## 4 Result and Discussion
### Dataset Balancing by Data Augmentation with GPT  
Based on the previously explained scenario, the process of balancing the dataset with data augmentation will use a parameter value of temperature = 0.7 and will also utilize both the GPT-3.5 and GPT-4 models. For grade label = 0, the prompt text used will be \"Please make a completely different sentence from this following sentence: '(answer)' so it counts as an opposite sentence\" to get the opposite or antonyms. Meanwhile, for other grade labels, the prompt text used will be \"Please paraphrase the following sentence '(answer)' \" to get similar sentences or synonyms. Figure 6 shows the new dataset, with additional data generated by the GPT-3.5 model. Subsequently, the results of this process will be referred to as BalMohler-3.5, which means Balanced Mohler dataset with GPT-3.5. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model Name**} & **Gradient** & **Split** & \multirow{2}{*}{**RMSE**} & \multirow{2}{*}{**F1-Score**} & \multirow{2}{*}{**Accuracy**} & **Pearson** \\  & **Checkpointing** & & & & & **Correlation** \\ \hline \multirow{2}{*}{bert-base-uncased} & & & 1.61902 & 0.19927 & 0.22287 & 0.21246 \\  & ssh-distilbert-base & & & 1.04117 & 0.65803 & 0.66826 & 0.63649 \\  & all-distilroberta-v1 & & & 1.0607 & 0.66809 & 0.64516 & 0.66427 \\  & all-MiniLM-L6-v2 & & & 0.09158 & 0.60616 & 0.64367 & 0.64367 \\  & mul-ti-qa-mptnet-base-dot-v1 & & & 0.89391 & 0.70164 & 0.70164 & 0.45196 \\  & mul-qa-distilvert-cos-v1 & & & 1.08879 & 0.63096 & 0.64474 & 0.47758 \\  & paraphrase-albert-small-v2 & & & 1.31466 & 0.58836 & 0.58333 & 0.43399 \\  & all-MiniLM-L12-v2 & & & 0.99299 & 0.63995 & 0.65351 & 0.43184 \\ \hline \multirow{2}{*}{bert-base-uncased} & & & 1.91571 & 0.42417 & 0.40351 & 0.25366 \\  & ssh-distilbert-base & & & 1.31803 & 0.67083 & 0.67105 & 0.59558 \\  & all-distilroberta-v1 & & & 1.24106 & 0.73221 & 0.72807 & 0.65142 \\  & all-MiniLM-L6-v2 & & & 1.108978 & 0.58192 & 0.56579 & 0.64576 \\  & mul-qa-mpnet-base-dot-v1 & & & 1.122907 & 0.68833 & 0.68859 & 0.76269 \\  & mul-ti-qa-distilvert-cos-v1 & & & 1.4047 & 0.67267 & 0.67105 & 0.71517 \\  & paraphrase-albert-small-v2 & & & 1.79938 & 0.50837 & 0.49123 & 0.62883 \\  & all-MiniLM-L12-v2 & & & 1.15167 & 0.63541 & 0.64474 & 0.71271 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Model performance for original dataset. Figure 7 depicts the new dataset with added data resulting from data augmentation using GPT-4. Compared to Figure 3, this new dataset has more evenly distributed numbers. The standard deviation for the original Mohler dataset is 400, while the standard deviation for the new dataset is around 98 for both models. This dataset will be referred to as BalMohler-4. In addition to the distribution of data, we will also evaluate the results of these new generated sentences based on METEOR and cosine similarity scores. To facilitate evaluation, we will categorize grade labels into two categories: the correct category for grade labels 2-5 and the false category for grade labels 0-1. The grade labels 0-1 are considered as the false category because they represent answers that are far from the correct answer. Table 4 shows the evaluation results of data augmentation from both models. Figure 6: New generated dataset using GPT-3.5. Figure 7: New generated dataset using GPT-4. These new generated sentences will be considered as student answers and will be compared with the desired answers for evaluation. So, a smaller value in the cosine similarity score indicates better results for the false category. Meanwhile, for the correct category, a larger value indicates better performance. The new sentences of the GPT-4 model are better, although there is only a small increase. Meanwhile, the METEOR score reflects the overall text quality, where a higher score indicates better quality. As seen in Table 4, the results of the new generated sentences with the GPT-3.5 model show a better performance, although the difference is not significant compared to the results from the GPT-4 model.