Design of an integrated multi-modal machine learning framework for real-time student engagement evaluation and learning outcome optimizations

The proposed framework achieved the highest accuracy of 94.2% in engagement prediction, 92.3% in dropout risk forecasting, and 91.5% in cognitive load estimation, surpassing contemporary baselines by 4% to 12% on all evaluation metrics. Adaptive learning pathways produced by the RL-ALP component enhance distance learning outcomes by 14.3% and decrease dropout rates by approximately 20.3%. Furthermore, real-time implementation is enabled due to the framework's computational efficiency, achieving inference time at 21.3 ms, which is about 50% faster than prior works. The findings thus affirm this system's capability of combining the behavioral, physiological, and interactional data modalities for accurate real-time monitoring of engagement and adaptive learning optimizations.1.A multi-modal framework integrating behavioral, physiological, and interaction-based features for engagement assessment.2.Five interconnected models leverage real-world datasets to improve cognitive load estimation and dropout prediction.3.Data-driven insights assist educators in optimizing learning strategies for better student retention and performance.