## 5 Experiments
### Results and the Proposed Model Hyperparameters
This simultaneous reduction in error rates during training indicates that the GRU model effectively avoids the issue of overfitting, showcasing its ability to generalize well to new, unseen data while improving its accuracy on the training data over time. Additionally, the accuracy of the student performance predictions is graphically depicted in Figure 8. This demonstrates that the proposed GRU model has been effectively trained. There is a noticeable increase in accuracy for both the training and testing phases \begin{table}
\begin{tabular}{c c c} \hline \hline
**No. of Parameters** & **Output Shape** & **Layer (Type)** \\ \hline
0 & (None, 10, 1) & Input\_1 (inputLayer) \\ \hline
200 & (None, 10, 100) & Word\_dense (Dense) \\ \hline
274,944 & (None, 10, 256) & Gru (GRU) \\ \hline
0 & (None, 256) & Global\_max\_pooling (Global MaxpoolingID) \\ \hline
514 & (None, 2) & Dense \\ \hline \multicolumn{3}{c}{Total Parameters: 275,658} \\ \hline \multicolumn{3}{c}{Trainable Parameters: 275,658} \\ \hline \multicolumn{3}{c}{Non-Trainable Parameters: 275,658} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of the proposed GRU model. Figure 8: Accuracy analysis for the proposed GRU model. Figure 9: Relationship between model loss (error) with epoch during the training and testing of the model using the educational dataset. within educational datasets, starting from epoch number 1 and continuing up to epoch number 4. This upward trend in accuracy highlights the GRU model's ability to perform and classify with precision. Moreover, an additional metric was employed to evaluate the performance of the proposed GRU model, as depicted in Figure 10 through a confusion matrix. This matrix effectively highlights the number of true positives, accurately predicted and correctly classified samples, alongside true negatives, which were correctly identified as belonging to the alternate class in the context of student performance classification. According to Figure 10, the model successfully identified 2885 samples as true positives and 110 samples as true negatives. Conversely, the confusion matrix also reveals instances of incorrect predictions, classified as false positives and false negatives. Specifically, the model incorrectly classified 38 samples as false positives, while no instances were recorded as false negatives. The data presented in Figure 10 underscores the model's high accuracy and proficiency in predicting student assessments, with a minimal error margin. During the development and evaluation of the proposed GRU model for predicting student academic performance, the research team encountered several challenges, including the following:  
* The complexity of educational data, characterized by large datasets with missing entries, necessitated meticulous preprocessing to maintain data integrity. * The risk of overfitting was significant due to the model's complexity. Strategies like dropout and early stopping were implemented to mitigate this risk. * Optimizing the model involved intricate parameter tuning to identify the ideal learning rates and layer configurations amidst a vast parameter space. * The training process demanded substantial computational resources to manage the extensive dataset and intricate model architecture efficiently. * Ensuring the model's generalization capability across different educational institutions required thorough testing and validation to confirm its efficacy on unseen data. These challenges were systematically addressed through targeted data preprocessing, regularization techniques, exhaustive hyperparameter optimization, the strategic allocation of computational resources, and rigorous validation procedures to enhance the model's performance and applicability. Furthermore, the GRU model offers a deeper analysis and insights into the educational dataset.