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README.md

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+---
+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)