# TSA-GRU: A Novel Hybrid Deep Learning Module for Learner Behavior Analytics in MOOCs
## 7 Conclusions  
In this work, we presented TSA-GRU, a hybrid model that brings together TSA and GRU-based sequential modeling to better trace student knowledge within MOOC platforms. Unlike many traditional approaches, such as BKT, Performance Factor Analysis (PFA), or even more recent architectures like MSA-GRU, TSA-GRU shows a stronger ability to track how learners progress over time by handling both short- and long-term learning patterns. The model produced promising results concerning the experimental design: it had a test accuracy of 95.60% and a loss of 0.0209, converging after five epochs. One could look at the convergence of the model in a positive light since, in practice, we need faster or even larger training (and potential modeling) cycles, implying the model can generalize and be trained and succeed without overfitting or unlimited training. Moreover, there were no large performance gains even with additional training epochs, further supporting the assertion that TSA-GRU learns things of interest quickly. One of TSA-GRU's core strengths is its ability to model how a student's knowledge changes over time. This feature opens up real opportunities for building systems that respond to learners individually, whether by helping to spot early warning signs for struggling students or by supporting more adaptive content delivery. Additionally, extensive validation through five-fold cross-validation, confidence interval analysis, and paired \(t\)-testing confirmed the robustness, statistical reliability, and consistent superiority of TSA-GRU over MSA-GRU. That said, a few limitations should be mentioned. At present, the attention mechanism depends on fixed threshold values, which must be selected manually. This could make it harder to transfer the model across to different datasets without tuning. Also, the current setup does not include emotional or contextual data, both of which could play a role in learning outcomes and model accuracy. For future development, one track we are examining is reinforcement learning, with an eye toward the model adapting the learning path during the assignment as the student interacts with the content. We are also interested in meta-learning approaches so that the model can generalize to new types of students or educational content with minimal retraining. Other potential areas are investigating self-supervised learning to work more with raw interaction data and utilizing graph neural networks to better represent the relationships between students, topics, or assessments. Enhancing model transparency is, furthermore, a substantial aim. By developing clearer interpretability mechanisms, we anticipate being able to support teachers in understanding why certain predictions are made, which would make the tool more actionable in classrooms. Testing TSA-GRU in situ in learning environments is another important next step that would indicate how it operates in real-world conditions. To conclude, TSA-GRU represents an important advance in student modeling. Its fidelity, performance, and adaptability make it an important part of the journey toward AI-enhanced personalized education systems. As we continue to develop TSA-GRU, we will consider possible uses of the toolbox to support better, smarter, and responsive educational technologies behavior in different learning scenarios. Conceptualization, S.O.B., A.B., M.D., M.M., M.S.K. and M.C.G. ; Methodology, S.O.B., A.B., M.D., M.M., M.S.K. and M.C.G. ; Software, S.O.B., A.B., M.D., M.M., M.S.K. and M.C.G. ; Validation, S.O.B., A.B., M.D., M.M., M.S.K. and M.C.G. ; Formal Analysis, S.O.B., A.B., M.D., M.M., M.S.K. and M.C.G. ; Investigation, S.O.B., A.B., M.D., M.M., M.S.K. and M.C.G. ; Resources, S.O.B., A.B., M.D., M.M., M.S.K. and M.C.G. ; Data Curation, S.O.B., A.B., M.D., M.M., M.S.K. and M.C.G. ; Writing--Original Draft, S.O.B., A.B., M.D., M.M., M.S.K. and M.C.G. ; Writing--Review and Editing, S.O.B., A.B., M.D., M.M., M.S.K. and M.C.G. ; Visualization, S.O.B., A.B., M.D., M.M., M.S.K. and M.C.G. ; Supervision, A.B., M.D., M.S.K. and M.C.G.