Automatic Short Answer Grading System in Indonesian Language Using BERT Machine Learning
Why this work is in the frame
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Bibliographic record
Abstract
A system capable of automatically grading short answers is a very useful tool. The system can be created using machine learning algorithms. In this study, a machine system using BERT is proposed. BERT is an open-source system that is set to English by default. The use of languages other than English Language is a challenge to be implemented in BERT. This study proposes a novel system to implement Indonesian Language in the BERT system for automatic grading of short answers. The experimental results were measured using two measuring instruments: Cohen's Kappa coefficient and the Confusion Matrix. The result of measuring the BERT output of the implemented system has a Cohen Kappa coefficient of 0.75, a precision of 0.94, a recall of 0.96, a Specificity of 0.76 and an F1 Score of 0.95. Based on the measurement results, it can be seen that the implementation of the automatic short answer grading system in Indonesian Language using BERT machine learning has been successful.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it