language:ind license:unknown short-answer-grading region:us
Indonesian short answers for Biology and Geography subjects from 534 respondents where the answer grading was done by 7 experts.\
@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}
}\
---
license: unknown
tags:
- short-answer-grading
language:
- ind
---

# id_short_answer_grading

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

## Dataset Usage

Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`.

## Citation

```
@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}
}
```

## License

Unknown

## Homepage

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

### NusaCatalogue

For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue)