Student Behavior Analysis and Adaptive Learning Path Planning Based on Multi-Feature Hypergraph Learning

This paper proposes a method for student behavior analysis and adaptive learning path planning based on multi-feature hypergraph learning. By integrating behavioral and spatiotemporal features, a hypergraph model is constructed to capture the complex relationships between student learning behaviors, enabling dynamic optimization of learning paths. Experimental results demonstrate that the proposed approach significantly outperforms traditional methods in terms of learning behavior prediction accuracy and path planning personalization. The method offers valuable insights for the development of intelligent education systems and provides a more accurate and adaptable approach to personalized learning path planning.