Intelligent Multi Model Ensemble for Engagement Prediction

For intelligent educational systems, the ability to monitor
and respond to student engagement in real time is essential for
enhancing learning outcomes. However, existing models often lack
adaptability and practical deployment potential, as they depend on
single data modalities, rigid ensemble mechanisms, and post-session
analysis. This study introduces an intelligent multimodal ensemble
framework designed to address these challenges by predicting student
engagement using predefined multimodal educational datasets that
include facial expressions, voice tone, physiological signals, and
interaction logs. The proposed system leverages deep neural networks
(CNNs for spatial and RNNs for temporal analysis) in combination
with classical machine learning algorithms (SVMs and Decision
Trees), integrated through an adaptive weighting mechanism that
dynamically adjusts model contributions based on predictive
confidence. Furthermore, explainable AI techniques, particularly
SHAP, are incorporated to enhance transparency and interpretability.
Experimental evaluations across multiple educational contexts
demonstrate the framework’s superior performance in terms of
accuracy, generalization, and real-time efficiency. Unlike prior
multimodal ensemble approaches, the proposed model uniquely
combines adaptive confidence-based weighting and SHAP-driven
interpretability, offering a balanced and deployable solution that
bridges the gap between accuracy and explainability in real-world
learning environments.