## 5 Experiments
### Results and the Proposed Model Hyperparameters  
The GRU classifier model was developed using an educational dataset for both its training and testing phases. This model incorporated a sequential architecture featuring a max-pooling layer, a dense layer, and utilized the ADAM optimizer to enhance its performance. The chosen loss function for the model was binary cross entropy, suitable for binary classification tasks. For validating the model's effectiveness, the K-fold cross-validation method was employed, specifically with a single fold (k = 1), effectively creating a straightforward training/test split. The model's architecture included a Fully Connected Neural Network (FCNN) layer with 100 neurons and nine input variables, adopting the ReLU activation function for non-linear processing. The design also incorporated two hidden layers: the initial layer being a GRU layer equipped with 256 units and a recurrent dropout of 0.23 to mitigate overfitting, and the subsequent layer, a one-dimensional global max-pooling layer for feature down-sampling. The output layer activated by a sigmoid function reflects the binary nature of the dataset's classification challenge. The implementation was carried out using Keras and Python, harnessing the ADAM optimizer's capabilities with a learning rate set at 0.01 and a momentum of 0.0, aiming for efficient training dynamics. The model's training was configured with a batch size of 90 and planned for 300 epochs, although an early stopping mechanism was introduced after just 7 epochs to prevent overfitting, with a patience setting of 2 epochs. Initially, the model comprised 275,658 parameters, highlighting its complexity and capacity for learning. Regarding the classification task, the model demonstrated a requirement of approximately 16 s per epoch, with each epoch involving a random shuffle of the training data to ensure varied exposure. The overarching goal in training this model was to minimize validation loss as measured using binary cross entropy, indicating a focused effort on enhancing the predictive accuracy for student assessments. Extensive testing and experimentation were conducted to fine-tune the proposed model, involving various configurations and hyperparameter adjustments to achieve optimal performance. This effort was aimed at accurately predicting student assessments within educational settings. The effectiveness of the model, as detailed in Figure 8, is evidenced by its high accuracy scores for the prediction task. The data presented in Figure 9 highlights the model's capability in accurately forecasting student assessments, particularly noting the significant impact of integrating the GRU layer and a fully connected neural network. Specifically, the model attained an impressive accuracy rate of 99.70%, showcasing its precision in evaluation predictions. The inclusion of a global max-pooling layer played a crucial role in bolstering the model's predictive accuracy concerning student evaluations. When compared to existing models documented in the literature, this model demonstrated a superior performance. For example, it outpaced an RNN model, which recorded an accuracy rate of 95.34%, a discrepancy attributed to the RNN's challenges with vanishing gradients, as indicated in Table 2. Additionally, the model showcased an enhanced performance compared with the ARD V.2 and AdaBoost models, which achieved accuracy rates of 93.18% and 94.57%, respectively. The successful application of GRU alongside max-pooling layers over the neural network layer underscores the model's comprehensive capability and effectiveness in autonomously predicting student assessments. Figure 9 offers a glimpse into the model's experimental evaluation for predicting student performance, while Table 2 consolidates the advantages offered by the GRU model in this context. Figure 9 illustrates the prediction model's error rates throughout the simulation process, demonstrating a consistent decrease in error for both the training and actual validation datasets as the learning progressed.