and understanding, but so far has not shown much promise in supporting the development or assessment of the higher level cognitive skills needed in a digital age. However, by focusing on supporting learners’ comprehension and understanding, AI can free up human teachers and instructors to focus their time on the development of these higher order skills. Again, this emphasises the importance of teachers and instructors moving their focus away from content delivery – which AI can increasingly manage well – and focusing more on teaching methods that support higher order skills development.
Furthermore these three technologies are not really separate and unrelated but will become increasingly integrated. AI applications could improve the power and range of both serious games and virtual reality. Games can be designed within a virtual reality. The extent to which these technologies become feasible in education will depend heavily on applications outside education which can then be carried over and adapted for educational purposes.
Again though we come back to three critical issues:
• what are the educational goals of the application?
• to what extent does the application help with the development of higher order cognitive and/or affective skills?
• what are the costs and organizational implications of such applications within education?
8.7d.2 Lessons to be learned from the use of emerging technologies
New technology developments show no sign of slowing down. Over time, other new technologies will emerge beside the three technologies discussed in this section. Educators will continue to be challenged to incorporate these new technologies as they emerge. In responding to this challenge, the following needs to be considered:
1. New technologies are not necessarily better than existing technologies for teaching. They may however offer new opportunities for teaching differently, and may enable new or better learning outcomes, as well as improving on existing learning outcomes.
2. Old technologies rarely disappear completely as a result of popular new technologies. Older technologies become more focused and find a niche that they serve best.
3. Most educators will be best served by not jumping on the latest technology bandwagon, but should wait a couple of years for a particular technology to reach at least the Gartner ‘slope of enlightenment’ before experimenting with the new technology.
4. More important than the general characteristics of a new technology is its design and application in education; in other words, how does it perform as an educational medium? Being a big success in the financial sector for instance does not mean a technology will be automatically appropriate for education. Indeed, the technology may need to be heavily adapted or modified to be useful in the educational sector.
5. Given the rate of change and the number of new technologies entering the market, educators need a strong framework or set of criteria for selecting and evaluating technologies, not just emerging technologies but also existing technology. This will be discussing in the following final section of this chapter.
Activity 8.7.d Assessing and developing applications of emerging technologies
• Are there other emerging technologies that you would have chosen over these three?
• How do you think teachers/instructors should react to emerging technologies? Ignore them? Wait for others in education to try them first? Or should they jump in and try a new technology as soon as possible?
• Some institutions such as UBC and Drexel University have set up emerging media labs to encourage faculty to experiment with new technologies. What other methods could be used to encourage teachers and instructors to experiment with new technologies?
For feedback on this activity, and my personal observations on these three emerging technologies, click on the podcast below:
An audio element has been excluded from this version of the text. You can listen to it online here: https://pressbooks.bccampus.ca/teachinginadigitalagev2/?p=1638 | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/08%3A_Pedagogical_differences_between_media/08.7%3A.d_Emerging_technologies%3A_conclusion_and_summary.txt |
Figure 8.7c.1 Image: Applift
8.7c.1 Focusing on AI’s affordances for teaching and learning
Artificial intelligence (AI) is a daunting topic as there are so many issues with respect to its use in education. AI is also currently going through yet another period of extreme hype as a panacea for education, currently being at the top of the peak of inflated expectations, but this hype is driven mainly by successful applications outside the field of education, such as in finance and marketing. Furthermore the term ‘AI’ is increasingly being used (incorrectly) as a general term for any computational activity.
Even in education, there are very different possible areas of application of AI. Zeide (2019) makes a very useful distinction between institutional, student support and instructional applications (Figure 8.7.c.2 below).
Figure 8.7c.2 AI applications in education Image: © Zeide, 2019
Although AI applications for institutional or student support purposes are very important, this chapter is focused on the pedagogical affordances of different media and technologies (what Zeide calls ‘instructional’ applications). In particular, the focus in this section will be on the role of AI as a form of media or technology for teaching and learning, its pedagogical affordances, and its strengths and weaknesses in this area.
Moreover, AI is really a sub-set of computing. Thus all the general affordances of computing in education set out in Section 5 of this chapter will apply to AI. This section aims to tease out the extra potential that AI can offer in teaching and learning. This will mean particularly focusing on its role as a medium rather than a general technology in teaching, which means looking at a wider context than just the computational aspects of AI, in particular its pedagogical role.
8.7c.2 What is artificial intelligence?
The original definition of artificial intelligence by McCarthy (1956, cited in Russell & Norvig, 2010) is:
every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.
Zawacki-Richter et al. (2019), in a review of the literature on AI in higher education, report that those authors that defined artificial intelligence tended to describe it as:
intelligent computer systems or intelligent agents with human features, such as the ability to memorise knowledge, to perceive and manipulate their environment in a similar way as humans, and to understand human natural language.
Klutka et al. (2018) also define AI in terms of what it can do in higher education (Figure 8.7.c.3 below):
Figure 8.7.c. 3 What AI can do in education Image: Klutka et al. (2018)
There are three basic computing requirements that set ‘modern’ AI apart from other computing applications:
• access to massive amounts of data;
• computational power on a large scale to manage and analyze the data;
• powerful and relevant algorithms for the data analysis.
8.7c.3 Why use artificial intelligence for teaching and learning?
There are two somewhat different goals for the general use of artificial intelligence. The first is to increase the efficiency of a system or organization, primarily by reducing the high costs of labour, namely by replacing relatively expensive human workers with relatively less costly machines (automation). In education in particular, the main cost is that of teachers and instructors. Politicians, entrepreneurs and policy makers increasingly see a move to automation as a way of reducing the costs of education.
The second is to increase the effectiveness of teaching and learning, in economic terms to increase outputs: better learning outcomes and greater benefits for the same or more cost. With this goal, AI would be used alongside or supporting teachers and instructors.
Klutka et al. (2018) provide a general statement of the potential of AI in higher education ‘instruction’ through Figure 8.7c.4:
Figure 8.7c.4 Goals for AI in higher education instruction Image: Klutka et al. (2018)
These are understandable goals, but we shall see later in this section that such goals to date are mainly aspirational rather than real.
In terms of this book, a key focus is on developing the knowledge and skills required by learners in a digital age. The key test then for artificial intelligence is to what extent it can assist in the development of these higher level skills.
8.7c.4 Affordances and examples of AI use in teaching and learning
Zawacki-Richter et al. (2019) in a review of the literature on AI in education initially identified 2,656 research papers in English or Spanish, then narrowed the list down by eliminating duplicates, limiting publication to articles in peer-reviewed journals published between 2007 and 2018, and eliminating articles that turned out in the end not to be about the use of AI in education. This resulted in a final 145 articles which were then analysed. Zawacki-Richter et al. then classified these 145 papers into different uses of AI in education. This section draws heavily on this classification. (It should be noted that within the 145 articles, only 92 were focused on instruction/student support. The rest were on institutional uses such as identifying at risk students before admission).
The Zawacki-Richter study offers one insight into the main ways that AI has been used in education for teaching and learning over the ten years between 2007 and 2018, the closest we can come to ‘affordances’. First, three main general ‘instructional’ categories (with considerable overlap) from the study are provided below, followed by some specific examples. (I have omitted Zawacki-Richter et al.’s category of profiling and prediction concerned with administrative issues such as admissions, course scheduling, and early warning systems for students at risk.)
8.7c.4.1 Intelligent tutoring systems (29 out of 92 articles reviewed by Zawacki-Richter et al.)
Intelligent tutoring systems:
• provide teaching content to students and, at the same time, support them by giving adaptive feedback and hints to solve questions related to the content, as well as detecting students’ difficulties/errors when working with the content or the exercises;
• curate learning materials based on student needs, such as by providing specific recommendations regarding the type of reading material and exercises done, as well as personalised courses of action;
• facilitate collaboration between learners, for instance, by providing automated feedback, generating automatic questions for discussion, and the analysis of the process.
8.7c.4.2 Assessment and evaluation (36 out of 92)
AI supports assessment and evaluation through:
• automated grading;
• feedback, including a range of student-facing tools, such as intelligent agents that provide students with prompts or guidance when they are confused or stalled in their work;
• evaluation of student understanding, engagement and academic integrity.
8.7c.4.3 Adaptive systems and personalization (27 out of 92)
AI enables adaptive systems and the personalization of learning by:
• teaching course content then diagnosing strengths or gaps in student knowledge, and providing automated feedback;
• recommending personalized content;
• supporting teachers in learning design by recommending appropriate teaching strategies based on student performance;
• supporting representation of knowledge in concept maps.
Klutka et al. (2018) identified several uses of AI for teaching and learning in universities in the USA. ECoach, developed at the University of Michigan, provides formative feedback for a variety of mainly large classes in the STEM field. It tracks students progress through a course and directs them to appropriate actions and activities on a personalized basis. Other applications listed in the report include sentiment analysis (using students’ facial expressions to measure their level of engagement in studying), an application to monitor student engagement in discussion forums, and organizing commonly shared mistakes in exams into groups for the instructor to respond once to the group rather than individually.
8.7c.4.4 Chatbots
A chatbot is programming that simulates the conversation or ‘chatter’ of a human being through text or voice interactions (Rouse, 2018). Chatbots in particular are a tool used to automate communications with students. Bayne (2014) describes one such application in a MOOC with 90,000 subscribers. Much of the student activity took place outside the Coursera platform within social media. The five academics teaching the MOOC were all active on Twitter, each with large networks, and Twitter activity around the MOOC hashtag (#edcmooc) was high across all instances of the course (for example, a total of around 180,000 tweets were exchanged on the first offering of the MOOC). A ‘Teacherbot’ was designed to roam the tweets using the course Twitter hashtag, using keywords to identify ‘issues’ then choosing pre-designed responses to these issues, which often entailed directing students to more specific research on a topic. For a review of research on chatbots in education, see Winkler and Söllner (2018).
8.7c.4.5 Automated essay grading
Natural language processing (NLP) artificial intelligence systems – often called automated essay scoring engines – are now either the primary or secondary grader on standardized tests in at least 21 states in the USA (Feathers, 2019). According to Feathers:
Essay-scoring engines don’t actually analyze the quality of writing. They’re trained on sets of hundreds of example essays to recognize patterns that correlate with higher or lower human-assigned grades. They then predict what score a human would assign an essay, based on those patterns.
Feathers though claims that research from psychometricians and AI experts show that these tools are susceptible to a common flaw in AI: bias against certain demographic groups (see Ongweso, 2019).
Lazendic et al. (2018) offer a detailed account of the plan for machine grading in Australian high schools. They state:
It is …crucially important to acknowledge that the human scoring models, which are developed for each NAPLAN writing prompt, and their consistent application, ensure and maintain the validity of NAPLAN writing assessments. Consequently, the statistical reliability of human scoring outcomes is fundamentally related to and is the key evidence for the validity of NAPLAN writing marking.
In other words, the marking must be based on consistent human criteria. However, it was announced later (Hendry, 2018) that Australian education ministers agreed not to introduce automated essay marking for NAPLAN writing tests, heeding calls from teachers’ groups to reject the proposal.
Perelman (2013) developed a computer program called the BABEL generator that patched together strings of sophisticated words and sentences into meaningless gibberish essays. The nonsense essays consistently received high, sometimes perfect, scores when run through several different scoring engines. See also Mayfield, 2013, and Parachuri, 2013, for thoughtful analyses of the issues in the automated marking of writing.
At the time of writing, despite considerable pressure to use automated essay grading for standardized exams, the technology still has many questions lingering over it.
8.7c.5 Strengths and weaknesses
There are several ways to assess the value of the teaching and learning affordances of particular applications of AI in teaching and learning:
• is the application based on the three core features of ‘modern’ AI: massive data sets, massive computing power; powerful and relevant algorithms?
• does the application have clear benefits in terms of affordances over other media, and particularly general computing applications?
• does the application facilitate the development of the skills and knowledge needed in a digital age?
• is there unintended bias built into the algorithms? Does it appear to discriminate against certain categories of people?
• is the application ethical in terms of student and teacher/instructor privacy and their rights in an open and democratic society?
• are the results of the application ‘explainable’? For example, can a teacher or instructor or those responsible for the application understand and explain to students how the results or decisions made by the AI application were reached?
These issues are addressed below.
8.7c.5.1 Is it really a ‘modern’ AI application in teaching and learning?
Looking at the Zawacki-Richter et al. study and many other research papers published in peer-reviewed journals, very few so-called AI applications in teaching and learning meet the criteria of massive data, massive computing power and powerful and relevant algorithms. Much of the intelligent tutoring within conventional education is what might be termed ‘old’ AI: there is not a lot of processing going on, and the data points are relatively small. Many so-called AI papers focused on intelligent tutoring and adaptive learning are really just general computing applications.
Indeed, so-called intelligent tutoring systems, automated multiple-choice test marking, and automated feedback on such tests have been around since the early 1980s. The closest to modern AI applications appear to be automated essay grading of standardised tests administered across an entire education system. However there are major problems with the latter. More development is clearly needed to make automated essay grading a valid exercise.
The main advantage that Klutka et al. (2018) identify for AI is that it opens up the possibility for higher education services to become scalable at an unprecedented rate, both inside and outside the classroom. However, it is difficult to see how ‘modern’ AI could be used within the current education system, where class sizes or even whole academic departments, and hence data points, are relatively small, in terms of the numbers needed for ‘modern’ AI. It cannot be said to date that modern AI has been tried, and failed, in teaching and learning; it’s not really even been tried.
Applications outside the current formal system are more realistic, for MOOCs, for instance, or for corporate training on an international scale, or for distance teaching universities with very large numbers of students. The requirement for massive data does suggest that the whole education system could be massively disrupted if the necessary scale could be reached by offering modern AI-based education outside the existing education systems, for instance by large Internet corporations that could tap their massive markets of consumers.
However, there is still a long way to go before AI makes that feasible. This is not to say that there could not be such applications of modern AI in the future, but at the moment, in the words of the old English bobby, ‘Move along, now, there’s nothing to see here.’
However, for the sake of argument, let’s assume that the definition of AI offered here is too strict and that most of the applications discussed in this section are examples of AI. How do these applications of AI meet the other criteria above?
8.7c.5.2 Do the applications facilitate the development of the skills and knowledge needed in a digital age?
This does not seem to be the case in most so-called AI applications for teaching and learning today. They are heavily focused on content presentation and testing for understanding and comprehension. In particular, Zawacki-Richter et al. make the point that most AI developments for teaching and learning – or at least the research papers – are by computer scientists, not educators. Since AI tends to be developed by computer scientists, they tend to use models of learning based on how computers or computer networks work (since of course it will be a computer that has to operate the AI). As a result, such AI applications tend to adopt a very behaviourist model of learning: present/test/feedback. Lynch (2017) argues that:
If AI is going to benefit education, it will require strengthening the connection between AI developers and experts in the learning sciences. Otherwise, AI will simply ‘discover’ new ways to teach poorly and perpetuate erroneous ideas about teaching and learning.
Comprehension and understanding are indeed important foundational skills, but AI so far is not helping with the development of higher order skills in learners of critical thinking, problem-solving, creativity and knowledge-management. Indeed, Klutka et al. (2018) claim that that AI can handle many of the routine functions currently done by instructors and administrators, freeing them up to solve more complex problems and connect with students on deeper levels. This reinforces the view that the role of the instructor or teacher needs to move away from content presentation, content management and testing of content comprehension – all of which can be done by computing – towards skills development. The good news is that AI used in this way supports teachers and instructors, but does not replace them. The bad news is that many teachers and instructors will need to change the way they teach or they will become redundant.
8.7c.5.3 Is there unintended bias in the algorithms?
It could be argued that all AI does is to encapsulate the existing biases in the system. The problem though is that this bias is often hard to detect in any specific algorithm, and that AI tends to scale up or magnify such biases. These are issues more for institutional uses of AI, but machine-based bias can discriminate against students also in a teaching and learning context as well, and especially in automated assessment.
8.7c.5.4 Is the application ethical?
There are many potential ethical issues arising from the use of AI in teaching and learning, mainly due to the lack of transparency in the AI software, and particularly the assumptions embedded in the algorithms. T he literature review by Zawacki-Richter et al. (2019) concluded:
…a stunning result of this review is the dramatic lack of critical reflection of the pedagogical and ethical implications as well as risks of implementing AI applications in higher education.
What data are being collected, who owns or controls it, how is it being interpreted, how will it be used? Policies will need to be put in place to protect students and teachers/instructors (see for instance the U.S. Department of Education’s student data policies for schools), and students and teachers/instructors need to be involved in such policy development.
8.7c.5.5 Are the results explainable?
The biggest problem with AI generally, and in teaching and learning in particular, is the lack of transparency. How did it give me this grade? Why I am directed to this reading rather than that one? Why isn’t my answer acceptable? Lynch (2017) argues that most data collected about student learning is indirect, inauthentic, lacking demonstrable reliability or validity, and reflecting unrealistic time horizons to demonstrate learning.
current examples of AIEd often rely on …. poor proxies for learning, using data that is easily collectable rather than educationally meaningful.’
8.7c.6 Conclusions
8.7c.6.1. Dream on, AI enthusiasts
In terms of what AI is actually doing now for teaching and learning, the dream is way beyond the reality. What works well in finance or marketing or astronomy does not necessarily translate to teaching and learning contexts. In doing the research for this section, it proved very difficult to find any compelling examples of AI for teaching and learning, compared with serious games or virtual reality. It is always hard to prove a negative, but the results to date of applying AI to teaching and learning are extremely limited and disappointing.
This is mainly due to the difficulty of applying ‘modern’ AI at scale in a very fragmented system that relies heavily on relatively small class sizes, programs, and institutions. Probably for modern AI to ‘work’, a totally different organizational structure for teaching and learning would be needed. But be careful what you wish for.
There is a strong affective or emotional influence in learning. Students often learn better when they feel that the instructor or teacher cares. In particular, students want to be treated as individuals, with their own interests, ways of learning, and some sense of control over their learning. Although at a mass level human behaviour is predictable and to some extent controllable, each student is an individual and will respond slightly differently from other students in the same context. Because of these emotional and personal aspects of learning, students need to relate in some way to their teacher or instructor. Learning is a complex activity where only a relatively minor part of the process can be effectively automated. Learning is an intensely human activity, that benefits enormously from personal relationships and social interaction. This relational aspect of learning can be handled equally well online as face-to-face, but it means using computing to support communication as well as delivering and testing content acquisition.
8.7c.6.2 Not fit for purpose
Above all, AI has not progressed to the point yet where it can support the higher levels of learning required in a digital age or the teaching methods needed to do this, although other forms of computing or technology, such as simulations, games and virtual reality, can.
In particular AI developers have been largely unaware that learning is developmental and constructed, and instead have imposed an old and less appropriate method of teaching based on behaviourism and an objectivist epistemology. However, to develop the skills and knowledge needed in a digital age, a more constructivist approach to learning is needed. There has been no evidence to date that AI can support such an approach to teaching, although it may be possible.
8.7c.6.3 AI’s real agenda
AI advocates often argue that they are not trying to replace teachers but to make their life easier or more efficient. This should be taken with a pinch of salt. The key driver of AI applications is cost-reduction, which means reducing the number of teachers, as this is the main cost in education. In contrast, the key lesson from all AI developments is that we will need to pay increased attention to the affective and emotional aspects of life in a robotic-heavy society, so teachers will become even more important.
Another problem with artificial intelligence is that the same old hype keeps going round and round. The same arguments for using artificial intelligence in education go back to the 1980s. Millions of dollars went into AI research at the time, including into educational applications, with absolutely no payoff.
There have been some significant developments in AI since then, in particular pattern recognition, access to and analysis of big data sets, powerful algorithms, leading to formalized decision-making within limited boundaries. The trick though is to recognise exactly what kind of applications these new AI developments are good for, and what they cannot do well. In other words, the context in which AI is used matters, and needs to be taken account of. Teaching and learning is a particularly difficult environment then for AI applications.
8.7c.6.4 Defining AI’s role in teaching and learning
Nevertheless, there is plenty of scope for useful applications of AI in education, but only if there is continuing dialogue between AI developers and educators as new developments in AI become available. But that will require being very clear about the purpose of AI applications in education and being wide awake to the unintended consequences.
In education, AI is still a sleeping giant. ‘Breakthrough’ applications of AI for teaching and learning are probably not going to come from the mainstream universities and colleges, but from outside the formal post-secondary system, through organizations such as LinkedIn, lynda.com, Amazon or Coursera, that have access to large data sets that make the applications of AI scalable and worthwhile (to them). However, this would pose an existential threat to public schools, colleges and universities. The issue then becomes: what system is best to protect and sustain the individual in a digital age: multinational corporations using AI for teaching and learning; or a public education system with human teachers using AI as a support for learners?
The key question then is whether technology should aim to replace teachers and instructors through automation, or whether technology should be used to empower not only teachers but also learners. Above all, who should control AI in education: educators, students, computer scientists, or large corporations? These are indeed existential questions if AI does become immensely successful in reducing the costs of teaching and learning: but at what cost to us as humans? Fortunately AI is not yet in a position to provide such a threat; but it may well do so soon.
References
Bayne, S. (2014) Teacherbot: interventions in automated teaching Teaching in Higher Education, Vol. 20. No.4
Feathers, T. (2019) Flawed Algorithms Are Grading Millions of Students’ Essays, Motherboard: Tech by Vice, 20 August
Hendry, J. (2018) Govts dump NAPLAN robo marking plans itnews, 30 January
Klutka, J. et al. (2018) Artificial Intelligence in Higher Education: Current Uses and Future Applications Louisville Ky: Learning House
Lazandic, G., Justus, J.-A., and Rabinowitz, S. (2018) NAPLAN Online Automated Scoring Research Program: Research Report, Canberra, Australia: Australian Curriculum, Assessment and Reporting Authority
Lynch, J. (2017) How AI will destroy education, buZZrobot, 13 November
Mayfield, E. (2013) Six ways the edX Announcement Gets Automated Essay Grading Wrong, e-Literate, April 8
Ongweso jr. E. (2019) Racial Bias in AI Isn’t Getting Better and Neither Are Researchers’ Excuses Motherboard: Tech by Vice, July 29
Parachuri, V. (2013) On the automated scoring of essays and the lessons learned along the way, vicparachuri.com, July 31
Perelman. L. (2013) Critique of Mark D. Shermis & Ben Hamner, Contrasting State-of-the-Art Automated Scoring of Essays: Analysis, Journal of Writing Assessment, Vol. 6, No.1
Rouse, M. (2019) What is chatbot? Techtarget Network Customer Experience, 5 January
Russell, S. and Norvig, P. (2010) Artificial Intelligence – A Modern Approach New Jersey: Pearson Education
Winkler, R. & Söllner, M. (2018): Unleashing the Potential of Chatbots in Education: A State-Of-The-Art Analysis. Academy of Management Annual Meeting (AOM) Chicago: Illinois
Zawacki-Richter, O. er al. (2019) Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Technology in Higher Education (in press – details to be added)
Zeide, E. (2019) Artificial Intelligence in Higher Education: Applications, Promise and Perils, and Ethical Questions EDUCAUSE Review, Vol. 54, No. 3, August 26
Activity 8.7.c Artificial intelligence
• what do you think about AI for teaching and learning? Is it so esoteric that you can safely not worry about it? Or do you feel you need to be better informed about that it can and cannot do?
• do you agree with the three minimum requirements for modern AI: large data sets, powerful computing capacity, and powerful algorithms? Are there other possible applications of AI that do not need to meet these three criteria?
• can you think of areas of teaching and learning that could generate large data sets even in a class of 30?
• what other skills beside comprehension could AI facilitate? How would it do this?
Click on the podcast below to get some feedback on these questions, plus some of my personal thoughts on AI and teaching and learning:
An audio element has been excluded from this version of the text. You can listen to it online here: https://pressbooks.bccampus.ca/teachinginadigitalagev2/?p=1597 | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/08%3A_Pedagogical_differences_between_media/08.7%3Ac_Emerging_technologies%3A_artificial_intelligence.txt |
Figure 8.8.1 Analysis of different media by pedagogical criteria (adapted from Bates, 2011)
8.8.1 Brief summary of pedagogical differences in media
I will now summarise the unique pedagogical characteristics of the different media discussed in this chapter.
Figure 8.8.1 presents a diagrammatic analysis of various learning media. I have arranged them primarily by where they fit along an epistemological continuum of objectivist (black), constructivist (blue) and connectivist (red), but also I have used two other dimensions, teacher control/learner control, and credit/non-credit. Note that this figure also enables traditional teaching modes, such as lectures and seminars, to be included and compared. Figure 8.8.1 represents my personal interpretation of these media, and other teachers or instructors may well re-arrange the diagram differently, depending on their particular applications of these tools.
Not all tools or media are represented here (for example, audio and video or MOOCs). The position of any particular tool in the diagram will depend on its actual use. Learning management systems can be used in a constructivist way, and blogs can be very teacher-controlled, if the teacher is the only one permitted to use a blog on a course. Badia et al (2011) have shown that educational design and the situational use of technology very much influence whether specific affordances or unique characteristics of a medium are successfully exploited. Student preferences or pre-dispositions can inhibit or support the successful implementation of specific affordances of different media (for instance, computer science students’ preferences for adaptive learning rather than the communication and discussion affordances of ICT – Arenas, 2015).
However, the aim here is not to provide a cast-iron categorization of the affordances of different educational media, but to provide a framework for teachers in deciding which tools and media are most likely to suit a particular teaching approach. Indeed, other teachers may prefer a different set of pedagogical values as a framework for analysis of the different media and technologies.
However, to give an example from Figure 8.8.1, a teacher may use an LMS to organize a set of resources, guidelines, procedures and deadlines for students, who then may use several of the social media, such as photos from mobile phones to collect data. The teacher provides a space and structure on the LMS for students’ learning materials in the form of an e-portfolio, to which students can load their work. Students in small groups can use discussion forums or FaceBook to work on projects together.
The example above is in the framework of a course for credit, but the framework would also fit the non-institutional or informal approach to the use of social media for learning, with a focus on tools such as FaceBook, blogs and YouTube. These applications would be much more learner driven, with the learner deciding on the tools and their uses. The most powerful examples are connectivist or cMOOCs, as we saw in Chapter 5.
8.8.2 Key takeaways
Chapter 8: Key Takeaways
There is a very wide range of media available for teaching and learning. In particular:
• text, audio, video, computing, social media and emerging technologies all have unique characteristics that make them useful for teaching and learning;
• the choice or combination of media will need to be determined by:
• the overall teaching philosophy behind the teaching;
• the presentational and structural requirements of the subject matter or content;
• the skills that need to be developed in learners;
• and not least by the imagination of the teacher or instructor (and increasingly learners themselves) in identifying possible roles for different media;
• learners now have powerful tools through social media for creating their own learning materials or for demonstrating their knowledge;
• courses can be structured around individual students’ interests, allowing them to seek appropriate content and resources to support the development of negotiated competencies or learning outcomes;.
• content is now increasingly open and freely available over the Internet; as a result learners can seek, use and apply information beyond the bounds of what a professor or teacher may dictate;
• students can create their own online personal learning environments;
• many students will still need a structured approach that guides their learning;
• teacher presence and guidance is likely to be necessary to ensure high quality learning via social media;
• teachers need to find the middle ground between complete learner freedom and over-direction to enable learners to develop the key skills needed in a digital age.
References
Arenas, E. (2015) Affordances of Learning Technologies in Higher Education Multicultural Environments The Electronic Journal of e-Learning Volume 13 No. 4 (pp 217-227)
Badia, A., Barberà, E., Guasch, T., Espasa, A. (2011). Technology educational affordance: Bridging the gap between patterns of interaction and technology usage in: Digital Education Review, Vol.19, pp 20-35
Bates, A. (2011) Understanding Web 2.0 and its implications for e-learning, in Lee, M. and McCoughlin, C. (eds.) Web 2.0-based E-learning: applying social informatics for tertiary teaching Hershey PA: Information Science
Activity 8.8 Choosing media for a teaching module
1. Take a module or main topic of a course you are teaching. Identify the key learning outcomes, in terms of skills to be taught, then the content area to be covered.
2. Then look through the key characteristics of each of the media in this chapter, and think how each medium might be used to teach your module. Use your analysis from Activities 8.2 to 8.7 Make a list of the functions you have chosen and their relationship to content and skills in the module.
3. Using Figure 8.8.1, allocate a range of tools and media that you might consider using and place them on the continuum.
4. Are you still happy with your choice?
Don’t worry – we haven’t finished yet. The next chapter will provide a way to make decisions on a more realistic basis. The main purpose here is to get you thinking about possible uses of different media in your subject area.
There is no feedback offered for this activity. Chapter 9 should give some guidance as to the appropriateness of your answers. | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/08%3A_Pedagogical_differences_between_media/08.8%3A_A_framework_for_analysing_the_pedagogical_characteristics_of_educational_media.txt |
Figure 9.9 The SECTIONS model
If you’ve worked your way right through the last three chapters, you are probably feeling somewhat overwhelmed by all the factors to take into consideration when selecting media. It is a complex issue, but if you have read all the previous sections, you are already in a good position to make well informed decisions. Let me explain.
9.10.1 Deductive versus inductive decision-making
Many years ago, when I first developed the ACTIONS model, I was approached by a good friend who worked for a large international computer company. (This was so long ago that data were entered to computers using punched cards). We sat down over a cup of coffee, and he outlined his plan. Here’s how the conversation went.
Pierre. Tony. I’m very excited about your model. We could take it and apply it in every school and university in the world.
Tony. Really? Now how would you do that?
Pierre. Well, you have a set of questions that teachers have to ask for each of the criteria. There is probably a limited set of answers to these questions. You could either work out what those answers are, or collect answers from a representative sample of teachers. You could then give scores to each technology depending on the answers they give. So when a teacher has to make a choice of technology, they would sit down, answer the questions, then depending on their answers, the computer would calculate the best choice of technology. Voilà!
Tony. I don’t think that’s going to work, Pierre.
Pierre: But why not?
Tony. I’m not sure, but I have a gut feeling about this.
Pierre. A gut feeling? My English is not so good. What do you mean by a gut feeling?
Tony. Pierre, your English is excellent. My response is not entirely logical, so let me try and think it through now, both for you and me, why I don’t think this will work. First, I’m not sure there is a limited number of possible answers to each question, but even if there is, it’s not going to work.
Pierre. Well, why not?
Tony. Because I’m not sure how a teacher would score their response to each question and in any case there’s going to be interaction between the the answers to the questions. It’s not the addition of each answer that will determine what technology they might use, but how those answers combine. From a computing point of view, there could be very many different combinations of answers, and I’m not sure what the significant combinations are likely to be with regard to choosing each technology.
Pierre. But we have very big and fast computers, and we can simplify the process through algorithms.
Tony. Yes, but you have to take into account the context in which teachers will make media selections. They are going to be making decisions about media all the time, in many different contexts. It’s just not practical to sit down at a computer, answer all the questions, then wait for the computer’s recommendation.
Pierre. But won’t you give this a try? We can work through all these problems.
Tony. Pierre, I really appreciate your suggestion, but my gut tells me this won’t work, and I really don’t want to waste your time or mine on this.
Pierre. Well, what are you going to tell teachers then? How will they make their decisions?
Tony. I will tell them to use their gut instinct, Pierre – when they have read and applied the ACTIONS model.
This really is a true story, although the actual words spoken may have been different. The fact that we do have artificial intelligence these days that technically could do this hasn’t changed my mind, because what we have in this scenario is a conflict between deductive reasoning (Pierre) and inductive reasoning (Tony).
9.10.1.1 Deductive reasoning
With deductive reasoning, you would do what Pierre suggests: start without any prior conceptions about which technology to use, answer each of the questions I posed at the end of each part of the SECTIONS model, then write down all the possible technologies that would fit the answers to each question, see what technology would best match each of the questions/criteria, and ‘score’ each technology on a recommended scale for each criterion. You would then try to find a way to add all those answers together, perhaps by using a very large matrix, and then end up with a decision about what technology to use.
A major problem though is that every teacher and every learning context is somewhat different each time a decision needs to be made. Experienced teachers in particular will bring a whole lot of knowledge with them – ideas about effective teaching methods, knowledge of the students, the requirement of the content and the skills they are trying to develop at the moment of decision, and above all the context in which the medium will be used (home, classroom, etc.) – before they have to make a decision.
9.10.1.2 Inductive reasoning
My solution is very different from Pierre’s. Mine is a more inductive approach to decision making. The main criterion for inductive logic is as follows:
As evidence accumulates, the degree to which the collection of true evidence statements comes to support a hypothesis, as measured by the logic, should tend to indicate that false hypotheses are probably false and that true hypotheses are probably true.
Stanford Encyclopedia of Philosophy
In terms of selecting media, you probably start with a number of possible technologies in mind at the beginning of the process (hypotheses – or your gut feeling). My suggested process is start with your gut feeling about which technologies you’re thinking of using, but keeping an open mind, then move through all the questions suggested in each of the SECTIONS criteria (that is, collecting evidence for or against your initial ‘gut feeling’.) You then start building more evidence to support or reject the use of a particular medium or technology. By the end of the process you have a ‘probabilistic’ view of what combinations of media will work best for you and why. This is not an exercise you would have to do in detail or even consciously every time. Once you have done it just a few times, the choice of medium or technology in each ‘new’ situation will be quicker and easier, because the brain stores all the previous information and you have a framework (the SECTIONS model) for organising new information as it arrives and integrating it with your previous knowledge.
9.10.1.3 Rapid decision-making
Now you’ve read this chapter you already have a set of questions for consideration (I have listed them all together in Appendix 1 for easy reference). You are now in the same position as the king who asked the alchemist how to make gold. ‘It’s easy’, said the alchemist, ‘so long as you don’t think about elephants.’ Well, having read the three chapters on media in full, you now have the elephants in your head. It will be difficult to ignore them. The brain is in fact a wonderful instrument for making intuitive or inductive decisions of this kind. The trick though is to have all this information somewhere in your head, so you can pull it all out when you need it. The brain does this very quickly. Your decisions won’t always be perfect, but they will be a lot better than if you hadn’t already thought about all these issues, and in life, rough but ready usually beats perfect but late.
9.10.2 Grounding media selection within a course development framework
Media selection does not happen in a vacuum. There are many other factors to consider when designing teaching. In particular, embedded within any decision about the use of technology in education and training will be assumptions about the learning process. We have already seen earlier in this book how different epistemological positions and theories of learning affect the design of teaching, and these influences will also determine a teacher’s or an instructor’s choice of appropriate media. Media selection is just one part of the course design process. It has to fit within the broader framework of course design.
In Figure 9.10.2 below, Hibbitts and Travin’s modification of the ADDIE model (see Chapter 4, Section 3) presents the following learning and technology development model that incorporates the various stages of course design:
Figure 9.10.2 Hibbitts and Travin’s Learning + technology development model
The SECTIONS model is strategy that could be used for assessing the technology fit within this course development process. Whether you are using ADDIE or an agile design approach, then, media selection will be influenced by the other factors in course design, adding more information to be considered. This will all be mixed in with your knowledge of the subject area and its requirements, your beliefs and values about teaching and learning, and a lot of emotion as well.
All this further reinforces the inductive approach to decision making that I have suggested. Don’t underestimate the power of your brain – it’s far better than a computer for this kind of decision-making. But it’s important to have the necessary information, as far as possible. So if you skipped a part of this chapter, or the previous two chapters on media, you might want to go back over it!
Activity 9.10: Choosing media and technologies
1. Choose the same course that you chose for Activity 9.1.
2. Go to Appendix 1, and see how many of the questions you can answer. Use Chapter 9 to help, if necessary, including your answers to some the activities in Chapter 9.
3. When you have answered as many questions as you can from Appendix 1, what media or technologies will you now think of using. How does this differ from your original list? If there are changes, why?
Again, no feedback is provided as each context will be different.
Chapter 9 Key Takeaways
1. Selecting media and technologies is a complex process, involving a very wide range of interacting variables.
2. There is currently no generally accepted theory or process for media selection. The SECTIONS model however provides a set of criteria or questions the result of which can help inform an instructor when making decisions about which media or technologies to use.
3. Because of the wide range of factors influencing media selection and use, an inductive or intuitive approach to decision-making, informed by a careful analysis of all the criteria in the SECTIONS framework, is one practical way to approach decision-making about media and technologies for teaching and learning.
4. However, media selection needs to be integrated within the broader framework of course design. | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/09%3A_Choosing_and_using_media_in_education%3A_the_SECTIONS_model/09.1%3A0_Deciding.txt |
Figure 9.1.1 The SECTIONS model
9.1.1 What the literature tells us
Given the importance of the topic, there is relatively little literature on how to choose appropriate media or technologies for teaching. There was a flurry of not very helpful publications on this topic in the 1970s and 1980s, but relatively little since (Baytak, undated). Indeed, Koumi (1994) stated that:
there does not exist a sufficiently practicable theory for selecting media appropriate to given topics, learning tasks and target populations . . . the most common practice is not to use a model at all. In which case, it is no wonder that allocation of media has been controlled more by practical economic and human/political factors than by pedagogic considerations (p. 56).
Mackenzie (2002) comments in a similar vein:
When I am discussing the current state of technology with teachers around the country, it becomes clear that they feel bound by their access to technology, regardless of their situation. If a teacher has a television-computer setup, then that is what he or she will use in the classroom. On the other hand, if there is an LCD projector hooked up to a teacher demonstration station in a fully equipped lab, he or she will be more apt to use that set up. Teachers have always made the best of whatever they’ve got at hand, but it’s what we have to work with. Teachers make due.
Mackenzie (2002) has suggested building technology selection around Howard Gardner’s multiple intelligences theory (Gardner, 1983, 2006), following the following sequence of decisions:
learner → teaching objective → intelligences → media choice.
Mackenzie then allocates different media to support the development of each of Gardner’s intelligences. Gardner’s theory of multiple intelligences has been widely tested and adopted, and Mackenzie’s allocations of media to intelligences make sense intuitively, but of course it is dependent on teachers and instructors applying Gardner’s theory to their teaching.
A review of more recent publications on media selection suggests that despite the rapid developments in media and technology over the last 20 years, my ACTIONS model (Bates, 1995) is one of the major models still being applied, although with further amendments and additions (see for instance, Baytak, undated; Lambert and Williams, 1999; Koumi, 2006). Indeed, I myself modified the ACTIONS model, which was developed for distance education, to the SECTIONS model to cover the use of media in campus-based as well as distance education (Bates and Poole, 2003).
Patsula (2002) developed a model called CASCOIME which includes some of the criteria in the Bates models, but also adds additional and valuable criteria such as socio-political suitability, cultural friendliness, and openness/flexibility, to take into account international perspectives. Zaied (2007) conducted an empirical study to test what criteria for media selection were considered important by faculty, IT specialists and students, and identified seven criteria. Four of these matched or were similar to Bates’ criteria. The other three were student satisfaction, student self-motivation and professional development, which are more like conditions for success and are not really easy to identify before making a decision.
Koumi (2006) and Mayer (2009) have come closest to to developing models of media selection. Mayer has developed twelve principles of multimedia design based on extensive research, resulting in what Mayer calls a cognitive theory of multimedia learning. (For an excellent application of Mayer’s theory, see UBC Wikis.) Koumi (2015) more recently has developed a model for deciding on the best mix and use of video and print to guide the design of xMOOCs.
Mayer’s approach is valuable at a more micro-level when it comes to designing specific multimedia educational materials, as is Koumi’s work. Mayer’s cognitive theory of multimedia design suggests the best combination of words and images, and rules to follow such as ensuring coherence and avoiding cognitive overload. When deciding to use a specific application of multimedia, it provides very strong guidelines. It is nevertheless more difficult to apply at a macro level. Because Mayer’s focus is on cognitive processing, his theory does not deal directly with the unique pedagogical affordances or characteristics of different media. Neither Mayer nor Koumi address non-pedagogical issues in media selection, such as cost or access. Mayer and Koumi’s work is not so much competing as complementary to what I am proposing. I am trying to identify which media (or combinations of media) to use in the first place. Mayer’s theory then would guide the actual design of the application. I will discuss Mayer’s twelve principles further in Section 5 of this chapter, which deals with teaching functions.
Puentedura’s SAMR model (2014), discussed in Chapter 7, Section 4, is valuable for assessing the choice of a particular medium, but it focuses solely on pedagogical issues, particularly in terms of whether the choice augments or transforms learning. Although this is a powerful criterion for media selection, the SAMR model does not take into account other essential factors in media selection, such as cost or ease of use.
It is not surprising that there are not many models for media selection. The models developed in the 1970s and 1980s took a very reductionist, behaviourist approach to media selection, resulting in often several pages of decision-trees, which are completely impractical to apply, given the realities of teaching, and yet these models still included no recognition of the unique affordances of different media. More importantly, technology is subject to rapid change, there are competing views on appropriate pedagogical approaches to teaching, and the context of learning varies so much. Finding a practical, manageable model founded on research and experience that can be widely applied has proved to be challenging.
9.1.2 Why we need a model
At the same time, every teacher, instructor, and increasingly learner, needs to make decisions in this area, often on a daily basis. A model for technology selection and application is needed therefore that has the following characteristics:
• it will work in a wide variety of learning contexts;
• it allows decisions to be taken at both a strategic, institution-wide level, and at a tactical, instructional, level;
• it gives equal attention to educational and operational issues;
• it will identify critical differences between different media and technologies, thus enabling an appropriate mix to be chosen for any given context;
• it is easily understood, pragmatic and cost-effective;
• it will accommodate new developments in technology.
For these reasons, then, I will continue to use the Bates’ SECTIONS model, with some modifications to take account of recent developments in technology, research and theory. The SECTIONS model is based on research, has stood the test of time, and has been found to be practical. SECTIONS stands for:
• S tudents
• E ase of use
• C ost
• T eaching functions, including pedagogical affordances of media
• I nteraction
• O rganizational issues
• N etworking
• S ecurity and privacy
I will discuss each of these criteria in the following sections, and will then suggest how to apply the model.
References
Bates, A. (1995) Teaching, Open Learning and Distance Education London/New York: Routledge
Bates, A. and Poole, G. (2003) Effective Teaching with Technology in Higher Education San Francisco: Jossey-Bass/John Wiley and Son
Baytak, A.(undated) Media selection and design: a case in distance educationAcademia.edu
Gardner, H. (1983) Frames of Mind: The Theory of Multiple Intelligences New York: Basic Books
Gardner, H. (2006) Multiple Intelligences: New Horizons and Theory in Practice New York: Basic Books
Koumi, J. (1994). Media comparisons and deployment: A practitioner’s view British Journal of Educational Technology, Vol. 25, No. 1
Koumi, J. (2006). Designing video and multimedia for open and flexible learning London: Routledge
Koumi, J. (2015) Learning outcomes afforded by self-assessed, segmented video–print combinationsCogent Education, Vol. 2, No.1
Lambert, S. and Williams R. (1999) A model for selecting educational technologies to improve student learning Melbourne, Australia: HERDSA Annual International Conference, July
Mackenzie, W. (2002) Multiple Intelligences and Instructional Technology: A Manual for Every Mind Eugene, Oregon: ISTE
Mayer, R. E. (2009). Multimedia Learning (2nd ed). New York: Cambridge University Press.
Patsula, P. (2002) Practical guidelines for selecting media: An international perspectiveThe Useableword Monitor, February 1
Puentedura, R. (2014) SAMR and Bloom’s Taxonomy: Assembling the Puzzle common sense education, September 24
UBC Wikis (2014)Documentation: Design Principles for Multimedia Vancouver BC: University of British Columbia
Zaied, A. (2007) A Framework for Evaluating and Selecting Learning Technologies The International Arab Journal of Information Technology, Vol. 4, No. 2
Activity 9.1 Making a preliminary decision on media selection
1. Choose a course that you are teaching or may be teaching. Identify what media or technologies you might be interested in using. Keep a note of your decision and your reasons for your choice of media/technologies.
When you have finished reading this chapter you will be asked to do a final activity (Activity 9.10) and then you can compare your answers to both this activity and Activity 9.10 after reading the whole chapter.
There is no feedback provided for this activity. | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/09%3A_Choosing_and_using_media_in_education%3A_the_SECTIONS_model/09.1%3A_Models_for_media_selection.txt |
The Malaysian Ministry of Education announced in 2012 that it will enable students to bring handphones to schools under strict guidelines
Image: © NewStraightsTimes, 2012
The first criterion in the SECTIONS model is students. At least three issues related to students need to be considered when choosing media and technology:
• student demographics;
• access; and
• differences in how students learn.
9.2.1 Student demographics
One of the fundamental changes resulting from mass higher education is that university and college teachers must now teach an increasingly diverse range of students. This increasing diversity of students presents major challenges for all teachers, not just post-secondary teachers. However, it has been less common for instructors at a post-secondary level to vary their approach within a single course to accommodate to learner differences, but the increasing diversity of students now requires that all courses should be developed with a wide variety of approaches and ways to learn if all students in the course are to be taught well.
In particular, it is important to be clear about the needs of the target group. First and second year students straight from high school are likely to require more support and help studying at a university or college level. They are likely to be less independent as learners, and therefore it may be a mistake to expect them to be able to study entirely through the use of technology. However, technology may be useful as a support for classroom teaching, especially if it provides an alternative approach to learning from the face-to-face teaching, and is gradually introduced, to prepare them for more independent study later in a program.
On the other hand, for students who have already been through higher education as a campus student, but are now in the workforce, a program delivered entirely by technology at a distance is likely to be attractive. Such students will have already developed successful study skills, will have their own community and family life, and will welcome the flexibility of studying this way.
Third and fourth year undergraduate students may appreciate a mix of classroom-based and online study or even one or two fully online courses, especially if some of their face-to-face classes are closed to further enrolments, or if students are working part-time to help cover some of the costs of being at college.
Lastly, within any single class or group of learners, there will be a wide range of differences in prior knowledge, language skills, and preferred study styles.The intelligent use of media and technology can help accommodate these differences. In particular, if you are trying to reach students in remote areas, or homeless or poor people, or students with physical disabilities, then this too should influence your choice of technology. Indeed, for most courses, there is likely to be a mix of different student needs, which suggests that a multi-media approach will be necessary to accommodate all student needs.
So, once again, it is important to know your students, and to keep this in mind when making decisions about what media or technology to use. This will be discussed further in Chapter 10.
9.2.2 Access
Of all the criteria in determining choice of technology, this is perhaps the most discriminating. No matter how powerful in educational terms a particular medium or technology may be, if students cannot access it in a convenient and affordable manner they cannot learn from it. Thus video streaming may be considered a great way to get lectures to students off campus, but if they do not have Internet access at home, or if it takes four hours or a day’s wages to download, then forget it. Difficulty of access is a particular restriction on using xMOOCs in developing countries. Even if potential learners have Internet or mobile phone access, which 3.8 billion globally still do not (ITU, 2018), it often costs a day’s wages to download a single YouTube video – see Marron, Missen and Greenberg, 2014.
Any teacher or instructor intending to use computers, tablets or mobile phones for teaching purposes needs answers to a number of questions:
• what is the institutional policy with regard to students’ access to a computer, tablets or mobile phones?
• can students use any device or is there a limited list of devices that the institution will support?
• is the medium or software chosen for teaching compatible with all makes of devices students might use?
• is the network adequate to support any extra students that this initiative will add?
• who else in the institution needs to know that you are requiring students to use particular devices?
If students are expected to provide their own devices (which increasingly makes sense):
• what kind of device do they need: one at home with Internet access or a portable that they can bring on to campus – or one that can be used both at home and on campus?
• what kind of applications will they need to run on their device(s) for study purposes?
• will they be able to use the same device(s) across all courses, or will they need different software/apps and devices for different courses?
• what skills will students need in operating the devices and the apps that will be run on them?
• if students do not have the skills, would it still be worth their learning them, and will there be time set aside in the course for them to learn these skills?
Students (as well as the instructor) need to know the answers to these questions before they enrol in a course or program. In order to answer these questions, you and your department must know what students will use their devices for. There is no point in requiring students to go to the expense of purchasing a laptop computer if the work they are required to do on it is optional or trivial. This means some advance planning on your part:
• what are the educational advantages that you see in student use of a particular device?
• what will students need to do on the device in your course?
• is it really essential for them to use a device in these ways, or could they easily manage without the device? In particular, how will assessment be linked to the use of the device?
It will really help if your institution has good policies in place for student technology access (see Section 9.7). If the institution does not have clear policies or infrastructure for supporting the technologies you want to use, then your job is going to be a lot harder.
The answer to the question of access and the choice of technology will also depend somewhat on the mandate of the institution and your personal educational goals. For instance, highly selective universities can require students to use particular devices, and can help the relatively few students who have financial difficulties in purchasing and using specified devices. If though the mandate of the institution is to reach learners denied access to conventional institutions, equity groups, the unemployed, the working poor, or workers needing up-grading or more advanced education and training, then it becomes critical to find out what technology they have access to or are willing to use. If an institution’s policy is open access to anyone who wants to take its courses, the availability of equipment already in the home (usually purchased for entertainment purposes) becomes of paramount importance.
Another important factor to consider is access for student with disabilities. This may mean providing textual or audio options for deaf and visually impaired students respectively. Fortunately there are now well established practices and standards under the general heading of Universal Design standards. Universal Design is defined as follows:
Universal Design for Learning, or UDL, refers to the deliberate design of instruction to meet the needs of a diverse mix of learners. Universally designed courses attempt to meet all learners’ needs by incorporating multiple means of imparting information and flexible methods of assessing learning. UDL also includes multiple means of engaging or tapping into learners’ interests. Universally designed courses are not designed with any one particular group of students with a disability in mind, but rather are designed to address the learning needs of a wide-ranging group.
Brokop, F. (2008)
Most institutions with a centre for supporting teaching and learning will be able to provide assistance to faculty to ensure the course meets universal design standards. For instance, BCcampus has produced an accessibility toolkit (Coolidge et l., 2018) and Norquest College, Alberta, has published a detailed guide to ensuring online materials are accessible for persons with disabilities.
9.2.3 Student differences with respect to learning with technologies
It may seem obvious that different students will have different preferences for different kinds of technology or media. The design of teaching would cater for these differences. Thus if students are ‘visual’ learners, they would be provided with diagrams and illustrations. If they are auditory learners, they will prefer lectures and podcasts. It might appear then that identifying dominant learning styles should then provide strong criteria for media and technology selection. However, it is not as simple as that.
McLoughlin (1999), in a thoughtful review of the implications of the research literature on learning styles for the design of instructional material, concluded that instruction could be designed to accommodate differences in both cognitive-perceptual learning styles and Kolb’s (1984) experiential learning cycle. In a study of new intakes conducted over several years at the University of Missouri-Columbia, using the Myers-Briggs inventory, Schroeder (1993) found that new students think concretely, and are uncomfortable with abstract ideas and ambiguity.
However, a major function of a university education is to develop skills of abstract thinking, and to help students deal with complexity and uncertainty. Perry (1970) found that learning in higher education is a developmental process. It is not surprising then that many students enter college or university without such ‘academic’ skills. Indeed, there are major problems in trying to apply learning styles and other methods of classifying learner differences to media and technology selection and use. Laurillard (2001) makes the point that looking at learning styles in the abstract is not helpful. Learning has to be looked at in context. Thinking skills in one subject area do not necessarily transfer well to another subject area. There are ways of thinking that are specific to different subject areas. Thus logical-rational thinkers in science do not necessarily make thoughtful husbands, or good literary critics.
Part of a university education is to understand and possibly challenge predominant modes of thinking in a subject area. While learner-centered teaching is important, students need to understand the inherent logic, standards, and values of a subject area. They also need to be challenged, and encouraged to think outside the box. In particular, at a university level we need strategies to gradually move students from concrete learning based on personal experience to abstract, reflective learning that can then be applied to new contexts and situations. Technology can be particularly helpful for that, as we saw in Chapter 8.
Thus when designing courses, it is important to offer a range of options for student learning within the same course. One way to do this is to make sure that a course is well structured, with relevant ‘core’ information easily available to all students, but also to make sure that there are opportunities for students to seek out new or different content. This content should be available in a variety of media such as text, diagrams, and video, with concrete examples explicitly related to underlying principles. The increasing availability of open educational resources (discussed in Chapter 11.2) makes the provision of this ‘richness’ of possible content much more viable.
Similarly, technology enables a range of learner activities to be made available, such as researching readings on the Web, online discussion forums, synchronous presentations, assessment through e-portfolios, and online group work. The range of activities increases the likelihood that a variety of learner preferences are being met, and also encourages learners to involve themselves in activities and approaches to learning where they may initially feel less comfortable. Thus it is important to ensure that students have a wide range of media (text, audio, video, computing) within a course or program.
Lastly, one should be careful in the assumptions made about student preferences for learning through digital technologies. On the one hand, technology ‘boosters’ such as Mark Prensky (2001) and Don Tapscott (2008) have argued that today’s ‘digital natives’ are different from previous generations of students. They argue that today’s students live within a networked digital universe and therefore expect their learning also to be all digitally networked. It is also true that professors in particular tend to underestimate students’ access to advanced technologies (professors are often late adopters of new technology), so you should always try to find up-to-date information on what devices and technologies students are currently using, if you can.
On the other hand, it is also dangerous to assume that all students are highly ‘digital literate’ and are demanding that new technologies should be used in teaching. Jones and Shao (2011) conducted a thorough review of the literature on ‘digital natives’, with over 200 appropriate references, including surveys of relevant publications from countries in Europe, Asia, North America, Australia and South Africa. They concluded that:
• students vary widely in their use and knowledge of digital media;
• the gap between students and their teachers in terms of digital literacy is not fixed, nor is the gulf so large that it cannot be bridged;
• there is little evidence that students enter university with demands for new technologies that teachers and universities cannot meet;
• students will respond positively to changes in teaching and learning strategies that include the use of new technologies that are well conceived, well explained and properly embedded in courses and degree programmes. However there is no evidence of a pent-up demand amongst students for changes in pedagogy or of a demand for greater collaboration;
• the development of university infrastructure, technology policies and teaching objectives should be choices about the kinds of provision that the university wishes to make and not a response to general statements about what a new generation of students are demanding;
• the evidence indicates that young students do not form a generational cohort and they do not express consistent or generationally organised demands, thus challenging general assumptions about the differences between post-millennials, millennials, Generation X and boomers in the way that they learn.
Graduating students that have been interviewed about learning technologies at the University of British Columbia made it clear that they will be happy to use technology for learning so long as it contributes to their success (in the words of one student, ‘if it will get me better grades’) but the students also made it clear that it was the instructor’s responsibility to decide what technology was best for their studies.
It is also important to pay attention to what Jones and Shao are not saying. They are not saying that social media, personal learning environments, or collaborative learning are inappropriate, nor that the needs of students and the workforce are unchanging or unimportant, but the use of these tools or approaches should be driven by a holistic look at the needs of all students, the needs of the subject area, and the learning goals relevant to a digital age, and not by an erroneous view of what a particular generation of students are demanding.
In summary, one great advantage of the intelligent application of technology to teaching is that it provides opportunities for students to learn in a variety of ways, thus adapting the teaching more easily to student differences. Thus, the first step in media selection is to know your students, their similarities and differences, what technologies they already have access to, and what digital skills they already possess or lack that may be relevant for your courses. This is likely to require the use of a wide range of media within the teaching to accommodate these differences.
9.2.4 The information you need about your students
It is critical to know your students. In particular, you need the following information to provide an appropriate context for decisions about media and technology:
1. What is the mandate or policy of your institution, department or program with respect to student access in general (selective vs open; accommodation of disabilities, etc.)? How will students who do not have access to a chosen technology be supported?
2. What are the likely demographics of the students you will be teaching? How appropriate is the technology you are thinking of using for these students?
3. If your students are to be taught at least partly off campus, to which technologies are they likely to have convenient and regular access at home or work?
4. If students are to be taught at least partly on campus, what is – or should be – your or your department’s policy with regard to students’ access to devices in class?
5. What digital skills do you expect your students to have before they start the program?
6. If students are expected to provide their own access to technology, will you be able to provide unique teaching experiences that will justify the purchase or use of such technology?
7. What prior approaches to learning are the students likely to bring to your program? How suitable are such prior approaches to learning likely to be to the way you need to teach the course? How could technology be used to cater for student differences in learning?
There are many different ways to get the information needed to answer these questions. In many cases, you will still have to make decisions on insufficient evidence, but the more accurate information you have about your potential students, the better your likely choice of media and technology. Almost certainly, though, you will have a variety and diversity of students, so the design of your teaching will need to accommodate this.
Activity 9.2: Knowing your students
• How many of the questions in Section 9.2.4 can you answer off the top of your head?
• What additional information do you need, and where can you find it?
There is no feedback provided on these questions. | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/09%3A_Choosing_and_using_media_in_education%3A_the_SECTIONS_model/09.2%3A_Students.txt |
Figure 9.3.1 Technology reliability is important!
Image: © pixgood.com
9.3.1 Keep it simple
In most cases, the use of technology in teaching is a means, not an end. Therefore it is important that students and teachers do not have to spend a great deal of time on learning how to use educational technologies, or on making the technologies work. The exceptions of course are where technology is the area of study, such as computer science or engineering, or where learning the use of software tools is critical for some aspects of the curriculum, for instance computer-aided design in architecture, spreadsheets in business studies, and geographical information systems in geology. In most cases, though, the aim of the study is not to learn how to use a particular piece of educational technology, but the study of history, mathematics, or biology.
One advantage of face-to-face teaching is that it needs relatively little advance preparation time compared with for instance developing a fully online course. Media and technologies vary in their capacity for speed of implementation and flexibility in up-dating. For instance, blogs are much quicker and easier to develop and distribute than virtual reality. Teachers and instructors then are much more likely to use technology that is quick and easy to use, and students likewise will expect such features in technology they are to use for studying. However, what’s ‘easy’ for instructors and students to use will depend on their digital literacy.
9.3.2 Computer and information literacy
If a great deal of time has to be spent by the students and teachers in learning how to use for instance software for the development or delivery of course material, this distracts from the learning and teaching. Of course, there is a basic set of literacy skills that will be required, such as the ability to read and write, to use a keyboard, to use word processing software, to navigate the Internet and use Internet software, and increasingly to use mobile devices. These generic skills though could be considered pre-requisites. If students have not adequately developed these skills in school, then an institution might provide preparatory courses for students on these topics.
It will make life a lot easier for both teachers and students if an institution has strategies for supporting students’ use of digital media. For instance, at the University of British Columbia, the Digital Tattoo project prepares students for learning online in a number of ways:
• introducing students to a range of technologies that could be used for their learning, such as learning management systems, open educational resources, MOOCs and e-portfolios;
• explaining what’s involved in studying online or at a distance;
• setting out the opportunities and risks of social media;
• advice on how to protect their privacy;
• how to make the most of connecting, networking and online searching;
• how to prevent cyber-bullying;
• maintaining a professional online presence.
If your institution does not have something similar, then you could direct your students to the Digital Tattoo site, which is fully open.
It is not only students though who may need prior preparation. Technology can be too seductive. You can start using it without fully understanding its structure or how it works. Even a short period of training – an hour of less – on how to use common technologies such as a learning management system or lecture capture could save you a lot of time and more importantly, enable you to see the potential value of all features and not just those that you stumble across.
9.3.3 Orientation
A useful standard or criterion for the selection of course media or software is that ‘novice’ students (students who have never used the software before) should be studying within 20 minutes of logging on. This 20 minutes may be needed to work out some of the key functions of the software that may be unfamiliar, or to work out how the course Web site is organized and navigated. This is more of an orientation period though than learning new skills of computing. If there is a need to introduce new software that may take a little time to learn, for instance, a synchronous ‘chat’ facility, or video streaming, it should be introduced at the point where it is needed. It is important though to provide time within the course for the students to learn how to do this.
9.3.4 Interface design
The critical factor in making technology transparent is the design of the interface between the user and the machine. Thus an educational program or indeed any Web site should be well structured, intuitive for the user to use, and easy to navigate.
Interface design is a highly skilled profession, and is based on a combination of scientific research into how humans learn, an understanding of how operating software works, and good training in graphic design. This is one reason why it is often wise to use software or tools that have been well established in education, because these have been tested and been found to work well.
The traditional generic interface of computers – a keyboard, mouse, and graphic user interface of windows and pull-down menus and pop-up instructions – is still extremely crude, and not isomorphic with most people’s preferences for processing information. It places very heavy emphasis on literacy skills and a preference for visual learning. This can cause major difficulties for students with certain disabilities, such as dyslexia or poor eyesight. However, in recent years, interfaces have started to become more user friendly, with touch screen and voice activated interfaces.
Nevertheless a great deal of effort often has to go into the adaptation of existing computer or mobile interfaces to make them easy to use in an educational context. The Web is just as much a prisoner of the general computer interface as any other software environment, and the educational potential of any Web site is also restricted by its algorithmic or tree-like structure. For instance, it does not always suit the inherent structure of some subject areas, or the preferred way of learning of some students.
There are several consequences of these interface limitations for teachers and instructors:
• it is really important to choose teaching software or other technologies that are intuitively easy to use, both by the students in particular, but also for the teacher/instructor in creating materials and interacting with students;
• when creating materials for teaching, the teacher needs to be aware of the issues concerning navigation of the materials and screen lay-out and graphics. While it is possible to add stimulating features such as audio and animated graphics, this comes at the cost of bandwidth. Such features should be added only where they serve a useful educational function, as slow delivery of materials is extremely frustrating for learners, who will normally have slower Internet access that the teacher creating the materials. Furthermore, web-based layout on desktop or laptop computers does not automatically transfer to the same dimensions or configurations on mobile devices, and mobile devices have a wide range of standards, depending on the device. Given that the design of Web-based materials requires a high level of specialized interface design skill, it is preferable to seek specialist help, especially if you want to use software or media that are not standard institutionally supported tools. This is particularly important when thinking of using new mobile apps, for instance;
• third, we can expect in the next few years some significant changes in the general computer interface with the development of speech recognition technology, adaptive responses based on artificial intelligence, and the use of haptics (e.g. hand-movement) to control devices. Changes in basic computer interface design could have as profound an impact on the use of technology in teaching as the Internet has.
9.3.5 Reliability
The reliability and robustness of the technology is also critical. Most of us will have had the frustration of losing work when our word programming software crashes or working ‘in the cloud’ and being logged off in the middle of a piece of writing. The last thing you want as a teacher or instructor is lots of calls from students saying they cannot get online access, or that their computer keeps crashing. (If the software locks up one machine, it will probably lock up all the others!) Technical support can be a huge cost, not just in paying technical staff to deal with service calls, but also in lost time of students and teachers.
‘Innovation in teaching’ will certainly bring rewards these days as institutions jostle for position as innovative institutions. It is often easier to get funding for new uses of technology than funding to sustain older but successful technologies. Although podcasts combined with a learning management system can be a very low-cost but highly effective teaching medium if good design is used, they are not sexy. It will usually be easier to get support for much more costly and spectacular technologies such as xMOOCs or virtual reality.
On the other hand, there is much risk in being too early into a new technology. Software may not be fully tested and reliable, or the company supporting the new technology may go out of business. Students are not guinea pigs, and reliable and sustainable service is more important to them than the glitz and glamour of untried technology. It is best to wait for at least a year for new apps or software to be fully tested in general applications before adopting them for teaching. It is wise then not to rush in and buy the latest software update or new product – wait for the bugs to be ironed out. Also if you plan to use a new app or technology that is not generally supported by the institution, check first with IT services to ensure there are not security, privacy or institutional bandwidth issues. Thus it is better to be at the leading edge, just behind the first wave of innovation, rather than at the bleeding edge.
A feature of online learning is that peak use tends to fall outside normal office hours. Thus it is really important that your course materials sit on a reliable server with high-speed access and 24 hour, seven days a week reliability, with automatic back-up on a separate, independent server located in a different building. Ideally, the servers should be in a secure area (with for instance emergency electricity supply) with 24 hour technical support, which probably means locating your servers with a central IT service or ‘in the cloud’, which means it is all the more important to ensure that materials are safely and independently backed up.
However, the good news is that most commercial educational software products such as learning management systems and lecture capture, as well as servers, are very reliable. Open source software too is usually reliable but probably slightly more at risk of technical failure or security breaches. If you have good IT support, you should receive very few calls from students on technical matters. The main technical issue that faculty face these days appears to be software up-grades to learning management systems. This often means moving course materials from one version of the software to the new version. This can be costly and time-consuming, particularly if the new version is substantially different from the previous version. Overall, though, reliability should not be an issue.
In summary, ease of use requires professionally designed commercial or open source course software, specialized help in graphics, navigation and screen design for your course materials, and strong technical support for server and software management and maintenance. Certainly in North America, most institutions now provide IT and other services focused specifically on supporting technology-based teaching. However, without such professional support, a great deal of your time as a teacher will be spent on technical issues, and to be blunt, if you do not have easy and convenient access to such support, you would be wise not to get heavily committed to technology-based teaching until that support is available.
9.3.6 Questions for consideration
Ease of use is another critical factor in the successful use of technology for teaching. Some of the questions then that you need to consider are:
1. How intuitively easy to use, both by students and by yourself, is the technology you are considering?
2. How reliable is the technology?
3. How easy is it to maintain and upgrade the technology?
4. The company that is providing the critical hardware or software you are using: is it a stable company that is not likely to go out of business in the next year or two, or is it a new start-up? What strategies are in place to secure any digital teaching materials you create should the organisation providing the software or service cease to exist?
5. Do you have adequate technical and professional support, both in terms of the technology and with respect to the design of materials?
6. How fast developing is this subject area? How important is it to regularly change the teaching materials? Which technology will best support this?
7. To what extent can the changes be handed over to someone else to do, and/or how essential is it for you to do them yourself?
8. What rewards am I likely to get for using new technology in my teaching? Will use of a new technology be the only innovation, or can I also change my way of teaching with this technology to get better results?
9. What are the risks in using this technology?
Activity 9.3 Ease of use
1. what would be the main challenges of just putting a web cam in the lecture hall and recording your lecture on your computer for streaming later for students who can’t get to a lecture?
2. how would you rank these technologies for ease of use (a) by you as a teacher/instructor (b) by students?:
• a learning management system
• live video (e.g. a streamed, live lecture using video-conferencing software such as Zoom, GoToMeeting, Microsoft Team)
• books
• virtual reality
• a podcast (a digital audio recording)
Click on the podcast below for my feedback on this activity:
An audio element has been excluded from this version of the text. You can listen to it online here: https://pressbooks.bccampus.ca/teachinginadigitalagev2/?p=226 | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/09%3A_Choosing_and_using_media_in_education%3A_the_SECTIONS_model/09.3%3A_Ease_of_Use.txt |
Figure 9.4.1 Total cost of a fully online masters’ course over 7 years (from Bates and Sangrà, 2011). For an explanation of this graph, click on the podcast below
An audio element has been excluded from this version of the text. You can listen to it online here: https://pressbooks.bccampus.ca/teachinginadigitalagev2/?p=230
9.4.1 A revolution in media
Until as recently as ten years ago, cost was a major discriminator affecting the choice of technology (Hülsmann, 2000, 2003; Rumble, 2001; Bates, 2005). For instance, for educational purposes, audio (lectures, radio, audio-cassettes) was far cheaper than print, which in turn was far cheaper than most forms of computer-based learning, which in turn was far cheaper than video (television, cassettes or video-conferencing). All these media were usually seen as either added costs to regular teaching, or too expensive to use to replace face-to-face teaching, except for purely distance education on a fairly large scale.
However, there have been dramatic reductions in the cost of developing and distributing all kinds of media (except face-to-face teaching) in the last ten years, due to several factors:
• rapid developments in consumer technologies such as smartphones that enable text, audio and video to be both created and transmitted by end users at low cost;
• compression of digital media, enabling even high bandwidth video or television to be carried over wireless, landlines and the Internet at an economic cost (at least in economically advanced countries);
• improvements in media software, making it relatively easy for non-professional users to create and distribute all kinds of media;
• increasing amounts of media-based open educational resources, which are already developed learning materials that are free for teachers and students alike to use.
The good news then is that in general, and in principle, cost should no longer be an automatic discriminator in the choice of media. If you are happy to accept this statement at face value, than you can skip the rest of this chapter. Choose the mix of media that best meets your teaching needs, and don’t worry about which medium is likely to cost more. Indeed, a good case could be made that it would now be cheaper to replace face-to-face teaching with purely online learning, if cost was the only consideration.
In practice however costs can vary enormously both between and within media, depending once again on context and design. Since the main cost from a teacher’s perspective is their time, it is important to know what are the ‘drivers’ of cost, that is, what factors are associated with increased costs, depending on the context and the medium being used. These factors are less influenced by new technological developments, and can therefore be seen as ‘foundational’ principles when considering the costs of educational media.
Unfortunately there are many different factors that can influence the actual cost of using media in education, which makes a detailed discussion of costs very complex (for a more detailed treatment, see Bates and Sangrà, 2011). As a result, I will try to identify the main cost drivers, then provide a table that provides a simplified guide to how these factors influence the costs of different media, including face-to-face teaching. This guide again should be considered as a heuristic device, so see this section as Media Costs 101.
9.4.2 Cost categories
The main cost categories to be considered in using educational media and technologies, and especially blended or online learning, are as follows:
9.4.2.1 Development
These are the costs needed to pull together or create learning materials using particular media or technologies. There are several sub-categories of development costs:
• production costs: making a video or building a course section in a learning management system, or creating a virtual world. Included in these costs will be the time of specialist staff, such as web designers or media or computer specialists, as well as any costs in web design or video production;
• your time as an instructor: the work you have to do as part of developing or producing materials. This will include planning/course design as well as development. Your time is money, and probably the largest single cost in using educational technologies, but more importantly, if you are developing learning materials you are not doing other things, such as research or interacting with students, so there is a real cost, even if it is not expressed in dollar terms;
• copyright clearance if you are using third party materials such as photos or video clips. Again, this is more likely to be thought of as time in finding and clearing copyright more than money;
• probably the cost of an instructional designer in terms of their time.
Development costs are usually fixed or ‘once only’ and are independent of the number of students. Once media are developed, they are usually scalable, in that once produced, they can be used by any number of learners without increased development costs. Using open educational resources can greatly reduce media development costs.
9.4.2.2 Delivery
This includes the cost of the educational activities needed during offering the course and would include instructional time spent interacting with students, instructional time spent on marking assignments, and would include the time of other staff supporting delivery, such as teaching assistants, adjuncts for additional sections and instructional designers and technical support staff.
Because of the cost of human factors such as instructional time and technical support needed in media-based teaching, delivery costs tend to increase as student numbers increase, and also have to be repeated each time the course is on offer. In other words, they are recurrent. However, increasingly with Internet-based delivery, there is usually a zero direct technology cost in delivery.
9.4.2.3 Maintenance costs
Once materials for a course are created, they need to be maintained. Urls go dead, set readings may go out of print or expire, and more importantly new developments in the subject area may need to be accommodated. Thus once a course is offered, there are ongoing maintenance costs.
Instructional designers and/or media professionals can manage some of the maintenance, but nevertheless teachers or instructors will need to be involved with decisions about content replacement or updating. Maintenance is not usually a major time consumer for a single course, but if an instructor is involved in the design and production of several online courses, maintenance time can build to a significant amount.
Maintenance costs are usually independent of the number of students, but are dependent on the number of courses an instructor is responsible for, and are recurrent each year.
9.4.2.4 Overheads
These include infrastructure or overhead costs, such as the cost of licensing a learning management system, lecture capture technology and servers for video streaming. These are real costs but not ones that can be allocated to a single course but will be shared across a number of courses. Overheads are usually considered to be institutional costs and, although important, probably will not influence a teacher’s decision about which media to use, provided these services are already in place and the institution does not directly charge for such services.
However, if a new online program is to be offered on a full cost-recovery basis, then other institutional overheads will also need to be added. Some will be the same as for on-campus courses (for example, a contribution towards the President’s Office), but other overheads applied to on-campus students, such as building maintenance, will not apply to a fully online program (which is the main reason that the net cost of an online program is usually less than that of a campus-based program).
8.4.3 Cost drivers
The primary factors that drive cost are:
• the development/production of materials;
• the delivery of materials;
• number of students/scalability;
• the experience of an instructor working with the medium;
• whether the instructor develops materials alone (self-development) or works with professionals.
Production of technology-based materials such as a video program, or a Web site, is a fixed cost, in that it is not influenced by how many students take the course. However, production costs can vary depending on the design of the course. Engle (2014) showed that depending on the method of video production, the development costs for a MOOC could vary by a factor of six (the most expensive production method – full studio production – being six times that of an instructor self-recording on a laptop).
Nevertheless, once produced, the cost is independent of the number of students. Thus the more expensive the course to develop, the greater the need to increase student numbers to reduce the average cost per student. (Or put another way, the greater the number of students, the more reason to ensure that high quality production is used, whatever the medium). In the case of MOOCs (which tend to be almost twice as expensive to develop as an online course for credit using a learning management system – University of Ottawa, 2013) the number of learners is so great that the average cost per student is very small. Thus there are opportunities for economies of scale from the development of digital material, provided that student course enrolments can be increased (which may not always be the case). This can be described as the potential for the scalability of a medium.
Similarly, there are costs in teaching the course once the course is developed. These tend to be variable costs, in that they increase as class size increases. If student-teacher interaction, through online discussion forums and assignment marking, is to be kept to a manageable level, then the teacher-student ratio needs to be kept relatively low (for instance, between 1:25 to 1:40, depending on the subject area and the level of the course). The more students, the more time a teacher will need to spend on delivery, or additional contract instructors will need to be hired. Either way, increased student numbers generally will lead to increased costs. MOOCs are an exception. Their main value proposition is that they do not provide direct learner support, so have zero delivery costs. However, this is probably the reason why such a small proportion of participants successfully complete MOOCs.
There may be benefits then for a teacher or for an institution in spending more money up front for interactive learning materials if this leads to less demand for teacher-student interaction. For instance, a mathematics course might be able to use automated testing and feedback and simulations and diagrams, and pre-designed answers to frequently asked questions, with less or even no time spent on individual assignment marking or communication with the teacher. In this case it may be possible to manage teacher-student ratios as high as 1:200 or more, without significant loss of quality.
Also, experience in using or working with a particular medium or delivery method is also important. The first time an instructor uses a particular medium such as podcasting, it takes much longer than subsequent productions or offerings. Some media or technologies though need much more effort to learn to use than others. Thus a related cost driver is whether the instructor works alone (self-development) or works with media professionals. Self-developing materials will usually take longer for an instructor than working with professionals.
There are advantages in teachers and instructors working with media professionals when developing digital media. Media professionals will ensure the development of a quality product, and above all can save teachers or instructors considerable time, for instance through the choice of appropriate software, editing, and storage and streaming of digital materials. Instructional designers can help in suggesting appropriate applications of different media for different learning outcomes. Thus as with all educational design, a team approach is likely to be more effective, and working with other professionals will help control the time teachers and instructors spend on media development.
Lastly, design decisions are critical. Costs are driven by design decisions within a medium. For instance cost drivers are different between lectures and seminars (or lab classes) in face-to-face teaching. Similarly, video can be used just to record talking heads, as in lecture capture, or can be used to exploit the affordances of the medium (see Chapter 8), such as demonstrating processes or location shooting. Computing has a wide and increasing range of possible designs, including online collaborative learning (OCL), computer-based learning, animations, simulations or virtual worlds.
Figure 9.4.3 attempts to capture the complexity of cost factors, focusing mainly on the perspective of a teacher or instructor making decisions. Again, this should be seen as a heuristic device, a way of thinking about the issue. Other factors could be added (such as social media, or maintenance of materials). I have given my own personal ratings for each cell, based on my experience. I have taken conventional teaching as a medium or ‘average’ cost, then ranked cells as to whether there is a higher or lower cost factor for the particular medium. Other readers may well rate the cells differently.
Figure 9.4.3 Cost drivers for educational media
Although the time it takes to develop and deliver learning using different technologies is likely to influence an instructor’s decision about what technology to use, it is not a simple equation. For instance, developing a good quality online course using a mix of video and text materials may take much more of the instructor’s time to prepare than if the course was offered through classroom teaching. However, the online course may take less time in delivery over several years, because students may be spending more time on task online, and less time in direct interaction with the instructor. Once again, we see that design is a critical factor in how costs are assessed.
In short, from an instructor perspective, time is the critical cost factor. Technologies that take a lot of time to use are less likely to be used than those that are easy to use and thus save time. But once again design decisions can greatly affect how much time teachers or instructors need to spend on any medium, and the ability of teachers and students to create their own educational media is becoming an increasingly important factor.
9.4.4 Issues for consideration
9.4.4.1 Lecture capture vs LMS: cost factors
In recent years, university faculty have generally gravitated more to lecture capture and video streaming for online course delivery, particularly in institutions where online or distance learning is relatively new, because it is ‘simpler’ to do than redesign and create mainly text based materials in learning management systems. Lecture capture also more closely resembles the traditional classroom method, so less change is required of the instructor.
Pedagogically though (depending on the subject area) lecture capture may be less effective than an online course using collaborative learning and online discussion forums. Also, from an institutional perspective lecture capture has a much higher technology cost than a learning management system. And, of course, lecture capture is often used in conjunction with an LMS. What different technologies tend to do though is change the spread of an instructors time between development and delivery. Media such as an LMS can have higher initial development costs but much lower annual delivery and maintenance costs than face-to-face teaching, for instance.
9.4.4.2 The student factor
Also, students themselves can now use their own devices to create multimedia materials for project work or for assessment purposes in the form of e-portfolios. Media allow instructors, if they wish, to move a lot of the hard work in teaching and learning from themselves to the students. Media allow students to spend more time on task, and low cost, consumer media such as mobile phones or tablets enable students themselves to create media artefacts, enabling them to demonstrate their learning in concrete ways. This does not mean that instructor ‘presence’ is no longer needed when students are studying online, but it does enable a shift in where and how a teacher or instructor can spend their time in supporting learning.
9.4.5 Conclusion
Cost is a critical factor influencing media choice. For instructors, the main cost will be their time. However it is important to look at time over the length of a course over several years, not just in the initial production or preparation of materials. Carefully produced media may take more time in production, but can save a great deal of time in delivery, especially if student activities and automated feedback can be built into the design. This is why some institutions have a special fund for innovative teaching or technology-based teaching and learning, to free up instructor time for design and development.
Media also differ considerably in the balance of costs between development, delivery, maintenance and overheads. Face-to-face teaching has minimal development costs, but heavy delivery costs in terms of instructor time; an LMS-based online course is has more of an equal balance between development and delivery costs. Serious games usually have high development costs but very low delivery costs.
Whatever the balance, cost is still a critical factor in media choice.
References
Bates, A. (2005) Technology, e-Learning and Distance Education London/New York: Routledge
Bates, A. and Sangrà, A. (2011) Managing Technology in Higher Education San Francisco: Jossey-Bass
Engle, W. (2104) UBC MOOC Pilot: Design and Delivery Vancouver BC: University of British Columbia
Hülsmann, T. (2000) The Costs of Open Learning: A Handbook Oldenburg: Bibliotheks- und Informationssytem der Universität Oldenburg
Hülsmann, T. (2003) Costs without camouflage: a cost analysis of Oldenburg University’s two graduate certificate programs offered as part of the online Master of Distance Education (MDE): a case study, in Bernath, U. and Rubin, E., (eds.) Reflections on Teaching in an Online Program: A Case Study Oldenburg, Germany: Bibliothecks-und Informationssystem der Carl von Ossietsky Universität Oldenburg
Rumble, G. (2001) The Cost and Costing of Networked Learning Journal of Asynchronous Learning Networks, Volume 5, Issue 2
University of Ottawa (2013)Report of the e-Learning Working Group Ottawa ON: The University of Ottawa
Activity 9.4 How will cost affect your decision about what media to use?
1. Are concerns about the possible cost/demands on your time influencing your decisions on what media to use? If so in what ways? Has this section on costs changed your mind?
2. How much time do you spend preparing lectures? Could that time be better spent preparing learning materials, then using the time saved from delivering lectures on interaction with students (online and/or face-to-face)?
3. What kind of help can you get in your institution from instructional designers and media professionals for media design and development? What media decisions will the answer to this question suggest to you? For instance, if you are in a k-12 school with little or no chance for professional support, what kind of media and design decisions are you likely to make?
4. If you were filling in the cells for Figure 9.4.3, what differences would there be with my entries? Why?
5. In Figure 9.4.3, add the following media: e-portfolios (in computing) and add another section under computing: social media. Add blogs, wikis and cMOOCs. How would you fill in the cells for each of these for development, delivery, etc.? Are there other media you would also add?
6. Do you agree with the statement: It would now be cheaper to replace face-to-face teaching with purely online learning, if cost was the only consideration? What are the implications for your teaching if this is really true? What considerations would still justify face-to-face teaching?
For my feedback on some of these questions, click on the podcast below:
An audio element has been excluded from this version of the text. You can listen to it online here: https://pressbooks.bccampus.ca/teachinginadigitalagev2/?p=230 | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/09%3A_Choosing_and_using_media_in_education%3A_the_SECTIONS_model/09.4%3A_Cost.txt |
Figure 9.5.1 People do not necessarily learn better … when the speaker’s image is added to the screen (Mayer, 2009).
9.5.1 The importance of design in multimedia teaching
Chapter 8 discussed the various pedagogical differences between media. Identifying appropriate uses of media is both an increasingly important requirement of teachers and instructors in a digital age, and a very complex challenge. This is one reason for working closely with instructional designers and media professionals whenever possible. Teachers working with instructional designers will need to decide which media they intend to use on pedagogical as well as operational grounds, which was the purpose of Chapter 8.
However, once the choice of media has been made, by focusing on design issues we can provide further guidelines for making appropriate use of media. In particular, having gone through the process suggested in Chapter 8 of identifying possible teaching roles or functions for different media, we can then draw on the work of Mayer (2012) and Koumi (2006, 2015) to ensure that whatever choice or mix of media we have decided on, the design leads to effective teaching.
Mayer’s research focused heavily on cognitive overload in rich, multimedia teaching. From all his research over many years, Mayer identified 12 principles of multimedia design, based on how learners cognitively process multimedia:
9.5.1.1 Coherence
People learn better when extraneous words, pictures and sounds are excluded rather than included. Basically, keep it simple in media terms.
9.5.1.2 Signalling
People learn better when cues that highlight the organization of the essential material are added. This replicates earlier findings by Bates and Gallagher (1977). Students need to know what to look for in multimedia materials.
9.5.1.3 [Avoid] Redundancy
People learn better from graphics + narration, than from graphics, narration and on-screen text.
9.5.1.4 Spatial contiguity
People learn better when corresponding words and pictures are presented near rather than far from each other on the page or screen
9.5.1.5 Temporal contiguity
People learn better when corresponding words and pictures are presented simultaneously rather than successively.
9.5.1.6 Segmenting
People learn better when a multimedia lesson is presented in user-paced segments rather than as a continuous lesson. Thus several ‘YouTube’ length videos are more likely to work better than a 50 minute video.
9.5.1.7 Pre-training
People learn better from a multimedia lesson when they know the names and the characteristics of the main concepts. This suggests a design feature for flipped classrooms, for instance. It may be better to use a lecture or readings that provide a summary of key concepts and principles before showing more detailed examples or applications of such principles in a video.
9.5.1.8 Modality
People learn better from graphics and narration than from animation and on-screen text. This reflects the importance of learners being able to combine both hearing and viewing at the same time to reinforce each other in specific ways.
9.5.1.9 Multimedia
People learn better from words and pictures than from words alone. This also reinforces what I wrote in 1995: Make all four media available to teachers and learners (Bates, 1995, p.13).
9.5.1.10 Personalization
People learn better from multimedia lessons when words are in conversational style rather than formal style. I would go even further than Mayer here. Multimedia can enable learners (particularly distance learners) to relate to the instructor, as suggested by Durbridge’s research (1983, 1984) on audio combined with text. Providing a ‘human voice and face’ to the teaching helps motivate learners, and makes multimedia teaching feel that it is directed solely at the individual learner, if a conversational style is adopted.
9.5.1.11 Voice
People learn better when the narration in multimedia lessons is spoken in a friendly human voice rather than a machine voice.
9.5.1.12 [No] image
People do not necessarily learn better from a multimedia lesson when the speaker’s image is added to the screen.
In re-reading Mayer’s work, I am struck by the similarities in findings, using different research methods, different multimedia technologies, and different contexts, to the research from the Audio-Visual Media Research Group at the British Open University in the 1970s and 1980s (Bates, 1984).
More recently, the University of British Columbia has done an excellent job of suggesting how Mayer’s design principles could be operationalised. Staff at the University of British Columbia have combined Mayer’s findings with Robert Talbert’s experience from developing a series of successful screencasts on mathematics, into a set of practical design guidelines for multimedia production.
Talbert’s key design principles are:
• keep it Simple: focus on one idea at a time.
• keep it Short: keep videos to a length 5-6 minutes max. to maximize attention.
• keep it Real: model the decision making and problem solving processes of expert learners.
• keep it Good: be intentional about planning the video; strive to produce the best video and audio quality possible.
Thus design decisions are critical in influencing the effectiveness of a particular technology. Well-designed lectures will teach better than a poorly designed online course, and vice versa.
9.5.2 Teaching as a weak discriminator in media selection
Chapter 8 was exclusively focused on the best uses of each medium. Section 9.5.1 above then goes on to look at effective design of multimedia. Most teachers and instructors would put the effectiveness of a medium for teaching and learning as the first criterion for media selection. If the technology is not educationally effective, why would you use it? Why do we need the other parts of the SECTIONS model?
However, if a student cannot access or use a technology, there will be no learning from that technology, no matter how useful the educational affordances or how well the medium is designed. Furthermore, motivated teachers will overcome educational weaknesses or shortcomings in a particular technology, or conversely teachers inexperienced in using media will often under-exploit the potential of a medium (such as using video for talking heads).
Similarly, students will respond differently to different technologies due to preferred learning styles or differences in motivation. Students who work hard can overcome poor use of learning technologies. It is not surprising then that with so many variables involved, teaching and learning is a relatively weak discriminator for selecting and using technologies. Access (and ease of use) are stronger discriminators than teaching effectiveness in selecting media. This explains why teaching that does not really exploit the educational affordances of a medium can often still get good results. Nevertheless, ideally one should try to make best use of the pedagogical features of a medium because when it is then combined with the other SECTIONS criteria, the teaching is likely to be more effective.
9.5.3 Questions for consideration
Therefore, it is not enough to focus just on the design of multimedia materials, as important as design is, even considering just the pedagogical context. The choice and use of media need to be related to other factors (what Mayer calls ‘boundary conditions’), such as individual differences between learners, the complexity of the content, and the desired learning outcomes. Thus when considering media from a strictly teaching perspective, the following questions need to be considered:
1. Who are my students?
2. What content needs to be covered?
3. What are the desired learning outcomes from the teaching in terms of skills development?
4. What instructional strategies or approaches to learning do I plan using?
5. What are the unique pedagogical characteristics of different media? How might different media help with the presentation of content and development of student skills in this course?
6. What is the best way to present the content to be covered in this course? How can media help with the presentation of content? Which media for what content?
7. What skills am I trying to develop on this course? How can media help students with the development of the requisite skills for this course? Which media for which skills?
8. What principles do I need to use when designing multimedia materials for their most effective use?
Working through these questions is likely to be an iterative rather than a sequential process. Depending on the way you prefer to think about and make decisions, it may help to write down the answers to each of the questions, but going through the process of thinking about these questions is probably more important, leaving you with the freedom to make choices on a more intuitive basis, having first taken all these – and other – factors into consideration.
References
Bates, A. (1984) Broadcasting in Education: An Evaluation London: Constables
Bates, A. (1995) Teaching, Open Learning and Distance Education London/New York: Routledge
Bates, A. and Gallagher, M. (1977) Improving the Effectiveness of Open University Television Case-Studies and Documentaries Milton Keynes: The Open University, I.E.T. Papers on Broadcasting, No. 77 (out of print – copies available from [email protected])
Durbridge, N. (1983) Design implications of audio and video cassettes Milton Keynes: Open University Institute of Educational Technology (out of print)
Durbridge, N. (1984) Audio-cassettes, in Bates, A. (ed.) The Role of Technology in Distance Education London/New York: Croom Hill/St Martin’s Press
Koumi, J. (2006). Designing video and multimedia for open and flexible learning London: Routledge
Koumi, J. (2015) Learning outcomes afforded by self-assessed, segmented video-print combinations Cogent Education, Vol. 2, No.1
Mayer, R. E. (2009). Multimedia learning (2nd ed). New York: Cambridge University Press
UBC Wikis (2014) Documentation: Design Principles for Multimedia Vancouver BC: University of British Columbia
Activity 9.5 Multimedia design principles
1. How well do you think Mayer’s design principles (9.5.1 to 9.5.12) would apply to classroom teaching?
For feedback on this question, click on the podcast below.
An audio element has been excluded from this version of the text. You can listen to it online here: https://pressbooks.bccampus.ca/teachinginadigitalagev2/?p=233 | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/09%3A_Choosing_and_using_media_in_education%3A_the_SECTIONS_model/09.5%3A_Teaching_and_media_selection.txt |
Figure 9.6.1 Computers enable learners to interact with learning materials (also ‘inherent’ interaction)
The fifth element of the SECTIONS model for selecting media is interaction. How do different media enable interaction? The extent to which a medium enables interaction – and the kind of interaction – is critically important, as there is now an overwhelming amount of research evidence to suggest that students learn best when they are ‘active’ in their learning. But what does this mean? And what role can or do new technologies play in supporting active learning?
9.6.1. Types of learner interaction
There are three different ways learners can interact when studying (Moore, 1989), and each of these ways requires a somewhat different mix of media and technology.
9.6.1.1 Interaction with learning materials
This is the interaction generated when students work on a particular medium, such as a printed textbook, a learning management system, or a short video clip, without direct intervention from an instructor or other students. This interaction can be ‘reflective’, without any overt actions, or it can be ‘observable’, in the form of an assessed response, such as a multiple choice test, or as notes to assist memory and comprehension.
Computer technology can greatly facilitate learners’ interaction with learning resources. Self-administered online tests can provide feedback to students on their comprehension or coverage of a subject area. Such tests can also provide feedback to teachers on topic areas where students are having difficulty, and can also be used for grading of students on their comprehension. Using standard test software built into learning management systems, students can be automatically assessed and graded on their comprehension of course materials. More advanced activities might include composing music using software that converts musical notation to audio, entering data to test concepts through online simulations, or participating in games or decision-making scenarios controlled by the computer. Thus computer-managed learner interaction is particularly good for developing comprehension and understanding of concepts and procedures, but it has limitations in developing the higher order learning skills of analysis, synthesis and critical thinking, without additional human intervention of some kind.
There are other ways besides computer-managed learning to facilitate interaction between learners and learning material. Textbooks may include activities set by the author (as in this textbook), or instructors can set student activities around set readings. Other student activities might include reading text or watching videos embedded in a learning management system, conducting a structured approach to finding and analyzing web-based materials, or downloading and editing information from the web to create e-portfolios of work. These activities may or may not be assessed, although evidence suggests that students, and in particular students studying online, tend to focus more an assessed activities.
In other words, with good design and adequate resources, technology-based instruction can provide high levels of student interaction with the learning materials. There are strong economic advantages in exploiting the possibilities of learners’ interaction with learning materials, because intense student-interaction with learning resources increases the time students spend on learning (‘time-on-task’), which tends to lead to increased learning (see Means et al., 2010). Perhaps more importantly, such activity, when well designed, can reduce the time the teacher needs to spend on interacting with each student.
9.6.1.2 Interaction between students and teacher
Figure 9.6.2 Student-teacher interaction Image: © Joseph Mehling, DartmouthLife, 2007
Student-teacher interaction is often needed though in order to develop many of the higher order learning outcomes, such as analysis, synthesis, and critical thinking. This is particularly important for developing academic learning, where students are challenged to question ideas, and to acquire deep understanding. This often requires dialogue and conversation, either one-on-one between instructor and students, or between an instructor and a group of students. The role of the teacher in for instance either face-to-face seminars or online collaborative learning is therefore critical.
Some technologies, such as online discussion forums, enable or encourage such dialogue or discourse between students and instructors at a distance. The main limitation of student-teacher interaction is that it can be time-demanding for the teacher, and therefore does not scale easily.
9.6.1.3 Student – student interaction
Figure 9.6.3 A student directed seminar at UBC Image: © University of British Columbia, 2014
High quality student-student interaction can be provided equally well both in face-to-face and online learning contexts. Asynchronous online discussion forums built into learning management systems can enable this kind of interaction. Connectivist MOOCs and communities of practice also enable student-student interaction.
Again though quality depends on good design. Merely putting students together in a group, whether online or face-to-face, is not likely to lead to either high levels of participation or high quality learning without careful thought being given to the educational goals of discussion within a course, the topics for discussion and their relationship to assessment and learning outcomes, and without strong preparation of the students by the instructor for self-directed discussions (see Chapter 4, Section 4, for more on this.)
In a technologically rich learning environment, then, a key decision for a teacher or course designer is choosing the best mix of these three different kinds of interaction, taking into consideration the epistemological approach, the amount of time available for both students and instructor, and the desired learning outcomes. Technology can enable all three kinds of interaction.
9.6.2 The interactive characteristics of media and technologies
Different technologies can enhance or inhibit each of the three types of interactivity outlined above. This again means looking at the dimension of interactivity as it applies to different media and technology. This dimension has three components or points on the dimension in terms of the extent an active response from a user is required when a medium or technology is used for teaching.
9.6.2.1 Inherent interactivity
Some media are inherently ‘active’ in that they ‘push’ learners to respond. An example is adaptive learning, where students cannot progress to the next stage of learning without interacting through a test that ascertains whether they have learned sufficiently to progress to the next stage, or what ‘corrective’ learning they still need to do. Behaviourist computer-based learning is inherently interactive, as it forces learners to respond. Technologies that control how a learner responds are often associated with more behaviourist approaches to teaching and learning.
9.6.2.2 Designed interactivity
Although some media or technologies are not inherently interactive, they can be explicitly designed to encourage interaction with learners. For instance, although a web page is not inherently interactive, it can be designed to be interactive, by adding a comment box or by requiring users to enter information or make choices. In particular, teachers or instructors can add or suggest activities within a particular medium. A podcast can be designed so that students stop the podcast every few minutes to do an activity based on the content of the podcast. This approach can be applied just as much to textbooks, where activities can be included, as to web pages.
In many cases, though, a medium will require the intervention of a teacher or instructor both to set activities around the learning materials and to provide appropriate feedback, thus adding to rather than reducing the workload of instructors. Thus where instructors have to intervene either to design activities or to provide feedback, the cost or time demands on the instructor are likely to be greater than if the other two kinds of interaction are used.
9.6.2.3 User-generated interaction
Some media may not have explicit interaction built in, but end users may still voluntarily interact with the medium, either cognitively and/or through some physical response. For instance someone in an art gallery may cognitively or emotionally respond to a particular painting (while others may just glance at it or pass it by). Students may choose to make sketches or drawings from the painting. Learners may respond in similar ways to reading a novel or poem.
The creators of the work may in fact deliberately design the work to encourage reflection or analysis, but not in explicit ways, leaving the interpretation of a work to the viewer or reader. (This of course is a constructivist approach to learning.) Media that encourage learners independently to be active without the necessary intervention of a teacher or instructor also have cost advantages, although the quality of the interaction will be more difficult to monitor or assess.
9.6.2.4 Who’s in control?
Thus one dimension of interactivity is control: to what extent is interaction controlled or enabled by the technology, by the creators/instructors, or by the users/learners? It can be seen that this is a complex dimension, once again influenced by epistemological positions, and also by design decisions on the teacher’s part. These categories of interactivity are in no way ‘fixed’, with different levels or types of interaction possible within the same medium or technology. In the end, interaction needs to be linked to desired learning outcomes. What kind of interaction will best lead to a particular type of learning outcome, and what technology or medium best provides this kind of interaction?
9.6.3 Interaction and feedback
Feedback is an important aspect of interaction, and timely and appropriate feedback on learner activities is often essential for effective learning. In particular, to what extent is feedback possible within a particular medium? Although for instance a learner may respond actively to a poem in a book, feedback on that interaction is usually not available just from the reading. Some other medium will need to be used to provide that feedback, such as a face-to-face poetry class or an online discussion forum.
On the other hand, with computer-based learning, once a student has responded to a multiple-choice question, the computer can mark the question and give almost instant feedback. However, with some technologies such as print, providing appropriate or immediate feedback to learners on their activities may be difficult or impossible. Although ‘model’ or ‘correct’ answers might be provided in a text on another page, quality feedback on activities must be provided by a teacher or instructor when using a printed medium.
Thus media and technologies again differ in their capacity to provide various kinds of feedback. From a teaching perspective, it is important to be clear about what kind of feedback is likely to be most effective, and then the most effective way to provide that feedback. In particular, under what circumstances is it appropriate to automate feedback, and when should feedback be provided by a teacher/instructor, or perhaps a teaching assistant, or even by other students?
9.6.4 Analysing the interactive qualities of different media
In Figure 9.6.4 I have analysed the interactive qualities of different educational media along two different dimensions: different types of student interaction; and characteristics of the medium, in terms of whether interaction is built into the medium, or needs to be added through deliberate design, or whether it is left to the learner to decide how to interact.
Figure 9.6.4 Media and student interaction
I have allocated a number of different media here according to the type of learner activity they help generate. The actual location though of some of these media will be dependent on design decisions made by the instructor. For instance, a podcast could be accompanied by an activity (designed), or just be a straight broadcast, with the student left to interpret its meaning and purpose in the course (learner-generated). In some cases, an activity may be triggered by one medium (such as a podcast) but the actual activity and the feedback may take place in another medium (such as through an online assessment).
9.6.5 Summary
Thus it can be seen that media and technology are somewhat slippery when it comes to categorising them in terms of interaction, because instructors and learners often have a choice in how the medium will actually be used, and that will affect how learner interaction and feedback takes place within a single medium. Thus once again the quality of the design of the interactive experiences is as important as the medium of choice for enabling the activity, although an inappropriate choice of technology can reduce the level of activity and/or the quality of the interactions. In reality teachers and learners are likely to use a combination of media and technologies to ensure high quality interactivity. However, using a number of different media is likely to increase cost and workload for both instructors and learners.
Once again, there is no evaluative judgement on my part in terms of which media or characteristics provide the ‘best’ interactivity. The choice of medium should depend on the kind of activities that are judged important by a teacher or instructor within the overall context of the teaching. The purpose of this analysis is to sensitize you to the differences between educational media in generating or facilitating different types of interactivity, so that you can make informed decisions. In this case, though, there are no clear media or technology ‘winners’ in terms of interactivity. Design decisions are likely to be more important than technology choice. Nevertheless, technology can enable students separated from their instructors still to get quality activities and feedback, and when appropriately used, technology used to support activities can result in more time on task for students.
9.6.6 Questions for consideration
1. In terms of the skills I am trying to develop, what kinds of interaction will be most useful? What media or technology could I use to facilitate that kind of interaction?
2. In terms of the effective use of my time, what kinds of interaction will produce a good balance between on the one hand student comprehension and student skills development, and on the other the amount of time I will be interacting personally or online with students?
Activity 9.6 Using media to promote student activity
1. Go to YouTube and type in your subject area into the ‘search’ box.
2. Choose a YouTube video from the list that comes up that you might recommend to your students to watch.
3. What kind of interaction would the YouTube video require from your students? Does it force them to respond in some way (inherent)?
4. In what way are they likely to respond to the YouTube on their own, e.g. make notes, do an activity, think about the topic (learner-generated)?
5. What activity could you suggest that they do, after they have watched the YouTube video (designed)? What type of knowledge or skill would that activity help develop? What medium or technology would students use to do the activity?
6. How would students get feedback on the activity that you set? What medium or technology would they and/or you use for getting and giving feedback on their activity?
7. How much work for you would that activity cause? Would the work be both manageable and worthwhile? Could the activity be scaled for larger numbers of students?
8. How could the YouTube video have been designed to generate more or better activity from viewers or students?
There is no feedback from me for this activity, which requires user-generated activity (that is, you have to do the work!)
Reference
Means, B. et al. (2009) Evaluation of Evidence-Based Practices in Online Learning: A Meta-Analysis and Review of Online Learning Studies Washington, DC: US Department of Education
Moore, M.G. (1989) Three types of interactionAmerican Journal of Distance Education, Vol.3, No.2 | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/09%3A_Choosing_and_using_media_in_education%3A_the_SECTIONS_model/09.6%3A_Interaction.txt |
Figure 9.7.1 A video production studio at University of Illinois Urbana-Champaign Image: UIUC. Just as important as the technical facilities are the media professionals who can help with the design of good quality educational videos.
9.7.1 Institutional readiness for teaching with technology
One of the critical issues that will influence the selection of media by teachers and instructors is:
• the way the institution structures teaching activities;
• the instructional and technology services already in place;
• the support for media and technology use that their institution provides.
If an institution is organised around a set number of classroom periods every day, and the use of physical classrooms, the teachers are likely to focus mainly on classroom delivery. As Mackenzie was quoted in Chapter 9 Section 1:
Teachers have always made the best of whatever they’ve got at hand, but it’s what we have to work with. Teachers make due.’
The reverse is equally true. If the school or university does not support a particular technology, teachers and instructors quite understandably won’t use it. Even if the technology is in place, such as a learning management system or a video production facility, if instructors are not trained or oriented to its use and potential, then it will either be under used or not used at all. Furthermore, if ‘core’ technologies’ such as learning management systems or lecture capture facilities are not properly managed or if the services are understaffed, teachers and instructors lose patience and confidence in the technology.
Because of the inertia in institutions, there is often a bias towards those technologies that can be introduced with the minimum of organisational change, although these may not be the technologies that would have maximum impact on learning. These organisational challenges are extremely difficult, and are often major reasons for the slow implementation of new technologies for teaching in education (see Marshall, 2009) for a method for assessing the readiness of institutions for online learning).
Most institutions that have successfully introduced media and technology for teaching on a large scale have recognized the need for adequate professional support for faculty, by providing instructional designers, media designers and IT support staff to support teaching and learning. Some institutions also provide funding for innovative teaching projects.
9.7.2 Work with professionals
Figure 8.7.2 Chris Crowley is an Instructional Designer/Project Manager for UBC’s Centre for Teaching, Learning and Technology. He is involved in the design, development and delivery of online courses and learning resources in a number of subject areas including Soil Science.
Even those experienced in using media for teaching and learning would be wise to work with instructional designers and professional media producers when creating any of the media discussed in this chapter (with the possible exception of social media). It is important for the choice of technology to be driven by educational goals, rather than starting with a particular medium or technology in mind.
There are several reasons for working with professionals:
• they understand the technology and as a result will enable you to develop a better product more quickly than working alone;
• two heads are better than one: working collaboratively will result in new and better ideas about how you could be using the medium;
• instructional designers and professional media producers will usually be familiar with project management and budgeting for media production, enabling resources to be developed in time and on budget. This is important as it is easy for teachers or instructors to get sucked into spending far more time than necessary on producing media.
The key point here is that although it is now possible for teachers and instructors to produce reasonably good quality audio and video on their own, they will always benefit from the input of professionals in media production.
9.7.3 Questions for consideration
1. How much and what kind of help can I get from the institution in choosing and using media for teaching? Is help easily accessible? How good is the help? Do the support people have the media professionalism I will need? Are they up to date in the use of new technologies for teaching?
2. Is there possible funding available to ‘buy me out’ for a semester and/or to fund a teaching assistant so I can concentrate on designing a new course or revising an existing course? Is there funding for media production?
3. To what extent will I have to follow ‘standard’ technologies, practices and procedures, such as using a learning management system, or lecture capture system, or will I be encouraged and supported to try something new?
4. Are there already suitable media resources freely available that I can use in my teaching, rather than creating everything from scratch? Can I get help from the library for instance in identifying these resources and dealing with any copyright issues (see Chapter 11, Section 2)?
If the answers are negative for each of these questions, you would be wise to set very modest goals initially for using media and technology.
Nevertheless the good news is that it is increasingly easy to create and manage your own media such as web sites, blogs, wikis, podcasts and simple video production using a desktop computer or even a mobile phone. Furthermore students themselves are often capable and interested in participating or helping with creating learning resources, if given the chance. Getting students involved in media production is a very good way for them to get a deeper understanding of a subject. Above all, there is an increasing amount of really good educational media coming available for free use for educational purposes, as we shall see in Chapter 11, so it is not necessary always to create media from scratch.
References
Bates, A. and Sangrà, A. (2011) Managing Technology in Higher Education San Francisco: Jossey-Bass
Marshall, S. (2009) E-Learning Maturity Model Version Two: New Zealand Tertiary Institution E-Learning Capability: Informing and Guiding E-Learning Architectural Change and Development Wellington NZ: Victoria University of Wellington
Activity 9.7
There is no activity provided for this section. The issues covered here are discussed in more depth in Bates and Sangrà (2011). | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/09%3A_Choosing_and_using_media_in_education%3A_the_SECTIONS_model/09.7%3A_Organisational_issues.txt |
Figure 9.8.1 UBC’s Math Exam Wiki (click on image to go to web page)
9.8.1 Networking and novelty in course design
In earlier versions of the SECTIONS model, ‘N’ stood for novelty. This was to recognise the importance of teachers and instructors trying something new to improve on their practice, in this case to try a new technology and see how well it worked for them. Also the ‘hype’ surrounding new developments in technology often provides a supportive environment for innovative teaching. This is still an important issue; without experiment and trying new ways of teaching and new technologies for teaching, there will be no improvement in practice.
However, more recent developments in social media raise another, increasingly important, question that needs to be asked when selecting media:
how important is it to enable learners to network beyond a course, with others such as subject specialists, professionals in the field, and relevant people in the community? Can the course, or student learning, benefit from such external connections?
If the answer to this is an affirmative, then this will affect what media to use, and in particular will suggest the use of social media such as blogs, wikis, Facebook, LinkedIn, or Google Hangout.
Five different ways social media are influencing the application of networking in course design are described below.
9.8.2 Supplementing ‘standard’ learning technologies
Some instructors are combining social media for external networking with ‘standard’ institutional technologies such as a learning management system or video delivery. The LMS, which is password protected and available only to the instructor and other enrolled students, allows for ‘safe’ communication within the course. The use of social media allows for connections with the external world (contributions can still be screened by the course blog or wiki administrator by monitoring and approving contributions.)
For instance, a course on Middle Eastern politics could have an internal discussion forum focused on relating current events directly to the themes and issues that are the focus of the course, but students may manage their own, public wiki that encourages contributions from Middle East scholars and students, and indeed anyone from the general public. Comments may end up being moved into and out of the more closed class discussion forum as a result.
9.8.3 Exclusive use of social media for credit courses
Other instructors are moving altogether away from ‘standard’ institutional technology such as learning management systems and lecture capture into the use of social media for managing the whole course. For instance, UBC’s course ETEC 522 uses WordPress, YouTube videos and podcasts for instructor and student contributions to the course. Indeed the choice of social media on this course changes every year, depending on the focus of the course, and new developments in social media. Jon Beasley-Murray at the University of British Columbia built a whole course around students creating a high level (featured-article) Wikipedia entry on Latin American literature (Latin American literature WikiProject – see Beasley-Murray, 2008).
9.8.4 Student generated learning resources
This is a particularly interesting development where students themselves use social media to create resources to help other students. For instance, graduate math students at UBC have created the Math Exam/Education Resources wiki, which provides ‘past exams with fully worked-out and reviewed solutions, video lectures & pencasts by topic‘. Such sites are open to anyone needing help in their studying, not just UBC students. The project involves voluntary collaboration between graduate students for the benefit of undergraduate students.
9.8.5 Self-managed learning groups
cMOOCs are an obvious example of self-managed learning groups using social media such as webinars, blogs and wikis.
9.8.6 Instructor-led open educational resources
YouTube in particular is becoming increasingly popular for instructors to use their knowledge to create resources available to anyone. The best example is still the Khan Academy, but there are many other examples, such as MIT’s OpenCourseWare. xMOOCs are another example. This will be discussed more in Chapter 11.
Once again, the decision to ‘open up’ teaching is as much a philosophical or value decision as a technology decision, but the technology is now there to encourage and enable this philosophy.
9.8.7 Questions for consideration
1. How important is it to enable learners to network beyond a course, with others such as subject specialists, professionals in the field, and relevant people in the community? Can the course, or student learning, benefit from such external connections?
2. If this is important, what’s the best way to do this? Use social media exclusively? Integrate it with other standard course technology? Delegate responsibility for its design and/or administration to students or learners?
References
Beasley-Murray, J. (2008) Was introducing Wikipedia to the classroom an act of madness leading only to mayhem if not murder? Wikipedia, March 18
Activity 9.8 Networking (and novelty)
1. How could you use social media in one of your courses to enable students in the course to connect to the outside world? How would it improve their learning? What would be the risks as well as the benefits?
For my feedback on this, click on the podcast below:
An audio element has been excluded from this version of the text. You can listen to it online here: https://pressbooks.bccampus.ca/teachinginadigitalagev2/?p=246 | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/09%3A_Choosing_and_using_media_in_education%3A_the_SECTIONS_model/09.8%3A_Networking_%28and_novelty%29.txt |
Figure 9.9.1 Strength of data protection laws. Click on image for more information.
Image: © 2019 DLA Piper
‘S’ too is a change from the earlier ACTIONS model, where ‘S’ stood for speed, in terms of how quickly a technology enabled a course to be developed. However, the issues previously raised under speed have also been included in SECTIONS ‘Ease of Use’ (Chapter 9, Section 2). This allows ‘Speed’ to be replaced with ‘Security and privacy’, issues which have become increasingly important for education in a digital age.
9.9.1 The need for privacy and security when teaching
Teachers, instructors and students need a private place to work online. Instructors want to be able to criticize politicians or corporations without fear of reprisal; students may want to keep rash or radical comments from going public or will want to try out perhaps controversial ideas without having them spread all over Facebook. Institutions want to protect students from personal data collection for commercial purposes by private companies, tracking of their online learning activities by government agencies, or marketing and other unrequested commercial or political interruption to their studies. In particular, institutions want to protect students, as far as possible, from online harassment or bullying. Creating a strictly controlled environment enables institutions to manage privacy and security more effectively.
Learning management systems provide password protected access to registered students and authorised instructors. Learning management systems were originally housed on servers managed by the institution itself. Password protected LMSs on secure servers have provided that protection. Institutional policies regarding appropriate online behaviour can be managed more easily if the communications are managed ‘in-house.’
9.9.2 Cloud based services and privacy
However, in recent years, more and more online services have moved ‘to the cloud’, hosted on massive servers whose physical location is often unknown even to the institution’s IT services department. Contract agreements between an educational institution and the cloud service provider are meant to ensure security and back-ups.
Nevertheless, Canadian institutions and privacy commissioners have been particularly wary of data being hosted out of country, where it may be accessed through the laws of another country. There has been concern that Canadian student information and communications held on cloud servers in the USA may be accessible via the U.S. Patriot Act. For instance, Klassen (2015) writes:
Social media companies are almost exclusively based in the United States, where the provisions of the Patriot Act apply no matter where the information originates. The Patriot Act allows the U.S. government to access the social media content and the personally identifying information without the end users’ knowledge or consent. The government of British Columbia, concerned with both the privacy and security of personal information, enacted a stringent piece of legislation to protect the personal information of British Columbians. The Freedom of Information and Protection of Privacy Act (FIPPA) mandates that no personally identifying information of British Columbians can be collected without their knowledge and consent, and that such information not be used for anything other than the purpose for which it was originally collected.
Concerns about student privacy have increased even more when it became known that countries were sharing intelligence information, so there remains a risk that even student data on Canadian-based servers may well be shared with foreign countries.
Perhaps of more concern though is that as instructors and students increasingly use social media, academic communication becomes public and ‘exposed’. Bishop (2011) discusses the risks to institutions in using Facebook:
• privacy is different from security, in that security is primarily a technical, hence mainly an IT, issue. Privacy needs a different set of policies that involves a much wider range of stakeholders within an institution, and hence a different (and more complex) governance approach from security;
• many institutions do not have a simple, transparent set of policies for privacy, but different policies set by different parts of the institution. This will inevitably lead to confusion and difficulties in compliance;
• there is a whole range of laws and regulations that aim to protect privacy; these cover not only students but also staff; privacy policy needs to be consistent across the institution and be compliant with such laws and regulation;
• Facebook’s current privacy policy (2011) leaves many institutions using Facebook at a high level of risk of infringing or violating privacy laws – merely writing some kind of disclaimer will in many cases not be sufficient to avoid breaking the law.
The controversy at Dalhousie University where dental students used Facebook for violent sexist remarks about their fellow women students is an example of the risks endemic in the use of social media.
9.9.3 The need for balance
Although there may well be some areas of teaching and learning where it is essential to operate behind closed doors, such as in some areas of medicine or areas related to public security, or in discussion of sensitive political or moral issues, in general though there have been relatively few privacy or security problems when teachers and instructors have opened up their courses, have followed institutional privacy policies, and above all where students and instructors have used common sense and behaved ethically. Nevertheless, as teaching and learning becomes more open and public, the level of risk does increase.
9.9.4 Questions for consideration
1. What student information am I obliged to keep private and secure? What are my institution’s policies on this?
2. What is the risk that by using a particular technology my institution’s policies concerning privacy could easily be breached? Who in my institution could advise me on this?
3. What areas of teaching and learning, if any, need I keep behind closed doors, available only to students registered in my course? Which technologies will best allow me to do this?
References
Bishop, J. (2011) Facebook Privacy Policy: Will Changes End Facebook for Colleges? The Higher Ed CIO, October 4
Klassen, V. (2015) Privacy and Cloud-Based Educational Technology in British Columbia Vancouver BC: BCCampus
See also:
Bates, T. (2011) Cloud-based educational technology and privacy: a Canadian perspective, Online Learning and Distance Education Resources, March 25
Activity 9.9 Security and privacy
1. Who in your institution can advise you on the institution’s policy or the state law on the use of social media or indeed any network outside your institution’s private internal network(s)?
Click on the podcast for my personal comments on this issue:
An audio element has been excluded from this version of the text. You can listen to it online here: https://pressbooks.bccampus.ca/teachinginadigitalagev2/?p=249 | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/09%3A_Choosing_and_using_media_in_education%3A_the_SECTIONS_model/09.9%3A_Security_and_privacy.txt |
Figure 10.1.1 Why get on the bus when you can study online? (UBC bus loop)
In Chapters 7, 8 and 9, the use of media incorporated into a particular course or program was explored. In this chapter, the focus is on deciding whether a whole course or program should be offered partly or wholly online. In Chapter 11 the focus is on deciding when and how to adopt an approach that incorporates ‘open-ness’ in its design and delivery.
10.1.1 The many faces of online learning
Online learning, blended learning, flipped learning, hybrid learning, flexible learning, open learning and distance education are all terms that are often used inter-changeably, but there are significant differences in meaning. More importantly, these forms of education, once considered somewhat esoteric and out of the mainstream of conventional education, are increasingly taking on greater significance and in some cases becoming mainstream themselves. As teachers and instructors become more familiar and confident with online learning and new technologies, there will be more innovation in integrating online and face-to-face teaching.
10.1.1.1 Variations on blended learning
At the time of writing though it is possible to identify at least the following modes of delivery:
• classroom teaching with no technology at all (which is very rare these days);
• blended learning, which encompasses a wide variety of designs, including:
• technology-enhanced learning, or technology used as classroom aids; a typical example would be the use of Powerpoint slides and/or clickers in a lecture;
• the use of a learning management system to support classroom teaching, for storing learning materials, providing a course schedule of topics, for online discussion, and for submitting student assignments, but teaching is still delivered mainly through classroom sessions;
• the use of lecture capture for flipped classrooms, where students watch the lecture via streamed video then come to class for discussion or other work; see for instance a calculus course offered at Queen’s University, Canada;
• one semester face-to-face on campus and two semesters online (one model at Royal Roads University);
• hybrid or flexible learning requiring the redesign of teaching so that students can do the majority of their learning online, coming to campus only for very specific face-to-face teaching, such as lab or hands-on practical work, that cannot be done satisfactorily online (for examples, see Section 10.1.1.2 below);
• fully online learning with no classroom or on-campus teaching, which is one form of distance education, including:
• courses for credit, which will usually cover the same content, skills and assessment as a campus-based version, but are available only to students admitted to a program;
• non-credit courses offered only online, such as courses for continuing professional education;
• fully open courses, such as MOOCs.
More than one third of higher education students in the USA now take at least one fully online course, and about 15 per cent of students are taking only online courses. While overall enrolments in the US higher education system have slowly declined (by almost 4 per cent between 2012 to 2016), online enrolments have grown by about 5 per cent over the same period (Seaman et al., 2018). In Canadian post-secondary institutions in 2017, approximately 8 per cent of all credit course registrations were fully online (Donovan et. al., 2018).
10.1.1.2 Hybrid learning
There is an important development within blended learning that deserves special mention, and that is the total re-design of campus-based classes that takes greater advantage of the potential of technology, which I call hybrid learning, with online learning combined with focused small group face-to-face interactions or mixing online and physical lab experiences. In such designs, the amount of face-to-face contact time is usually reduced, for instance from three classes a week to one, to allow more time for students to study online.
In hybrid learning the whole learning experience is re-designed, with a transformation of teaching on campus built around the use of technology. For instance:
• Carol Twigg at the National Center for Academic Transformation has for many years worked with universities and colleges to redesign usually large lecture class programs to improve learning and reduce costs through the use of technology. This program ran very successfully between 1999 and 2018;
• Virginia Tech many years ago created a successful program for first and second year math teaching built around 24 x 7 computer-assisted learning supported by ‘roving’ instructors and teaching assistants (Robinson and Moore, 2006);
• The University of British Columbia launched in 2013 what it calls a flexible learning initiative focused on developing, delivering, and evaluating learning experiences that promote effective and dramatic improvements in student achievement. Flexible learning enables pedagogical and logistical flexibility so that students have more choice in their learning opportunities, including when, where, and what they want to learn.
Thus ‘blended learning’ can mean minimal rethinking or redesign of classroom teaching, such as the use of classroom aids, or complete redesign as in flexibly designed courses, which aim to identify the unique pedagogical characteristics of face-to-face teaching, with online learning providing flexible access for the rest of the learning.
Instructors in more than three quarters of Canadian post-secondary institutions in 2017 were integrating online with classroom teaching, but no more than one in five institutions had a significant number of courses in this format. However, most institutions are predicting a rapid increase in such courses over the next few years (Donovan et al., 2019)
10.1.2 The continuum of online learning
Figure 10.1.2 The continuum of technology-based learning (modes of delivery). Adapted from Bates and Poole, 2003.
Thus there is a continuum of technology-based learning, as illustrated in Figure 10.1.2 above.
10.1.3 Decisions, decisions!
These developments open up a whole new range of decisions for instructors. Every instructor now needs to decide:
• what kind of course or program should I be offering?
• what factors should influence this decision?
• what is the role of classroom teaching when students can now increasingly study most things online?
• should I open up my teaching to anyone, and if so, under what circumstances?
This chapter aims to help you answer these questions.
References
Bates, A. and Poole, G. (2003) Effective Teaching with Technology in Higher Education: Foundations for Success San Francisco: Jossey-Bass
Donovan, T. et al. (2019) Tracking Online and Distance Education in Canadian Universities and Colleges: 2018 Halifax NS: Canadian Digital Learning Research Association
Robinson, B. and Moore, A. (2006) ‘Virginia Tech: the Math Emporium’ in Oblinger, D. (ed.) Learning Spaces Boulder CO: EDUCAUSE
Seaman, J., Allen, I., and Seaman, J. (2018) Grade Increase: Tracking Distance Education in the United States Wellesley MA: The Babson Survey Research Group
Activity 10.1 Where on the continuum are your courses?
1. If you are currently teaching, where on the continuum is each of your courses? How easy is it to decide? Are there factors that make it difficult to decide where on the continuum any of your courses should fit?
2. How was it decided what the mode of delivery would be for the courses you teach? If you decided, what were the reasons for the location of each course on the continuum?
3. Are you happy with the decision(s)?
3. What kind of students do you have in each type of course?
There is no feedback provided on this activity | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/10%3A_Modes_of_delivery/10.1%3A_The_continuum_of_technology-based_learning.txt |
Figure 10.2.1 Which is the best?
Many surveys have found that a majority of faculty still believe that online learning or distance education is inevitably inferior in quality to classroom teaching (see for instance Jaschik and Letterman, 2014). In fact, there is no scientifically-based evidence to support this opinion. The evidence points in general to no significant differences, and if anything research suggests that blended or hybrid learning has some advantages over face-to-face teaching in terms of learning performance (see, for example, Means et al., 2010).
9.2.1 The influence of distance education on online learning
We can learn a great deal from earlier developments in distance education. Although the technology is different, fully online learning is, after all, just another version of distance education.
Much has been written about distance education (see, for instance, Wedemeyer, 1981; Peters, 1983; Holmberg, 1989; Keegan, 1990; Moore and Kearsley, 1996; Peters, 2002; Bates, 2005; Evans et al., 2008) but in concept, the idea is quite simple: students study in their own time, at the place of their choice (home, work or learning centre), and without face-to-face contact with a teacher. However, students are ‘connected’, today usually through the Internet, with an instructor, adjunct faculty or tutor who provides learner support and student assessment.
Distance education has been around a very long time. It could be argued that in the Christian religion, St. Paul’s epistle to the Corinthians was an early form of distance education (53-57 AD). The first distance education degree was offered by correspondence by the University of London (UK) in 1858. Students were mailed a list of readings, and took the same examination as the regular on-campus students. If students could afford it, they hired a private tutor, but the Victorian novelist Charles Dickens called it the People’s University, because it provided access to higher education to students from less affluent backgrounds. The program still continues to this day, but is now called the University of London (Worldwide), with more than 50,000 students in 180 countries.
In North America, historically many of the initial land-grant universities, such as Penn State University, the University of Wisconsin, and the University of New Mexico in the USA, and Memorial University, University of Saskatchewan and the University of British Columbia in Canada, had state- or province-wide responsibilities. As a result these institutions have a long history of offering distance education programs, mainly as continuing education for farmers, teachers, and health professionals scattered across the whole state or province. These programs have now been expanded to cover undergraduate and professional masters students. Australia is another country with an extensive history of both k-12 and post-secondary distance education.
Qualifications received from most of these universities carry the same recognition as degrees taken on campus. For instance, the University of British Columbia, which has been offering distance education programs since 1936, makes no distinction on student transcripts between courses taken at a distance and those taken on campus, as both kinds of students take the same examinations.
Another feature of distance education, pioneered by the British Open University in the 1970s, but later adopted and adapted by North American universities that offered distance programs, is a course design process, based on the ADDIE model, but specially adapted to serve students learning at a distance. This places a heavy emphasis on defined learning outcomes, production of high quality multimedia learning materials, planned student activities and engagement, and strong learner support, even at a distance. As a result, campus-based universities that offered distance education programs were well placed for the move into online learning in the 1990s. These universities have found that in general, students taking the online programs do almost as well as the on-campus students (course completion rates are usually within 5-10 per cent of the on-campus students – see Ontario, 2011), which is somewhat surprising as the distance students often have full-time jobs and families.
It is important to acknowledge the long and distinguished pedigree of distance education from internationally recognised, high quality institutions, because commercial diploma mills, especially in the USA, have given distance education an unjustified reputation of being of lower quality. As with all teaching, distance education can be done well or badly. However, where distance education has been professionally designed and delivered by high quality public institutions, it has proved to be very successful, meeting the needs of many working adults, students in remote areas who would otherwise be unable to access education on a full-time basis, or on-campus students wanting to fit in an extra course or with part-time jobs whose schedule clashes with their lecture schedule. However, universities, colleges and even schools have been able to do this only by meeting high quality design standards.
At the same time, there has also been a small but very influential number of campus-based teachers and instructors who quite independently of distance education have been developing best practices in online or computer-supported learning. These include Roxanne Hiltz and Murray Turoff (1978) who were experimenting with online or blended learning as early as the late 1970s at the New Jersey Institute of Technology, and Linda Harasim (2017) at Simon Fraser University, who all focused particularly on online collaborative learning and knowledge construction within a campus or school environment.
There is also plenty of evidence that teachers and instructors in many schools, colleges and universities new to online learning have not adopted these best practices, instead merely transferring lecture-based classroom practice to blended and online learning, often with poor or even disastrous results.
10.2.2 What the research tells us
There have been thousands of studies comparing face-to-face teaching to teaching with a wide range of different technologies, such as televised lectures, computer-based learning, and online learning, or comparing face-to-face teaching with distance education. With regard to online learning there have been several meta-studies. A meta-study combines the results of many ‘well-conducted scientific’ studies, usually studies that use the matched comparisons or quasi-experimental method (Means et al., 2010; Barnard et al., 2014). Nearly all such ‘well-conducted’ meta-studies find no or little significant difference between the modes of delivery, in terms of the effect on student learning or performance. For instance, Means et al. (2010), in a major meta-analysis of research on blended and online learning for the U.S. Department of Education, reported:
In recent experimental and quasi-experimental studies contrasting blends of online and face-to-face instruction with conventional face-to-face classes, blended instruction has been more effective, providing a rationale for the effort required to design and implement blended approaches. When used by itself, online learning appears to be as effective as conventional classroom instruction, but not more so.
Means et al. attributed the slightly better performance of blended learning to students spending more time on task. This highlights a common finding, that where differences have been found, they are often attributed to factors other than the mode of delivery. Tamim et al. (2011) identified ‘well-conducted’ comparative studies covering 40 years of research. Tamim et al. found there is a slight tendency for students who study with technology to do better than students who study without technology. However, the measured difference was quite weak, and the authors state:
it is arguable that it is aspects of the goals of instruction, pedagogy, teacher effectiveness, subject matter, age level, fidelity of technology implementation, and possibly other factors that may represent more powerful influences on effect sizes than the nature of the technology intervention.
Research into any kind of learning is not easy; there are just so many different variables or conditions that affect learning in any context. Indeed, it is the variables we should be examining, not just the technological delivery. In other words, we should be asking a question first posed by Wilbur Schramm as long ago as 1977:
What kinds of learning can different media best facilitate, and under what conditions?
In terms of making decisions then about mode of delivery, we should be asking, not which is the best method overall, but:
What are the most appropriate conditions for using face-to-face, blended or fully online learning respectively?
Fortunately, there is much research and best practice that provides guidance on that question, at least with respect to blended and online learning (see, for instance, Anderson, 2008; Picciano et al., 2013; Halverson et al., 2012; Zawacki-Richter and Anderson, 2014). Ironically, what we lack is good research on the unique potential of face-to-face teaching in a digital age when so much can also be done just as well online.
10.2.3 Challenging the supremacy of face-to-face teaching
Although there has been a great deal of mainly inconclusive research comparing online learning with face-to-face teaching in terms of student learning, there is very little evidence or even theory to guide decisions about what is best done online and what is best done face-to-face in a blended learning context, or about the circumstances or conditions when fully online learning is in fact a better option than classroom teaching. Generally the assumption appears to have been that face-to-face teaching is the default option by virtue of its superiority, and online learning is used only when circumstances prevent the use of face-to-face teaching, such as when students cannot get to the campus, or when classes are so large that interaction with students is at a minimum.
However, online learning has now become so prevalent and effective in so many contexts that it is time to ask:
what are the unique characteristics of face-to-face teaching that make it pedagogically different from online learning?
It is possible of course that there is nothing pedagogically unique about face-to-face teaching, but given the rhetoric around ‘the magic of the campus’ (Sarma, 2013) and the hugely expensive fees associated with elite campus-based teaching, or indeed the high cost of publicly funded campus-based education, it is about time that we had some evidence-based theory about what makes face-to-face teaching so special. This will be discussed further in Section 5 of this chapter.
In the meantime, a method for determining which mode of delivery (face-to-face, blended or online) will be discussed in the next sections.
References
Anderson, A. (ed.) (2008) The Theory and Practice of Online Learning Athabasca AB: Athabasca University Press
Barnard, R. et al. (2014) Detecting bias in meta-analyses of distance education research: big pictures we can rely onDistance Education Vol. 35, No. 3
Bates, A.W. (2005) Technology, e-Learning and Distance Education London/New York: Routledge
Evans, T., Haughey, M. and Murphy, D. (2008) International Handbook of Distance Education Bingley UK: Emerald Publishing
Halverson, L. R., Graham, C. R., Spring, K. J., & Drysdale, J. S. (2012) An analysis of high impact scholarship and publication trends in blended learning Distance Education, Vol. 33, No. 3
Harasim, L. (2017) Learning Theory and Online Technologies 2nd edition New York/London: Taylor and Francis
Hiltz S., and Turoff M.(1978) Network Nation: Human communication via computer Reading, MA: Addison Wesley
Holmberg, B. (1989) Theory and Practice of Distance Education New York: Routledge
Jaschik, S. and Letterman, D. (2014) The 2014 Inside Higher Ed Survey of Faculty Attitudes to Technology Washington DC: Inside Higher Ed
Keegan, D. (ed.) (1990) Theoretical Principles of Distance Education London/New York: Routledge
Means, B. et al. (2010) Evaluation of Evidence-Based Practices in Online Learning: A Meta-Analysis and Review of Online Learning Studies Washington, DC: US Department of Education
Moore, M. and Kearsley, G. (1996) Distance Education: A Systems View Belmont CA: Wadsworth
Ontario (2011) Fact Sheet Summary of Ontario eLearning Surveys of Publicly Assisted PSE Institutions Toronto: Ministry of Training, Colleges and Universities
Peters, O. (1983) Distance education and industrial production, in Sewart et al. (eds.) Distance Education: International Perspectives London: Croom Helm
Peters, O. (2002) Distance Education in Transition: New Trends and Challenges Oldenberg FGR: Biblothecks- und Informationssystem der Universität Oldenberg
Picciano, A., Dziuban, C. and & Graham, C. (eds.), Blended Learning: Research Perspectives, Volume 2. New York: Routledge, 2013
Schramm, W. (1977) Big Media, Little Media Beverley Hills CA/London: Sage
Sarma, S. (2013) The Magic Beyond the MOOCs Boston MA: LINC 2013 conference (recorded presentation)
Tamim, R. et al. (2011) What Forty Years of Research Says About the Impact of Technology on Learning: A Second-Order Meta-Analysis and Validation Study Review of Educational Research, Vol. 81, No. 1
Wedemeyer, C. (1981) Learning at the Back Door: Reflections on Non-traditional Learning in the Lifespan Madison: University of Wisconsin Press
Zawacki-Richter, O. and Anderson, T. (eds.) (2014) Online Distance Education: Towards a Research Agenda Athabasca AB: AU Press, pp. 508
Activity 10.2 Defining the magic of the campus
1. Can you define the ‘magic of the campus’? What is it about face-to-face teaching that makes it special, compared with teaching online? Write down the three things you think are the most important.
2. Could you do the same for teaching online? If not, what are the things that make the campus special?
Click on the podcast below for some feedback on these questions
An audio element has been excluded from this version of the text. You can listen to it online here: https://pressbooks.bccampus.ca/teachinginadigitalagev2/?p=260 | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/10%3A_Modes_of_delivery/10.2%3A_Comparing_modes_of_delivery.txt |
Figure 10.3.1 Who are your students? Image: UBC Library
When making choices about mode of delivery, teachers and instructors need to ask the following four questions:
• who are – or could be – my students?
• what is my preferred teaching approach?
• what are the content and skills that I need to teach?
• what resources will I have to support my decision?
As always, start with the learners.
10.3.1 Fully online/distance learners
Research (see for instance Dabbagh, 2007) has repeatedly shown that fully online courses suit some types of student better than others:
• older, more mature students;
• students with already high levels of education;
• part-time students who are working and/or with families.
This applies not only to MOOCs (see Chapter 5) and other non-credit courses, but even more so to courses and programs for credit. There are in fact several different markets for online learning.
10.3.1.1 Undergraduate online students
Today, ‘distance’ is more likely to be psychological or social, rather than geographical. For instance, from survey data regularly collected from students at the University of British Columbia (UBC):
• less than 20 per cent give reasons related to distance or travel for taking an online course;
• most of the more than 10,000 or so UBC students (there are over 60,000 students in total) taking at least one fully online course are not truly distant. The majority (over 80 per cent) live in the Greater Vancouver Metropolitan Area, within 90 minutes commute time to the university, and almost half within the relatively compact City of Vancouver. Comparatively few (less than 10 per cent) live outside the province (although this proportion is slowly growing each year);
• two thirds of UBC’s online students have paid work of one kind or another;
• many undergraduate students in their fourth year take an online course because the face-to-face classes are ‘capped’ because of their large size, or because they are short of the required number of credits to complete a degree. Taking a course online allows these students to complete their program without having to come back for another year;
• the main reason for most UBC students taking fully online courses is the flexibility they provide, given the work and family commitments of students and the difficulty caused by timetable conflicts for face-to-face classes.
In the USA, almost one in three undergraduate students are taking at least one online course (Allen and Seaman, 2017). At an undergraduate level, students are likely to take a maximum of three to four online courses as part of a regular campus-based degree program at universities and up to five online courses at two year colleges, in Canada (Donovan et al., 2018).
Until recently in North America, there were few undergraduate programs offered entirely online, except in specialist institutions such as the open universities in Canada (Athabasca, Téluq, Thompson Rivers Open Learning) and University of Phoenix, Western Governors University, and University of Maryland University College in the USA. However, in recent years a number of specialist online undergraduate programs have started to be offered, such as the Bachelor of Mining Engineering Technology for working miners at Queen’s University, Canada
This suggests that fully online courses are more suitable for more experienced students with a strong motivation to take such courses because of the impact they have on their quality of life. In general, online students need more self-discipline in studying and a greater motivation to study to succeed. This does not mean that other kinds of students cannot benefit from online learning, but extra effort needs to go into the design and support of such students online.
10.3.1.2 Graduate online students
Although in the USA, the proportion of students taking distance education courses at a graduate level overall is almost the same (17 per cent) as those taking on-campus graduate courses – 15 per cent, the proportion of students taking distance education courses at a graduate level is much higher for private, not-for profit – 37 per cent, and for-profit institutions – 28 per cent (Allen and Seaman, 2017). (As in Canada – Donovan. et al., 2018 – distance education now is almost synonymous with online learning in the USA).
The most rapid area of growth in online courses is for masters programs aimed at working professionals. So far, apart from MBAs and teacher education, public universities tend to be relatively slow in recognising the importance of this market, which at worse could be self-financing, and at best could bring in much needed additional revenues. The for-profit universities, though, such as the University of Phoenix, Laureate University and Capella University, and especially some of the private, not-for-profit universities in the USA have been quicker to move into this market.
10.3.1.3 Remote learners
Often it is also assumed that isolated or remote learners are the main market for distance or fully online learners in that they are a long way away from any local school, college or university. Certainly in Canada, there are such students and the ability to study locally rather than travel great distances can be very appealing. However, in many remote rural areas, Internet access can be difficult, with either slow satellite connections or telephone-based, slow-speed modems. Remote learners will also struggle if there is no easily accessible or culturally appropriate local support for their studies.
Since the vast majority of online learners are urban, living within one hour’s travel of a college or university campus, it is the flexibility rather than the distance that matters to these learners.
10.3.1.4 Lifelong learners
On the other hand, fully online courses really suit working professionals. In a digital age, the knowledge base is continually expanding, jobs change rapidly, and hence there is strong demand for on-going, continuing education, often in ‘niche’ areas of knowledge. Online learning is a convenient and effective way of providing such lifelong learning.
Lifelong learners are often working with families and really appreciate the flexibility of studying fully online. They often already have higher education qualifications such as a first degree, and therefore have learned how to study successfully. They may be engineers looking for training in management, or professionals wanting to keep up to date in their professional area. They are often better motivated, because they can see a direct link between the new course of study and possible improvement in their career prospects. They are therefore ideal students for online courses (even though they may be older and less tech savvy than students coming out of high school).
What is important for such learners is that the courses are technically well designed, in that learners do not need to be highly skilled in using computers to be able to study the courses.
10.3.1.5 Changing demographics
One other factor to consider is the impact of changing demographics. In the USA, overall higher education enrolments declined by 3 per cent between 2012-2015, while distance education enrolments increased by 4 per cent over the same period (Allen and Seaman, 2017).
In jurisdictions where the school-age population is starting to decline, expanding into lifelong learning markets may be essential for maintaining student enrolments. Although the rate of growth in distance education/online learning is not spectacular, it may eventually turn out to be a way to keep some academic departments alive.
10.3.1.6 New business models
However, to make lifelong learning online programs work, institutions need to make some important adjustments. In particular there must be incentives or rewards for faculty to move in this direction and there needs to be some strategic thinking about the best way to offer such programs.
The University of British Columbia has developed a series of very successful, fully online, self-financing professional masters’ programs. Students can initially try one or two courses in the Graduate Certificate in Rehabilitation before applying to the master’s program. The certificate can be completed in less than two years while working full-time, and paying per course rather than for a whole Master’s year, providing the flexibility needed by lifelong learners. UBC also partnered with Tec de Monterrey in Mexico, with the same program being offered in English by UBC and in Spanish by Tec de Monterrey, as a means of kick-starting its very successful Master in Educational Technology program, which, when it opened, doubled the number of graduate students in UBC’s Faculty of Education, and is still running now almost 20 years after its initial offering. We shall see these examples are important when we examine the development of modular programming in Section 11.5.2.
Online learning also offers the opportunity to offer programs where an institution has unique research expertise but insufficient local students to offer a full master’s program. By going fully online, perhaps in partnership with another university with similar expertise but in a different jurisdiction, it may be able to attract students from across the country or even internationally, enabling the research to be more widely disseminated and to build a cadre of professionals in newly emerging areas of knowledge – again an important goal in a digital age.
10.3.2 Blended learning learners
In terms of blended learning, the ‘market’ is less clearly defined than for fully online learning. The benefit for students is increased flexibility, but they will still need to be relatively local in order to attend the campus-based sessions. The main advantage is for the 50 per cent or more of students, at least in Canada, who are working more than 15 hours a week (Marshall, 2010) to help with the cost of their education and to keep their student debt as low as possible. Also, blended learning provides an opportunity for the gradual development of independent learning skills, as long as this is an intentional teaching strategy.
The research also suggests that these skills of independent learning need to be developed while students are on campus. In other words, online learning, in the form of blended learning, should be deliberately introduced and gradually increased as students work through a program, so by the time they graduate, they have the skills to continue to learn independently – a critical skill for the digital age. In general, it is not a good idea to offer fully online courses in the early years of a university or college career, unless they are exceptionally well designed with a considerable amount of online learner support – and hence are likely to be expensive to mount, if they are to be successful.
As well as the benefits of more flexibility for students, especially those working part-time, the academic benefits of blended learning are being better understood. These will be discussed in more detail in the next section. At this point, there is evidence that in Canada, at least, more and more institutions are seeing a move by instructors to blended or hybrid learning, providing the advantages of both online and face-to-face teaching (Donovan et al., 2018)
10.3.3 Face-to-face learners
Many students coming straight from high school will be looking for social, sporting and cultural opportunities that a campus-based education provides. Also students lacking self-confidence or experience in studying are likely to prefer face-to-face teaching, providing that they can access it in a relatively personal way.
However, the academic reasons for preference for face-to-face teaching by freshmen and women are less clear, particularly if students are faced with very large classes and relatively little contact with professors in the first year or so of their programs. In this respect, smaller, regional institutions, which generally have smaller classes and more face-to-face contact with instructors, have an advantage. Also, blended or flipped learning is increasingly being used for very large classes, with lectures available online, and smaller groups meeting face-to-face with an instructor or teaching assistants.
We shall see later in this chapter that blended and fully online learning offer the opportunity to re-think the whole campus experience so that better support is provided to on-campus learners in their early years in post-secondary education. More importantly, as more and more studying is done online, universities and colleges will be increasingly challenged to identify the unique pedagogical advantages of coming to campus, so that it will still be worthwhile for students to get on the bus to campus every morning.
10.3.4 Know your learners
It is therefore very important to know what kind of students you will be teaching. For some students, it will be better to enrol in a face-to-face class but be gradually introduced to online study within a familiar classroom environment. For other students, the only way they will take the course will be if it is available fully online. It is also possible to mix and match face-to-face and online learning for some students who want the campus experience, but also need a certain amount of flexibility in their studying. Going online may enable you to reach a wider market (critical for departments with low or declining enrolments) or to meet strong demand from working professionals. Who are (or could be) your students? What kind of course will work best for them?
We shall see that identifying the likely student market for a course or program is the strongest factor in deciding on mode of delivery.
References
Allen, I. and Seaman, J. (2017) Digital Learning Compass: Distance Education Enrollment Report 2017 Babson Survey Research Group/eLiterate/WCET
Dabbagh, N. (2007) The online learner: characteristics and pedagogical implicationsContemporary Issues in Technology and Teacher Education, Vol. 7, No.3
Donovan, T. et al. (2018) Tracking Online and Distance Education in Canadian Universities and Colleges: 2018 Halifax NS: Canadian Digital Learning Research Association
Marshall, K. (2010) Employment patterns of post-secondary students Perspectives on Labour and Income Ottawa ON: Statistics Canada, September
Activity 10.3 Knowing your students
1. Choose one of your courses. Do you know the key student demographics: age, gender, working or not, single or with families, language skills? If not how could you get this information?
2. If you had this information, would it change the way you teach?
3. If you are teaching a face-to-face class, are there other kinds of students who would be interested in taking your course if it was online?
There is no feedback on this activity. | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/10%3A_Modes_of_delivery/10.3%3A_Which_mode.txt |
Figure 10.4.1 What should students enrolled in campus courses do online? Image: UBC Library
Analysing student demographics may help to decide whether or not a course or program should be either campus-based or fully online, but we need to consider more than just student demographics to make the decision about what to do online and what to do on campus for the majority of campus-based courses and programs that will increasingly have an online component.
10.4.1 A suggested method
10.4.1.1 Finding an approach based on successful experience
It should be stated up front that there is no generally agreed theory or even best practices for making this decision. The default mode has been that face-to-face teaching must be inherently superior, and you only go online if you must. However, we have seen that online learning has over the last ten years or so demonstrated clearly that many areas of knowledge can be taught just as well or better online. I will look therefore to examples where there has been a conscious decision to identify the relative affordances of different media, including face-to-face teaching. The area where this becomes most clear is in the teaching of science.
I am going to draw on a method used initially at the U.K. Open University for designing distance education courses and programs in science in the 1970s. The challenge was to decide what was best done in print, on television, via home experiment kits, and finally in a one week residential hands-on summer school at a traditional university. Since then, Dietmar Kennepohl and Lawton Shaw, of Athabasca University, have edited an excellent book about teaching science online (Kennepohl and Shaw, 2010). Also, the Colorado Community College System has recently been using a combination of remotely operated labs for student practical work, combined with home kits, for teaching online introductory science courses (Schmidt and Shea, 2015).
Each of these initiatives has adopted a pragmatic method for making decisions about what must be done face-to-face and what can be done online. What each of these approaches had in common wais trusting the knowledge and experience of subject experts who are willing to approach this question in an open-minded way, and working with instructional designers or media producers on an equal footing.
From these experiences, I have extracted one possible process for determining when to go online and when not to, on purely pedagogical grounds, for a course that is being designed from scratch in a blended delivery mode. It is based on a five step process:
1. identify the overall instructional approach/pedagogy required
2. identify the main content to be covered
3. identify the main skills to be taught
4. analyse the resources available
5. analyse the most appropriate mode of delivery for each of the learning objectives identified above
I will choose a subject area at random: haematology (the study of blood), in which I am not an expert. But here’s what I would suggest if I was working with a subject specialist in this area.
Figure 10.4.2 Can the study of haematology be done online?
Image: CC Wikimedia Commons: National Cancer Institute, USA
10.4.1.2 Step 1: identify the main instructional approach.
This is discussed in some detail in Chapters 2 to 4, but here are the kinds of decision to be considered:
Figure 10.4.3 Which teaching approach?
This should lead to a general plan or approach to teaching that identifies the teaching methods to be used in some detail. In the example of haematology, the instructor wants to take a more constructivist approach, with students developing a critical approach to the subject matter. In particular, she wants to relate the course specifically to certain issues, such as security in handling and storing blood, factors in blood contamination, and developing student skills in analysis and interpretation of blood samples.
10.4.1.3 Step 2. Identify the main content to be covered
Content covers facts, data, hypotheses, ideas, arguments, evidence, and description of things (for instance, showing or describing the parts of a piece of equipment and their relationship). What do they need to know in this course? In haematology, this will mean understanding the chemical composition of blood, what its functions are, how it circulates through the body, descriptions of the relevant parts of cell biology, what external factors may weaken its integrity or functionality, and so forth, the equipment used to analyse blood and how the equipment works, principles, theories and hypotheses about blood clotting, the relationship between blood tests and diseases or other illnesses, and so on.
In particular, what are the presentational requirements of the content in this course? Dynamic activities need to be explained, and representing key concepts in colour will almost certainly be valuable. Observations of blood samples under many degrees of magnitude will be essential, which will require the use of a microscope.
There are now many ways to represent content: text, graphics, audio, video and simulations. For instance, graphics, a short video clip, or photographs down a microscope can show examples of blood cells in different conditions. Increasingly this content is already available over the web for free educational use (for instance, see the American Society of Hematology’s video library). Creating such material from scratch is more expensive, but is becoming increasingly easy to do with high quality, low cost digital recording equipment. Using a carefully recorded video of an experiment will often provide a better view than students will get crowding around awkward lab equipment.
10.4.1.4 Step 3. Identify the main skills to be developed during the course
Skills describe how content will be applied and practiced. This might include analysis of the components of blood, such as the glucose and insulin levels, the use of equipment (where ability to use equipment safely and effectively is a desired learning outcome), diagnosis, interpreting results by making hypotheses about cause and effect based on theory and evidence, problem-solving, and report writing.
Developing skills online can be more of a challenge, particularly if it requires manipulation of equipment and a ‘feel’ for how equipment works, or similar skills that require tactile sense. (The same could be said of skills that require taste or smell). In our hematology example, some of the skills that need to be taught might include the ability to analyse analytes or particular components of blood, such as insulin or glucose, to interpret results, and to suggest treatment. The aim here would be to see if there are ways these skills can also be taught effectively online. This would mean identifying the skills needed, working out how to develop such skills (including opportunities for practice) online, and how to assess such skills online.
Let’s call Steps 2 and 3 the key learning objectives for the course.
10.4.1.5 Step 4: Analyse the most appropriate mode for each learning objective
Then create a table as in Figure 10.4.4:
Figure 10.4.4 Allocating mode of delivery
In this example, the instructor is keen to move as much as possible online, so she can spend as much time as possible with students, dealing with laboratory work and answering questions about theory and practice. She was able to find some excellent online videos of several of the key interactions between blood and other factors, and she was also able to find some suitable graphics and simple animations of the molecular structure of blood which she could adapt, as well as creating with the help of a graphics designer her own graphics. Indeed, she found she had to create relatively little new material or content herself.
The instructional designer also found some software that enabled students to design their own laboratory set-up for certain elements of blood testing which involved combining virtual equipment, entering data values and running an experiment. However, there were still some skills that needed to be done hands-on in the laboratory, such as inserting glucose and using a ‘real’ microscope to analyse the chemical components of blood. However, the online material enabled the instructor to spend more time in the lab with students.
It can be seen in this example that most of the content can be delivered online, together with a critically important skill of designing an experiment, but some activities still need to be done ‘hands-on’. This might require one or more evening or weekend sessions in a lab for hands-on work, thus delivering most of the course online, or there may be so much hands-on work that the course may have to be a hybrid of 50 per cent hands-on lab work and 50 per cent online learning.
With the development of animations, simulations and online remote labs, where actual equipment can be remotely manipulated, it is becoming increasingly possible to move even traditional lab work online. At the same time, it is not always possible to find exactly what one needs online, although this will improve over time. In other subject areas such as humanities, social sciences, and business, it is much easier to move the teaching online.
This is a crude method of determining the balance between face-to-face teaching and online learning for a blended learning course, but it least it’s a start. It can be seen that these decisions have to be relatively intuitive, based on instructors’ knowledge of the subject area and their ability to think creatively about how to achieve learning outcomes online. However, we have enough experience now of teaching online to know that in most subject areas, a great deal of the skills and content needed to achieve quality learning outcomes can be taught online. It is no longer possible to argue that the default decision must always be to do the teaching in a face-to-face manner.
Thus every instructor now needs to ask the question: if I can move most of my teaching online, what are the unique benefits of the campus experience that I need to bring into my face-to-face teaching? Why do students have to be here in front of me, and when they are here, am I using the time to best advantage?
10.4.2 Analyse the resources available
There is one more consideration besides the type of learners, the overall teaching method, and making decisions based on pedagogical grounds, and that is to consider the resources available. (This should really be Step 4, before allocating learning objectives to different modes, but it will be difficult to avoid in any case.).
10.4.2.1 The time of the instructor
In particular, the key resource is the time of the teacher or instructor. Careful consideration is needed about how best to spend the limited time available to an instructor. It may be all very well to identify a series of videos as the best way to capture some of the procedures for blood testing, but if these videos do not already exist in a format that can be freely used, shooting video specially for this one course may not be justified, in terms of either the time the instructor would need to spend on video production, or the costs of making the videos with a professional crew.
Time to learn how to do online teaching is especially important. There is a steep learning curve and the first time will take much longer than subsequent online courses. The institution should offer some form of training or professional development for instructors thinking of moving online or into blended learning. Ideally instructors should get some release time (up to one semester from one class) in order to do the design and preparation for an online course, or a re-designed hybrid course. This however is not always possible, but one thing we do know. Instructor workload is a function of course design. Well designed online courses should require less rather than more work from an instructor.
10.4.2.2. Learning technology support staff.
If your institution has a service unit for faculty development and training, instructional designers and web designers for supporting teaching, use them. Such staff are often qualified in both educational sciences and computer technology. They have unique knowledge and skills that can make your life much easier when teaching online. (This will be discussed further in Chapter 13.)
The availability and skill level of learning technology support from the institution is a critical factor. Can you get the support of an instructional designer and media producers? If not, it is likely that much more will be done face-to-face than online, unless you are already very experienced in online learning.
10.4.2.3 Readily available technology
Most institutions now have a learning management system such as Blackboard or Moodle, or a lecture capture system for recording lessons. But increasingly, instructors will need access to media producers who can create videos, digital graphics, animations, simulations, web sites, and access to blog and wiki software. Without access to such technology support, instructors are more likely to fall back on tried and true classroom teaching.
10.4.2.4 Colleagues experienced in blended and online learning
It really helps if there are experienced colleagues in the department who understand the subject discipline and have done some online teaching. They will perhaps even have some materials already developed, such as graphics, that they will be willing to share.
10.4.2.5 Money
Are there resources available to buy you out for one semester to spend time on course design? Many institutions have development funds for innovative teaching and learning, and there may be external grants for creating new open educational resources, for instance. This will increase the practicality and hence the likelihood of more of the teaching moving online.
We shall see that as more and more learning material becomes available as open educational resources, teachers and instructors will be freed up from mainly content presentation to focusing on more interaction with students, both online and face to face. However, although open educational resources are becoming increasingly available, they may not exist in the topics required or they may not be of adequate quality in terms of either content or production standards (see Chapter 11.2 for more on OERs).
The extent to which these resources are available will help inform you on the extent to which you will be able to go online and meet quality standards. In particular, you should think twice about going online if none of the resources listed above is going to be available to you.
10.4.3 The case for multiple modes
Increasingly, it is becoming difficult to separate markets for particular courses or programs. Although the majority of students taking a first year university course are likely to be coming straight from high school, some will not. There may be a minority of students who left high school directly for work, or went to a two year college to get vocational training, but now find they need a degree. Especially in professional graduate programs, students may be a mix of those who have just completed their bachelor’s course and are still full-time students, and those that are already in the work-force but need the specialist qualification. There will be a mix of students in third and fourth year undergraduate courses, some of whom will be working over 15 hours a week, and others who are studying more or less full time. In theory, then, it may be possible to identify a particular market for mainly face-to-face, blended or fully online learning, but in practice most courses are likely to have a mix of students with different needs.
If, though, as seems likely, more and more courses will end up as blended learning, then it is worth thinking about how courses could be designed to serve multiple markets. For instance, if we take our haematology course, it could be offered to full-time third year undergraduate students studying biology, or it could also be offered either on its own or with other related courses as a certificate in blood management for nurses working in hospitals. It might also be useful for students studying medicine who have not taken this particular course as an undergraduate, or even for patients with conditions related to their blood levels, such as diabetes.
If for instance our instructor developed a course where students spent approximately 50 per cent of their time online and the rest on campus, it may eventually be possible to design this for other markets as well, with perhaps practical work for nurses being done in the hospital under supervision, or just the online part being offered as a short MOOC for patients. For some courses (perhaps not haematology), it may be possible to offer the course wholly online, in blended format or wholly face-to-face. This would allow the same course to reach several different markets.
10.4.4 Questions for consideration in choosing modes of delivery
In summary, here are some questions to consider, when designing a course from scratch:
1. What kind of learners are likely to take this course? What are their needs? Which mode(s) of delivery will be most appropriate to these kinds of learners? Could I reach more or different types of learners by choosing a particular mode of delivery?
2. What is my view of how learners can best learn on this course? What is my preferred method(s) of teaching to facilitate that kind of learning on this course?
3. What is the main content (facts, theory, data, processes) that needs to be covered on this course? How will I assess understanding of this content?
4. What are the main skills that learners will need to develop on this course? What are the ways in which they can develop/practice these skills? How will I assess these skills?
5. How can technology help with the presentation of content on this course?
6. How can technology help with the development of skills on this course?
7. When I list the content and skills to be taught, which of these could be taught:
• fully online
• partly online and partly face-to-face
• can only be taught face-to-face?
8. What resources do I have available for this course in terms of:
• professional help from instructional designers and media producers;
• possible sources of funding for release time and media production;
• good quality open educational resources.
9. What kind of classroom space will I need to teach the way I wish? Can I adapt existing spaces or will I need to press for major changes to be made before I can teach the way I want to?
10. In the light of the answers to all these questions, which mode of delivery makes most sense?
References
Kennepohl, D. and Shaw, L. (eds.) (2010) Accessible Elements: Teaching Science Online and at a Distance Athabasca AB: Athabasca University Press
Schmidt, S. and Shea, P. (2015) NANSLO Web-based Labs: Real Equipment, Real Data, Real People! WCET Frontiers
Activity 10.4 Deciding on the mode of delivery
1. Try following the process above for a possible new course that you would like to teach or for revising a course you are already teaching.
There is no feedback on this activity. | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/10%3A_Modes_of_delivery/10.4%3A_Choosing_between_face-to-face_and_online_teaching_on_campus.txt |
Figure 10.5.1 The magic of the campus?
Image: © Cambridge Advanced Studies Program, Cambridge University, U.K., 2015
As more and more teaching is moved online, even for campus-based students, it will become increasingly important to think about the function of face-to-face teaching and the use of space on campus.
10.5.1 Identifying the unique characteristics of face-to-face teaching in a digital world
Sanjay Sarma, Director of MIT’s Office of Digital Learning, made an attempt at MIT’s LINC 2013 conference to identify the difference between campus-based and online learning, and in particular MOOCs. He made the distinction between MOOCs as open courses available to anyone, reflecting the highest level of knowledge in particular subject areas, and the ‘magic’ of the on-campus experience, which he claimed is distinctly different from the online experience (Sarma, 2013).
He argued that it is difficult to define or pin down the magic that takes place on-campus, but referred to:
• ‘in-the-corridor’ conversations between faculty and staff;
• hands-on engineering with other students outside of lectures and scheduled labs;
• the informal learning that takes place between students in close proximity to one another.
There are a couple of other characteristics that Sarma hinted at but did not mention explicitly in his presentation:
• the very high standard of the students admitted to MIT, who ‘push’ each other to even higher standards;
• the importance of the social networks developed by students at MIT that provide opportunities later in life.
Easy and frequent access to laboratories is a serious contender for the uniqueness of campus-based learning, as this is difficult to provide online, although there is an increasing number of developments in remote labs and the use of simulations. Opportunities for finding future spouses is another contender. Probably the most important though is access to social contacts that can further your career (see my podcast feedback on Activity 10.2 for more on the ‘unique affordances’ of campus-based teaching.).
I leave it to you to judge whether these are unique features of face-to-face teaching, or whether the key advantages of a campus experience are more specific to expensive and highly selective elite institutions. For most teachers and instructors, though, more concrete and more general pedagogical advantages for face-to-face teaching need to be identified.
10.5.2 The law of equal substitution
In the meantime, we should start from the assumption that from a strictly pedagogical perspective, most courses can be taught equally well online or face-to-face, what I call the law of equal substitution. This means that other factors, such as cost, convenience for teachers, social networking, the skills and knowledge of the instructor, the type of students, or the context of the campus, will be stronger determinants of whether to teach a course online or on campus than the academic demands of the subject matter. These are all perfectly justifiable reasons for privileging the campus experience.
At the same time, there are likely to be some critical areas where there is a strong pedagogical rationale for students to learn in a face-to-face or hands-on context. In other words, we need to identify the exceptions to the law of equal substitution. These unique pedagogical characteristics of campus-based teaching need to be researched more carefully, or at least be more theory-based than at present, but currently there is no powerful or convincing method or rationale to identify what the uniqueness is of the campus experience in terms of learning outcomes. The assumption appears to be that the campus experience must be better, at least for some things, because this is the way we have always done things. We need to turn the question on its head: what is the academic or pedagogical justification for the campus, when students can learn most things online?
10.5.3 The impact of online learning on the campus experience
This question becomes particularly important when we examine how an increased move to blended or hybrid learning is going to impact on learning spaces. In some ways, this may turn out to be a ticking time bomb for schools, colleges and universities.
10.5.3.1 Rethinking the design of classrooms
As we move from lectures to more interactive learning, we will need to think about the spaces in which learning will take place, and how pedagogy, online learning and the design of learning spaces influence one another. To make it worthwhile for students to come to campus when they can do an increasing amount of their study online, the on-campus activities must be meaningful. If for instance we want students to come to campus for interpersonal communication and intense group work, will there be sufficiently flexible and well-equipped spaces for students to do this, remembering that they will want to combine their online work with their classroom activities?
In essence, new technology, hybrid learning and the desire to engage students and to develop the knowledge and skills needed in a digital age are leading some teachers and architects to rethink the classroom and the way it is used.
Figure 10.5.2 Design for an interactive classroom from Steelcase (© Steelcase, 2013)
Steelcase, a leading American manufacturer of office and educational furniture, is not only conducting impressive research into learning environments, but is way ahead of many of our post-secondary institutions in thinking through the implications of online learning for classroom design. Their educational research website, and two of their reports: Active Learning Spaces and Rethinking Space: Sparking Creativity are documents that all post-secondary institutions and even k-12 planners should be looking at.
In Active Learning Spaces, Steelcase reports:
Formal learning spaces have remained the same for centuries: a rectangular box filled with rows of desks facing the instructor and writing board….As a result, today’s students and teachers suffer because these outmoded spaces inadequately support the integration of the three key elements of a successful learning environment: pedagogy, technology and space.
Change begins with pedagogy. Teachers and teaching methods are diverse and evolving. From one class to the next, sometimes during the same class period, classrooms need change. Thus, they should fluidly adapt to different teaching and learning preferences. Instructors should be supported to develop new teaching strategies that support these new needs.
Technology needs careful integration. Students today are digital natives, comfortable using technology to display, share and present information. Vertical surfaces to display content, multiple projection surfaces and whiteboards in various configurations are all important classroom considerations.
Space impacts learning. More than three-quarters of classes include class discussions and nearly 60 percent of all classes include small group learning, and those percentages are continuing to grow. Interactive pedagogies require learning spaces where everyone can see the content and can see and interact with others. Every seat can and should be the best seat in the room. As more schools adopt constructivist pedagogies, the “sage on the stage” is giving way to the “guide on the side.” These spaces need to support the pedagogies and technology in the room to allow instructors who move among teams to provide real-time feedback, assessment, direction and support students in peer-to-peer learning. Pedagogy, technology and space, when carefully considered and integrated, define the new active learning ecosystem.
In Rethinking Space: Sparking Creativity, Andrew Kim, Steelcase Education Researcher, states:
Creative work is most effective in learning spaces that support team work flow and sharing of information.
Figure 10.5.4 Interactive classroom at Queen’s University, Kingston, Ontario
The design of classroom spaces now needs to take into account that students are doing an increasing amount of their work online (and often outside the classroom). The classroom must support opportunities for accessing, working on, sharing and demonstrating knowledge gained both within and outside the classroom. Thus if the classroom is organized into ‘clusters’ of furniture and equipment to support small group work, these clusters will also require power so students can plug in their devices, wireless Internet access, and the ability to transmit work to shared screens around the room (in other words, a class Intranet). Students also need quiet places or breakout spaces where they can work individually as well as in groups. When faculty are presented with such use of space, they naturally adopt more active learning approaches.
10.5.3.2 The impact of flipped classrooms and hybrid learning on classroom design
These classrooms designs assume that students are learning in relatively small classes. However, we are also seeing the redesign of large lecture classes using hybrid designs such as flipped classrooms. Indeed Mark Valenti (2013) of the Sextant Group (an audio-visual company) is reported as saying: ‘We’re basically seeing the beginning of the end of the lecture hall.’
Nevertheless, given the current financial context, we should not assume that the classroom time for these redesigned large lectures classes will be spent in small groups in individual classrooms (there are probably not enough small classrooms to accommodate these classes which often have over a thousand students). Larger spaces that can be organized into smaller working groups, then easily reconvened into a large, single group, will be needed. What the space for these large classes certainly should not be is the raked rows of benches which now are now the norm in most large lecture theatres.
Steelcase is also doing research on appropriate spaces for faculty. For instance, if a university or department is planning a learning commons or common area for students, why not locate faculty offices in the same general area instead of in a separate building? Indeed, a case could be made for integrating faculty office space with more open teaching areas.
10.5.3.3 The impact on capital building plans
It is obvious why a company such as Steelcase is interested in these developments. There is a tremendous commercial opportunity for selling new and better forms of classroom furniture that meets these needs. However, that is the problem. Universities, colleges and especially schools simply do not have the money to move quickly towards new classroom designs, and even if they did, they should do some careful thinking first about:
• what kind of campus will be needed over the next 20 years, given the rapid moves to hybrid and online learning;
• how much they need to invest in physical infrastructure when students can do much of their studies online.
Nevertheless, there are several opportunities for at least setting priorities for innovation in classroom design:
• where new campuses or major buildings are being built or renewed;
• where large first and second year classes are being redesigned: maybe a prototype classroom design could be tried for one of these large lecture redesigns and tested; if successful the model could be added slowly to other large lecture classes;
• where a department or program is being redesigned to integrate online learning and classroom teaching in a major way; they would receive priority for funding a new classroom design;
• all major new purchases of classroom furniture to replace old or worn out equipment should first be subject to a review of classroom designs.
The important point here is that investment in new or adapted physical classroom space should be driven by decisions to change pedagogy/teaching methods. This will mean bringing together academics, IT support staff, instructional designers and staff from facilities, as well as architects and furniture suppliers. Second, as Winston Churchill said: ‘we shape our buildings and afterwards our buildings shape us.’ Providing teachers and instructors with a flexible, well-designed learning environment is likely to encourage major changes in their teaching; stuffing them into rectangular boxes with rows of desks will do the opposite.
Perhaps most important of all, institutions need to start re-examining their future growth plans for buildings on campus. In particular:
• will we need additional classrooms and additional lecture theatre buildings if students will be spending up to half their time studying online or in flipped classes?
• do we have enough learning areas where large numbers of students can work in small groups and can then quickly reconvene?
• do we have the technical facilities that will allow students seamlessly to work and study both face-to-face and online, and to share and capture the work when working physically together on campus?
• would we be better investing in the re-design of existing space rather than building new learning spaces?
What is clear is that institutions now need to do some hard thinking about online learning, its likely impact on campus teaching, and above all what kind of campus experience we want students to have when they can do much of their studying online. It is this thinking that should shape our investment in buildings, desks and chairs.
10.5.4 Re-thinking the role of the campus
If we accept the principle of equal substitution for many academic purposes, then this brings us back to the student on the bus question. If students can learn most things equally well (and more conveniently) online, what can we offer them on campus that will make the bus journey worthwhile? This is the real challenge that online learning presents.
It is not just a question of what teaching activities need to be done in a face-to-face class or lab, but the whole cultural and social purpose of a school, college or university. Students in many of our large, urban universities have become commuters, coming in just for their lectures, maybe using the learning commons between lectures, getting a bite to eat, then heading home. As we have ‘massified’ our universities, the broader cultural aspects have been lost.
Online and hybrid learning provides a chance to re-think the role and purpose of the whole campus, as well as what we should be doing in classrooms when students have online learning available any time and anywhere. Of course we could just close up shop and move everything online (and save a great deal of money), but we should at least explore what would be lost before doing that.
References
Sarma, S. (2013) The Magic Beyond the MOOCs Boston MA: LINC 2013 conference
Steelcase Education (undated) Active Learning Spaces Michigan: Grand Rapids
Steelcase Education (undated) Rethinking Space: Sparking Creativity Michigan: Grand Rapids
Valenti, M. (2013), in Williams, L., ‘AV trends: hardware and software for sharing screens, University Business, June
Activity 10.5 Redesigning your classroom space
Where the caretaker determines pedagogy: I worked in one school where every morning the chairs and desks were laid out in neat rows facing the front. The caretaker would get furious if they were left arranged in any other layout by the end of the day. I therefore spent too much lesson time with students re-arranging the desks for group work then tidying up afterwards. (I was young and didn’t dare defy the caretaker, who was quite formidable).
1. If you were designing from scratch a learning space for a group of 40 students (maximum), what would the learning space look like, given all the potential technology and teaching methods you and the students could be using?
2.If you have a lecture class of 200 students and wanted to change your teaching method, how would you redesign the teaching and what kind of space(s) would you need?
Key Takeaways from Chapter 10
1. There is a continuum of technology-based learning, from ‘pure’ face-to-face teaching to fully online programs. Every teacher or instructor needs to decide where on the continuum a particular course or program should be.
2. We do not have good research evidence or theories to make this decision, although we do have growing experience of the strengths and limitations of online learning. What is particularly missing is an evidence-based analysis of the strengths and limitations of face-to-face teaching when online learning is also available.
3. In the absence of good theory, I have suggested four factors to consider when deciding on mode of delivery, and in particular the different uses of face-to-face and online learning in blended courses:
• student characteristics and needs
• your preferred teaching strategy, in terms of methods and learning outcomes
• the pedagogical and presentational requirements of the subject matter, in terms of (a) content and (b) skills
• the resources available to an instructor (including the instructor’s time).
4. The move to blended or hybrid learning in particular means rethinking the use of the campus and the facilities needed fully to support learning in a hybrid mode. | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/10%3A_Modes_of_delivery/10.5%3A_The_future_of_the_campus.txt |
Figure 11.1.1 ‘I’m just a committed and even stubborn person who wants to see every child getting quality education…’
Malala Yousafzai’s Nobel Prize speech, 2014. Click on image to see the speech.
In recent years, there has been a resurgence of interest in open learning, mainly related to open educational resources and MOOCs. Although in themselves open educational resources (OER) and MOOCs are important developments, they tend to cloud other developments in open education that are likely have even more impact on education as a whole. It is therefore necessary to step back a little to get a broader understanding of not just OER and MOOCs, but open learning in general. This will help us better understand the significance of MOOCs, OER and other developments in open education, and their likely impact on teaching and learning now and in the future.
11.1.1 Open education as a concept
Open education can take a number of forms:
• education for all: free or very low cost school, college or university education available to everyone within a particular jurisdiction, usually funded primarily through the state;
• open access to programs that lead to full, recognised qualifications. These are offered by national open universities or more recently by the OERu;
• open access to courses or programs that are not for formal credit, although it may be possible to acquire badges or certificates for successful completion. MOOCs are a good example;
• open educational resources that instructors or learners can use for free. MIT’s OpenCourseware, which provides free online downloads of MIT’s video recorded lectures and support material, is one example;
• open textbooks, online textbooks that are free for students to use (such as this one);
• open research, whereby research papers are made available online for free downloading (see for instance Open Research Central);
• open data, that is, data open to anyone to use, reuse, and redistribute, subject only, at most, to the requirement to attribute and share; see for example the World Bank’s Open Data Bank;
• open pedagogy, a method of teaching and learning that builds on principles of open-ness and learner participation
Each of these developments is discussed in more detail in this chapter, except for MOOCs, which are discussed extensively in Chapter 5.
11.1.2 Education for all – except higher education
Open education is primarily a goal, or an educational policy. An essential characteristic of open education is the removal of barriers to learning. It can mean no prior qualifications to study, no discrimination by gender, race, age or religion, affordability for everyone, and for students with disabilities, through a determined effort to provide education in a suitable form that overcomes the disability (for example, audio recordings for students who are visually impaired). Ideally, no-one should be denied access to an open educational program. Thus open learning must be scalable as well as flexible.
11.1.2.1 State-funded schools
State-funded public education for the education of children from around the age of five through to sixteen or in some countries eighteen is the most extensive and widespread form of open education. For example, the British government passed the 1870 Education Act that set the framework for schooling of all children between the ages of 5 and 13 in England and Wales. Although there were some fees to be paid by parents, the Act established the principle that education would be paid for mainly through taxes and no child would be excluded for financial reasons. Schools would be administered by elected local school boards (Living Heritage, undated).
Over time, access to publicly funded education in most economically developed countries has been widened to include all children up to the age of 18. UNESCO’s Education for All (EFA) movement is a global commitment to provide quality basic education for all children, youth and adults, supported, at least in principle, by 164 national governments. Nevertheless today there are over 250 million of ‘out-of-school’ children, adolescents and youth worldwide (UNESCO, 2018), or roughly one in five.
11.1.2.2 Post-secondary education
Access to post-secondary or higher education has been more limited than access to schools, partly on financial grounds, but also in terms of ‘merit’. Universities have required those applying for university to meet academic standards determined by prior success in school examinations or institutional entry exams. This has enabled elite universities in particular to be highly selective.
However, after the Second World War, the demand for an educated population, both for social and economic reasons, in most economically advanced countries resulted in the gradual expansion of universities and post-secondary education in general. In most OECD countries, roughly 35-60 per cent of an age cohort will go on to some form of post-secondary education. Especially in a digital age, there is an increasing demand for highly qualified workers, and post-secondary education is a necessary gateway to most of the best jobs. Therefore there is increasing pressure for full and free open access to post-secondary, higher or tertiary education.
11.1.2.3 The cost of widening access
However, as we saw in Chapter 1, the cost of widening access to ever increasing numbers results in increased financial pressure on governments and taxpayers. Following the financial crisis of 2008, many states in the USA found themselves in severe financial difficulties, which resulted in substantial funding cuts to the U.S. higher education system (see for instance, Rivera, 2012), which in turn resulted in a rapid increase in tuition fees.
It is probably more than co-incidental that other forms of open education such as MOOCs and OER arose at a time of increasing cuts to the funding of public education in the USA. Solutions that enable increased access without a proportionate increase in funding or tuition fees are almost desperately being sought by governments and institutions. It is against this background that the renewed interest in open education should be framed.
11.1.3 Open access in higher education
11.1.3.1 Open universities
In the 1970s and 1980s, there was a rapid growth in the number of open universities that required no or minimal prior qualifications for entry. In the United Kingdom, for instance, in 1969, less than 10 per cent of students leaving secondary education went on to university. This was when the British government established the Open University, a distance teaching university open to all, using a combination of specially designed printed texts, and broadcast television and radio, with one week residential summer schools on traditional university campuses for the foundation courses (Perry, 1976; Weinbren, 2015).
The Open University started in 1971 with 25,000 students in the initial entry intake, and now has over 200,000 registered students. It has been consistently ranked by government quality assurance agencies in the top ten U.K. universities for teaching, and in the top 30 for research, and number one for student satisfaction (out of over 180). It currently has over 200,000 registered students (Weinbren, 2015). However, it can no longer cover the full cost of its operation from government grants and there is now a range of different fees to be paid. In addition access to higher education has now widened to the point where 50% of a high school cohort now enter some form of higher education in the UK (UK Department of Education, 2018).
There are now nearly 100 publicly funded open universities around the world, including Canada (Athabasca University and Téluq). These open universities are often very large. The Open University of China has over one million enrolled undergraduate students and 2.4 million junior high school students, Anadolou Open University in Turkey has over 1.2 million enrolled undergraduate students, the Open University of Indonesia (Universitas Terbuka) almost half a million, and the University of South Africa 350,000. These large, degree awarding national open universities provide an invaluable service to millions of students who otherwise would have no access to higher education (see Daniel, 1998, and more recently, Contact North, 2019, for a good overview).
11.1.3.2 Alternatives to open universities
It should be noted however that there is no publicly funded open university in the USA, which is one reason why MOOCs have received so much attention there. The Western Governors’ University is the most similar to an open university, and private, for-profit universities such as the University of Phoenix fill a similar niche in the market.
As well as the national open universities, which usually offer their own degrees, there is also the OERu, which is basically an international consortium of mainly British Commonwealth and U.S. universities and colleges offering open access courses that enable learners either to acquire full credit for transfer into one of the partner universities or to build towards a full degree, offered by the university from which most credits have been acquired. Students pay a fee for assessment.
11.1.4 Limitations of open learning
Open, distance, flexible and online learning are rarely found in their ‘purest’ forms. No teaching system is completely open (minimum levels of literacy are required, for instance). Thus there are always degrees of openness. Openness has particular implications for the use of technology. If no-one is to be denied access, then technologies that are available to everyone need to be used. If an institution is deliberately selective in its students, it has more flexibility with regard to choice of technology. It can for instance require all students who wish to take an online or blended course to have their own computer and Internet access. It cannot do that if its mandate is to be open to all students. Truly open universities then will always be behind the leading edge of educational applications of technology.
Despite the success of many open universities, open universities often lack the status of a campus-based institution. Their degree completion rates are often very low. The U.K. OU’s degree completion rate is 22 per cent (Woodley and Simpson, 2014), but nevertheless still higher for whole degree programs than for most single MOOC courses.
Lastly, some of the open universities have been established for more than 40 years and have not always quickly adapted to changes in technology, partly because of their large size and their substantial prior investment in older technologies such as print and broadcasting, and partly because they do not wish to deny access to potential students who may not have access to the latest technology.
Thus open universities are now increasingly challenged by both an explosion in access to higher education generally, and in the use of online learning by conventional universities. For instance, in Canada, Donovan et. (2018) report that nearly all universities and most colleges are now offering fully online courses (although access is still mainly based on prior qualifications). New developments such as MOOCs, and open educational resources, the topic of the next section, are further challenges for open universities.
References
Contact North (2019) Searchable Directory of More than 65 Open Universities Worldwide Sudbury ON: Teachonline.ca
Daniel, J. (1998) Mega-Universities and Knowledge Media: Technology Strategies for Higher Education. London: Kogan Page
Donovan, T. et al. (2018) Tracking Online and Distance Education in Canadian Universities and Colleges: 2018 Halifax NS: Canadian Digital Learning Research Association
Living Heritage (undated) Going to school: the 1870 Education Act, London: UK Parliament
Perry, W. (1976) The Open University Milton Keynes: Open University Press
Rivera, C. (2012) Survey offers dire picture of California’s two-year colleges Los Angeles Times, August 28
U.K. Department of Education (2018) Participation Rates in Higher Education: Academic Years 2006/2007 – 2017/2018 (Provisional) London: Department of Education HE Statistics
UNESCO (2014) Education for All, 2000-2015: achievements and challenges Paris FR: The UNESCO 2015 EFA Global Monitoring Report team
UNESCO (2018) One in Five Children, Adolescents and Youth is Out of School Paris FR: UNESCO Institute for Statistics Fact Sheet No 42
Weinbren, D. (2015) The Open University: A History Manchester UK: Manchester University Press/The Open University
Woodley, A. and Simpson, O. (2014) ‘Student drop-out: the elephant in the room’ in Zawacki-Richter, O. and Anderson, T. (eds.) (2014) Online Distance Education: Towards a Research Agenda Athabasca AB: AU Press, pp. 508
Activity 11.1 Should access to post-secondary education be completely open to anyone?
1. Should access to post-secondary or higher education be open to everyone?
If yes, what are reasonable limitations on this principle?
What should be the government’s role, if any, in making this possible?
If your answer is no to the first part of this question, why should education up to post-secondary education be open, but not afterwards? Is it simply money, or are there other reasons?
2. Are open universities still relevant in a digital age? | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/11%3A_Trends_in_open_education/11.1%3A_Open_learning.txt |
Figure 11.H The Hart River, Yukon.
Image: © www.protectpeel.ca, CC BY-NC
Over a number of years, research faculty in the Departments of Land Management and Forestry at the University of Western Canada had developed a range of digital graphics, computer models and simulations about watershed management, partly as a consequence of research conducted by faculty, and partly to generate support and funding for further research.
At a faculty meeting several years ago, after a somewhat heated discussion, faculty members voted, by a fairly small majority, to make these educational resources openly available for re-use for educational purposes under a Creative Commons license that requires attribution and prevents commercial use without specific written permission from the copyright holders, the faculty responsible for developing the artefacts.
What swayed the vote is that the majority of the faculty actively involved in the research wanted to make these resources more widely available. The agencies responsible for funding the work that resulted in the development of the learning artefacts (mainly national research councils) welcomed the move to makes these artefacts more widely available as open educational resources.
Initially, the researchers just put the graphics and simulations up on the research group’s web site. It was left to individual faculty members to decide whether to use these resources in their teaching. Over time, faculty started to introduce these resources into a range of on-campus undergraduate and graduate courses.
After a while, though, word seemed to get out about these OER. Research members began to receive e-mails and phone calls from other researchers around the world. It became clear that there was a network or community of researchers in this field who were creating digital materials as a result of their research, and it made sense to share and re-use materials from other sites. This eventually led to an international web ‘portal’ of learning artefacts on watershed management.
The researchers also started to get calls from a range of different agencies, from government ministries or departments of environment, local environmental groups, First Nations/aboriginal bands, and, occasionally, major mining or resource extraction companies, leading to some major consultancy work for the faculty in the departments. At the same time, the faculty were able to attract further research funding from non-governmental agencies such as the Nature Conservancy and some ecological groups, as well as from their traditional funding source, the national research councils, to develop more OER.
By this time, the departments had access to a fairly large amount of OER. There were already two fourth and fifth level fully online courses built around the OER that were being offered successfully to undergraduate and graduate students. A proposal was therefore put forward to create initially a fully online post-graduate certificate program on watershed management, built around existing OER, in partnership with a university in the USA and another one in Sierra Leone. This certificate program was to be self-funding from tuition fees, with the tuition fees for the 25 Sierra Leone students to be initially covered by an international aid agency.
The Dean, after a period of hard negotiation, persuaded the university administration that the departments’ proportion of the tuition fees from the certificate program should go directly to the departments, who would hire additional tenured faculty from the revenues to teach or backfill for the certificate, and the departments would pay 25 per cent of the tuition revenues to the university as overheads. This decision was made somewhat easier by a fairly substantial grant from Foreign Affairs Canada to make the certificate program available in English and French to Canadian mining and resource extraction companies with contracts and partners in African countries.
Although the certificate program was very successful in attracting students from North America, Europe and New Zealand, it was not taken up very well in Africa beyond the partnership with the university in Sierra Leone, although there was a lot of interest in the OER and the issues raised in the certificate courses. After two years of running the certificate, then, the departments made two major decisions:
• another three courses and a research project would be added to the certificate courses, and this would be offered as a fully cost recoverable online master in watershed resource management. This would attract greater participation from managers and professionals in African countries in particular, and provide a recognised qualification that many of the certificate students were requesting;
• drawing on the very large network of external experts now involved one way or another with the researchers, the university would offer a series of MOOCs on watershed management issues, with volunteer experts from outside the university being invited to participate and provide leadership in the MOOCs. The MOOCs would be able to draw on the existing OER.
Five years later, the following outcomes were recorded by the Dean at an international conference on sustainability:
• the online master’s program had doubled the total number of graduate students in her Faculty;
• the master’s program was fully cost-recoverable from tuition fees;
• there were 120 graduates a year from the master’s program;
• the degree completion rate was 64 per cent;
• six new tenured faculty had been hired, plus another six post-doctoral research staff;
• several thousand students had registered and paid for at least one course in the certificate or master’s program, of which 45 per cent were from outside Canada;
• over 100,000 students had taken the MOOCs, almost half from developing countries;
• there were now over 1,000 hours of OER on watershed management available and downloaded many times across the world;
• the university was now internationally recognised as a world leader in watershed management.
Although this scenario is purely a figment of my imagination, it is influenced by real and exciting work being done by the following at the University of British Columbia: | textbooks/socialsci/Education_and_Professional_Development/Teaching_in_a_Digital_Age_2e_(Bates)/11%3A_Trends_in_open_education/11.1%3A_Scenario_H%3A_Watershed_management.txt |
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