# 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
#### 4.4.2 Comparison of Knowledge Tracing Models  
To evaluate the performance of the method described above, we compared training and testing performance across several metrics (training loss, training accuracy, test loss, and test accuracy). The results shown in Table 3 provide strong evidence that modern attention-based architectures (TSA-GRU and MSA-GRU) are generally more accurate than classical models (BKT and PFA), with notably lower test losses. Additionally, the TSA-GRU model achieved the highest test accuracy, as well as the lowest test loss, which provides further evidence of its ability to better model complex student behavior due to the nature and capabilities of attention-based architectures. Overall, these results provide strong evidence that the TSA-GRU model (and other similar attention-enhanced architectures) is capable of producing better multiple-choice predictions than traditional models in the task of knowledge tracing. We conducted a controlled experiment comparing TSA-GRU, MSA-GRU, BKT, and Performance Factor Analysis (PFA) using the 2009-2010 ASSISTments dataset under identical experimental conditions. TSA-GRU achieved the best results, with a test accuracy of 95.60% and a test loss of 0.0209. MSA-GRU vs. TSA-GRU  
Although Modular Sparse Attention with GRU is capable of computing a certain range of dependencies at a much reduced computational overhead, it lacks efficiency in handling the very complicated long-range dependencies necessary for learning trajectories. In contrast, TSA-GRU combines TSA and GRU as its core for capturing richer contextual insights from all student interactions, as Figure 10 demonstrates. Our results substantiate this difference. MSA-GRU, had a training loss of 0.0667, a training accuracy of 81.36%, a testing loss of 0.0222, and a testing accuracy of 80.00%. In contrast, the TSA-GRU recorded a training loss of just 0.0296 and a greater training accuracy of 95.62% along with a test loss of 0.0209 and test accuracy of 95.60%. The analysis also illustrates that TSA-GRU not only exhibits better performance in predictive capacity, but has also shown its competence in deciphering complex sequential behaviors from educational data--hence making it an optimal choice for advanced knowledge tracing challenges. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Train Loss** & **Train Accuracy** & **Test Loss** & **Test Accuracy** \\ \hline MSA-GRU & 0.0667 & 81.36\% & 0.0222 & 80.00\% \\ BKT & 0.6651 & 64.10\% & 0.6691 & 64.01\% \\ PFA & 0.6334 & 63.63\% & 0.6363 & 64.11\% \\ TSA & 0.0678 & 75.06\% & 0.0238 & 74.83\% \\ TSA-GRU & 0.0296 & 95.62\% & 0.0209 & 95.60\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative analysis of knowledge tracing methods. BKT vs. TSA-GRU:  
The BKT model and TSA-GRU are two distinct representations of different paradigms of knowledge tracing. BKT is a probabilistic model that uses binary correctness observations to infer the gradual and probabilistic process of mastering any skill that is conceived to proceed according to the structure of hidden Markov models. Although such an approach is more interpretable and simpler, it cannot fit very complex learning patterns since it has a training loss of 0.6651 and a test loss of 0.6691, as demonstrated in Figure 11, while the corresponding low training and test accuracy are at 64.10% and 64.01%, respectively. So, one thing becomes clear, unlike this, the TSA-GRU model follows a hybrid architecture combining temporal sparsity attention with GRU, representing long-term dependencies and implied contextual relations in student interactions. This modern approach greatly surpasses BKT, with a training loss of 0.0296, a test loss of 0.0209, and solid training and testing accuracies of 95.62% and 95.60%. TSA-GRU may have other large datasets and computational power requirements. The main feature is its capability for delivering high predictive performance by modeling complex learning pathways, which makes it suitable for large-scale data-driven education applications.