README.md

---
language: 
- ind
pretty_name: Id Short Answer Grading
task_categories: 
- short-answer-grading
tags: 
- short-answer-grading
---

Indonesian short answers for Biology and Geography subjects from 534 respondents where the answer grading was done by 7 experts.

## Languages

ind

## Supported Tasks

Short Answer Grading

## Dataset Usage
### Using `datasets` library
```
from datasets import load_dataset
dset = datasets.load_dataset("SEACrowd/id_short_answer_grading", trust_remote_code=True)
```
### Using `seacrowd` library
```import seacrowd as sc
# Load the dataset using the default config
dset = sc.load_dataset("id_short_answer_grading", schema="seacrowd")
# Check all available subsets (config names) of the dataset
print(sc.available_config_names("id_short_answer_grading"))
# Load the dataset using a specific config
dset = sc.load_dataset_by_config_name(config_name="<config_name>")
```

More details on how to load the `seacrowd` library can be found [here](https://github.com/SEACrowd/seacrowd-datahub?tab=readme-ov-file#how-to-use).


## Dataset Homepage

[https://github.com/AgeMagi/tugas-akhir](https://github.com/AgeMagi/tugas-akhir)

## Dataset Version

Source: 1.0.0. SEACrowd: 2024.06.20.

## Dataset License

Unknown

## Citation

If you are using the **Id Short Answer Grading** dataloader in your work, please cite the following:
```
@article{
    JLK,
    author = {Muh Haidir and Ayu Purwarianti},
    title = { Short Answer Grading Using Contextual Word Embedding and Linear Regression},
    journal = {Jurnal Linguistik Komputasional},
    volume = {3},
    number = {2},
    year = {2020},
    keywords = {},
    abstract = {Abstract—One of the obstacles in an efficient MOOC is the evaluation of student answers, including the short answer grading which requires large effort from instructors to conduct it manually.
                Thus, NLP research in short answer grading has been conducted in order to support the automation, using several techniques such as rule
                and machine learning based. Here, we’ve conducted experiments on deep learning based short answer grading to compare the answer
                representation and answer assessment method. In the answer representation, we compared word embedding and sentence embedding models
                such as BERT, and its modification. In the answer assessment method, we use linear regression. There are 2 datasets that we used, available
                English short answer grading dataset with 80 questions and 2442 to get the best configuration for model and Indonesian short answer grading
                dataset with 36 questions and 9165 short answers as testing data. Here, we’ve collected Indonesian short answers for Biology and Geography
                subjects from 534 respondents where the answer grading was done by 7 experts. The best root mean squared error for both dataset was achieved
                by using BERT pretrained, 0.880 for English dataset dan 1.893 for Indonesian dataset.},
    issn = {2621-9336},	pages = {54--61},	doi = {10.26418/jlk.v3i2.38},
    url = {https://inacl.id/journal/index.php/jlk/article/view/38}
}

@article{lovenia2024seacrowd,
    title={SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages}, 
    author={Holy Lovenia and Rahmad Mahendra and Salsabil Maulana Akbar and Lester James V. Miranda and Jennifer Santoso and Elyanah Aco and Akhdan Fadhilah and Jonibek Mansurov and Joseph Marvin Imperial and Onno P. Kampman and Joel Ruben Antony Moniz and Muhammad Ravi Shulthan Habibi and Frederikus Hudi and Railey Montalan and Ryan Ignatius and Joanito Agili Lopo and William Nixon and Börje F. Karlsson and James Jaya and Ryandito Diandaru and Yuze Gao and Patrick Amadeus and Bin Wang and Jan Christian Blaise Cruz and Chenxi Whitehouse and Ivan Halim Parmonangan and Maria Khelli and Wenyu Zhang and Lucky Susanto and Reynard Adha Ryanda and Sonny Lazuardi Hermawan and Dan John Velasco and Muhammad Dehan Al Kautsar and Willy Fitra Hendria and Yasmin Moslem and Noah Flynn and Muhammad Farid Adilazuarda and Haochen Li and Johanes Lee and R. Damanhuri and Shuo Sun and Muhammad Reza Qorib and Amirbek Djanibekov and Wei Qi Leong and Quyet V. Do and Niklas Muennighoff and Tanrada Pansuwan and Ilham Firdausi Putra and Yan Xu and Ngee Chia Tai and Ayu Purwarianti and Sebastian Ruder and William Tjhi and Peerat Limkonchotiwat and Alham Fikri Aji and Sedrick Keh and Genta Indra Winata and Ruochen Zhang and Fajri Koto and Zheng-Xin Yong and Samuel Cahyawijaya},
    year={2024},
    eprint={2406.10118},
    journal={arXiv preprint arXiv: 2406.10118}
}

```