Abstract
Accurate assessment of student engagement is central to technology-enhanced learning, yet existing models remain constrained by class imbalance, instability across data splits, and limited interpretability. This study introduces a multimodal engagement assessment framework that addresses these issues through three complementary strategies: (1) class-aware loss functions to alleviate class imbalance, (2) temporal data augmentation and heterogeneous ensembling to enhance model stability, and (3) SHAP-based analysis of the most stable component for reliable interpretability. Reliability was established through repeated cross-validation with multiple seeds across seven deep learning architectures and the proposed ensemble. The framework established a mean accuracy of 0.901 ± 0.043 and a mean macro F1 of 0.847 ± 0.068, surpassing baselines such as ResNet (Accuracy = 0.917), Inception (Macro F1 = 0.862), and LightGBM (Accuracy = 0.922). Ablation studies highlighted temporal augmentation and ensemble diversity as key contributors, while sensitivity analyses confirmed robustness with variance consistently below 0.07 across seeds and folds. Efficiency profiling established MCNN and TimeCNN as the optimal deployment architecture, combining near-optimal accuracy with superior computational efficiency. SHAP-based interpretation was extended to provide feature-level and class-wise attribution, revealing consistent relationships between predictions and behavioral or cognitive cues. Overall, the study demonstrates that balanced evaluation and ensemble stability are essential for reliable engagement assessment.