Online Learning Behavior Prediction and Intervention Strategies Based on Multimodal Feature Fusion

Abstract

Online learning platforms generate massive amounts of multimodal data including clickstreams, video interactions, assessment responses, and textual submissions. This paper proposes a comprehensive framework for predicting student learning behaviors and implementing personalized intervention strategies through multimodal feature fusion. The methodology integrates temporal behavioral patterns, content engagement metrics, and assessment performance indicators to construct predictive models capable of identifying at-risk students and learning preference patterns. A novel attention-based fusion mechanism combines heterogeneous data sources while maintaining interpretability for educational stakeholders. Experimental validation on a dataset of 15,847 students demonstrates superior performance compared to traditional single-modal approaches, achieving 87.3% accuracy in dropout prediction and 82.6% precision in learning engagement classification. The intervention strategies generated through reinforcement learning show significant improvement in student retention rates and learning outcomes. The framework provides actionable insights for educators and contributes to the advancement of intelligent tutoring systems in online education environments.