Update README.md

README.md

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 # Dataset Documentation
 
 ## Overview
-The `students-coding-questions-from-ai-assistant.csv` dataset contains 6776 questions asked by students from CodeAid, an AI coding assistant, during a C programming class over a 12-week semester from January to April 2023. The students had previously taken two Python programming courses. The course did not allow the use of ChatGPT, but CodeAid was permitted. CodeAid, powered by GPT-3, did not directly disclose code solutions even when requested by students. Instead, it functioned like a teaching assistant, providing scaffolded responses in natural language, generating interactive pseudo-codes, and suggesting fixes without directly showing the corrected code.
+This dataset contains 6776 questions asked by students from CodeAid, an AI coding assistant, during a C programming class over a 12-week semester from January to April 2023. The course did not allow the use of ChatGPT, but CodeAid was permitted. CodeAid, powered by GPT-3, did not directly disclose code solutions even when requested by students. Instead, it functioned like a teaching assistant, providing scaffolded responses in natural language, generating interactive pseudo-codes, and suggesting fixes without directly showing the corrected code.
 
 ## Dataset Use Cases
 1. **Query Classification**: The dataset can be used to develop models that classify queries into categories explained in the paper such as the ones we identified in the thematic analysis of our CHI'24 paper: **Code and Conceptual Clarification**, **Function-Specific Queries**, **Code Execution Probes**, **Buggy Code Resolution**, **Problem Source Identification**, **Error Message Interpretation**, **High-level Coding Guidance**, and **Direct Code Requests**.
@@ -28,8 +28,8 @@ The dataset includes the following columns:
 - **user_id**: Unique identifier for the user. Type: String.
 - **time**: Timestamp of when the event occurred. Format: ISO 8601, Type: DateTime.
 - **feature_type**: Describes the type of feature the row represents, e.g., 'Question from Code'. Type: String.
-- **feature_version**: Version of the feature. Type: String. Values: 'v1' (used `code-davinci-002`), 'v2' (used `gpt-3.5-turbo-1106`)
-- **input_question**: The question input by the user. This column exists when `feature_type` is either `"General Question"`, or `"Question from Code".
+- **feature_version**: Version of the feature. Type: String. Values: `"v1"` (used `code-davinci-002`), `"v2"` (used `gpt-3.5-turbo-1106`)
+- **input_question**: The question input by the user. This column exists when `feature_type` is either `"General Question"`, or `"Question from Code"`.
 - **input_code**: The code snippet provided by the user. This column exists when `feature_type` is either `"General Question"`, `"Question from Code"`,  `"Help Fix Code"`, or `"Explain Code"`.
 - **input_intention**: A brief description of what the user intends to achieve with the code. This column exists when `feature_type` is `"Help Fix Code"`. 
 - **input_task_description**: Detailed description of the task for which the code is written. This column exists when `feature_type` is `"Help Write Code"`.