## 3 Materials and Methodology
### Evaluation Metrics  
All processes need to be evaluated, both the new generated datasets and the short answer grading systems. The evaluation metrics that are commonly used to measure system performance can be seen in the following points.  
1. Accuracy  
Proportion of correctly graded answers. Accuracy is a straightforward metric; it is essential to consider its limitations, especially in scenarios with imbalanced datasets. In imbalanced datasets, where one class significantly outweighs the other, accuracy can be misleading. To address the limitations of accuracy, we use other evaluation metrics such as precision, recall, and F1-score [25].  
2. F1-Score  
The F1-score is a weighted comparison of average precision and recall. The formula for the F1-score is as follows:  
\[F1=\frac{2\times Precision\times Recall}{Precision+Recall}, \tag{1}\]  
The F1-score pays attention to the model's ability to handle imbalanced data classes. In addition, by using the F1-score as one of the evaluation metrics, we can compare the resulting model with other published models.  
3. Pearson Correlation Score  
Pearson's correlation is the most commonly used method in statistics to evaluate the strength and presence of a linear relationship between predicted and manual grades. [PERSON]'s correlation coefficient is the covariance of the two variables divided by the product of their standard deviations [20].  
4. Root Mean Square Error (RMSE)  
Root mean square error or root mean square deviation is one of the most commonly used measures for evaluating the quality of predictions. It shows how far predictions fall from the measured true values using Euclidean distance. The use of RMSE is very common, and it is considered an excellent general-purpose error metric for numerical predictions. RMSE is calculated as follows:  
\[RMSE=\sqrt{MSE}, \tag{2}\]  
Meanwhile, mean square error (MSE) measures the square of differences between predictions and target values and computes the mean of them. MSE is calculated as follows:  
\[MSE=\frac{1}{N}\sum_{i=1}^{N}{(y_{i}-\hat{y}_{i})}^{2} \tag{3}\]  
Figure 5: Proposed data augmentation process.  
Meteor  
METEOR stands for Metric for Evaluation of Translation with Explicit Ordering; it is known for its higher correlation with human judgment, especially at the sentence level. These metrics always take a value between 0 and 1. This value indicates how similar the predicted text is to the reference texts, with values closer to 1 representing more-similar texts. METEOR's ability to measure the quality of the generated answer is based on unigram precision and recall. It significantly improves the correlation with human judgments. METEOR computes the similarity score of two texts by using a combination of unigram precision, unigram recall, and some additional measures like stemming and synonymy matching [21].  
The cosine similarity technique is used for measuring the similarity between two vectors. The way it works is by measuring the cosine of the angle between two documents which are expressed in vectors. [PERSON] et al. stated that the angle between vectors determines whether they are pointing in the same or different directions. If vectors are pointing in the same direction, it means that the documents are similar; the closer they are expressed on the axis, the more similar they are. Vice versa, the farther they are expressed on the axis, the less similar they are [26].