# TSA-GRU: A Novel Hybrid Deep Learning Module for Learner Behavior Analytics in MOOCs
## 2 Related Works
### Review and Comparative Analysis of Prior Work  
Table 1 illustrates a thorough comparison of different methods of learner behavior analytics in e-learning and MOOC contexts. As outlined in Table 1, there are several key trends that can be identified in the studies reviewed. First, deep learning methods (CNN, BiLSTM, and hybrid approaches) have consistently outperformed traditional machine learning methods with respect to accuracy and generalizability, particularly with dropout prediction and MOOC engagement identification. For example, models that utilize a BiLSTM and CNN [13; 25] have demonstrated considerable predictive performance while using a moderate number of computational resources, suggesting a potential application in a real-time system. \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Method** & **The Resolved Problem** & **Caps Resolved by the Approach** & **Accuracy** & **Scalability** & **Interpretability** & **Resource Consumption** & **Adaptability to Large Datasets** \\ \hline \hline \multirow{4}{*}{Machine Learning [11]} & Traditional engagement metrics & Introduces k-means clustering to & & & & & \\  & do not capture true & indicate the necessity of & & & & & \\  & student engagement. & behavioral, emotional, and & & & & & & \\  & & Combines process mining. & & & & & & \\  & & & learning analytics, and X/1 to & & & & \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Method** & **The Resolved Problem** & **Caps Resolved by the Approach** & **Accuracy** & **Scalability** & **Interpretibility** & **Resource Consumption** & **Adaptability to Large Datasets** \\ \hline \multicolumn{1}{c}{**MOOC Dropout Prediction Models**} & & & & & & & \\ \hline Fractional-Iterative BLSTM [21] & Conventional models struggle with feature identification. & Use fractional calculosis in & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\ \hline Fractional-Iterative BLSTM [21] & Conventional models struggle with feature identification. & Use fractional calculosis in & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\ \hline \multicolumn{1}{c}{**Personalized Learning in MOOCs**} & & & & & & & \\ \hline Knowledge-based & Global start and lack of & Combines semantic web rules & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & \\  & & & & & & & \\ \hline Knowledge-based & Global start and lack of & Combines semantic web rules & & & & & \\  & & & & & & & \\  & & & & & & \\  & & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\ \hline \hline \multicolumn{1}{c}{**S**ondent Performance Prediction in Online Learning**} & & & & & & & \\ \hline Behavior-Integrated & ML models often ignore & Integrate clustering with ML & & & & & \\  & Predictor [22] & Resource log. & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._Deep learning approaches have demonstrated a significant lack of interpretability. Approaches that incorporate XAI [12] or decision trees [17; 18] provide some degree of accountability but may result in either a lack of scalability or predictive capacity. Thus, the trade-offs in interpretability, prediction, and accountability must be balanced when developing the aforementioned systems with the potential for uncovering unknown behavioral insights. Cold-start and sparsity issues have been addressed frequently with clustering [11; 17] and self-supervised learning [23], which suggests that unsupervised techniques are critical for generalizing results between learner populations.