## 4 Result and Discussion
### Additional Experiments  
We conducted additional experiments to determine whether our proposed method also improves the performance of the ASAG system on other datasets. We utilized the SemEval-2013 dataset, a benchmark dataset from the SemEval-2013 Shared Task 7 [28]. Specifically, we used the two-way SciEnts Bank subset, which includes two grade labels: \"correct\" as grade label 1 and \"incorrect\" as grade label 0. This dataset consists of questions, desired answers, student answers, and two-way grade labels in the science domain.  
Figure 12: Average running time of each experiment.  
We used both the original dataset and an augmented version. The initial dataset contained 4925 rows, with 2944 rows (60%) labeled as grade 0 and 1981 rows (40%) labeled as grade 1. We applied the same augmentation process to this dataset. Using prompt engineering in GPT models, we generated additional answers for grade label 0 by using antonyms of the desired answer and synonyms of words from the student answers to create new answers for grade label 1. Figure 13 illustrates the data distribution for both the original and the balanced datasets after augmentation with the GPT-3.5 and GPT-4.0 models. The blue bar represents data with a grade label of 0, while the orange bar represents data with a grade label of 1.  
We also evaluated the newly generated dataset using the METEOR score and cosine similarity score. The evaluation results are presented in Table 7, which shows that the dataset generated using the GPT-3.5 model performed better. These datasets were also implemented using the recommended models from the experiments in the previous section: the all-distilroberta-v1 and multi-qa-mpnet-base-dot-v1 models.  
From the five scenarios mentioned in Table 5, we only present the best two results for this additional experiment, using the same combination of hyperparameters. The results are displayed in Table 8.  
Our best results from these additional experiments are also highlighted in green in the respective column. These results indicate that the augmentation process successfully increased system performance. Moreover, the results labeled in blue indicate experiments using larger models. The larger models achieved better scores in F1-score, accuracy, and Pearson correlation but, as with the previous dataset, the performance improvement was not significant compared to the average running time. When compared with previous research using the same dataset, our proposed method also improves performance quite well.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Augmentation Model** & **Grade Category** & **Cosine Similarity** & **METEOR** \\ \hline GPT-3.5 & False & 0.4411 & 0.5921 \\ GPT-3.5 & Correct & 0.7121 & 0.5498 \\ GPT-4 & False & 0.4429 & \\ GPT-4 & Correct & 0.7059 & \\ \hline \hline \end{tabular}
\end{table}
Table 7: SciEnts Bank data augmentation results evaluation.  
Figure 13: SciEnts Bank datasets data distribution.  
When compared with the Mohler dataset, the improvement in experiments with the SciEnts Bank dataset was not very significant. Based on further observations, we found that the Mohler dataset had an average of 20 words per row of data, while the SciEnts Bank dataset had an average of only 12 words per row of data. This difference in word count may contribute to the less significant performance improvement, as fewer words were considered in the evaluation.