# TSA-GRU: A Novel Hybrid Deep Learning Module for Learner Behavior Analytics in MOOCs
## 4 Experimental Results and Comparaison
### TSA-GRU Performance Across Epochs and with Other Models
#### 4.4.1 TSA-GRU Performance Across Epochs  
Model Performance Across Five Epochs  
Figure 4 showed that the TSA-GRU model registered significant improvement in performance during the five training epochs, gradually fine-tuning its predictions with little loss and incremental accuracy gains. Most of its training performance settled down at a training loss of about 0.0302 (95.69% accuracy) and a test loss of 0.0211 (95.96% accuracy). This indicates that TSA-GRU was able to reasonably correctly capture sequential patterns in student interactions and, based on that, quickly learned from emerging data to achieve peak performance. Figure 3: Epoch-wise performance analysis of TSA-Transformer. Figure 2: Layer-wise architecture and hyperparameter details of TSA-GRU. Model Performance Across 10 Epochs  
Through 10 epochs in Figure 5, TSA-GRU showed a smoothly decreasing loss curve for both training and test loss, indicating its high capability of learning and generalizing. The training loss started at around 0.0319 and finally reached equilibrium at around 0.0298. The test loss reduced from around 0.0283 to 0.0210. This demonstrates the potential of the model to capture useful features from sequences of student learning. There was a small improvement in accuracy (tolerance equal 0.2); the training accuracy was improved from about 94.02% to 94.68% at the end, while the testing accuracy was improved from about 93.83% to 94.68%. These results show the capability of TSA-GRU in modeling complex layer-wise sequential dependencies and achieving strong predictive performance. Model Performance Across 20 Epochs  
The TSA-GRU model was trained for 20 epochs; both the training and test loss curves, as shown in Figure 6, show a tendency to decrease, demonstrating the great capability for learning and generalization in this network. The initial value for training loss is about 0.0309; 0.0210 is the value for test loss. Over the entire period of training, minor variations notwithstanding, the model stabilized at the end to a training loss of 0.0293 and a test loss of 0.0205. Regarding the accuracy tolerance at 0.2, the training accuracy reached 95.39%, whereas the test accuracy was 95.18%. These results demonstrate that TSA-GRU captures intricate sequential patterns of student learning data very well, thus justifying its capability for sound knowledge tracing operations. Figure 4: Accuracy and loss curves with 5 epochs. Figure 5: Accuracy and loss curves with 10 epochs. In Figure 7, the performance of the TSA-GRU model after 30 epochs of training is established. A steady decrease in training and testing loss confers stable and strong performance. The training loss starts from approximately 0.0324 and decreases to about 0.0295, whereas the test loss drops from around 0.0221 to 0.0205, with many bumps during the training phase. In terms of accuracy, the TSA-GRU model scored quite high: training accuracy stabilizing around 95.78% and test accuracy settling at 95.67%. The results suggest that TSA-GRU, among other models, best captures the learning patterns of students and generalizes better across various knowledge tracing tasks, while the insignificant ups and downs indicate it remains sensitive to the variations in the data and could be adjusted further upon tuning. Model Performance Across 40 Epochs  
The TSA-GRU model's training is remarkably consistent over 40 epochs, as shown in Figure 8, as both training and test losses continue their steady decline, which is a clear indicator of learning and generalization. Training loss dropped from 0.0316 to 0.0288, while test loss had dropped from 0.0313 to 0.0201 by the last epoch. In tandem, the training accuracy increased from 93.79% to 95.60% and the testing accuracy rose from 93.76% to 95.74%. Such observations verified that the TSA-GRU model could adequately seize the essential patterns that established the sequences of students' learning and is both resilient and consistently applicable to various knowledge tracing problems while accommodating slight training perturbations.