Combining Balancing Dataset and SentenceTransformers to Improve Short Answer Grading Performance

Short-answer questions can encourage students to express their understanding. However, these answers can vary widely, leading to subjective assessments. Automatic short answer grading (ASAG) has become an important field of research. Recent studies have demonstrated a good performance using computationally expensive models. Additionally, available datasets are often unbalanced in terms of quantity. This research attempts to combine a simpler SentenceTransformers model with a balanced dataset, using prompt engineering in GPT to generate new sentences. Our recommended model also tries to fine-tune several hyperparameters to achieve optimal results. The research results show that the relatively small-sized all-distilroberta-v1 model can achieve a Pearson correlation value of 0.9586. The RMSE, F1-score, and accuracy score also provide better performances. This model is combined with the fine-tuning of hyperparameters, such as the use of gradient checkpointing, the split-size ratio for testing and training data, and the pre-processing steps. The best result is obtained when the new generated dataset from the GPT data augmentation is implemented. The newly generated dataset from GPT data augmentation achieves a cosine similarity score of 0.8 for the correct category. When applied to other datasets, our proposed method also shows an improved performance. Therefore, we conclude that a relatively small-sized model combined with the fine-tuning of the appropriate hyperparameters and a balanced dataset can provide performance results that surpass other models that require larger resources and are computationally expensive.