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This title aims to fully demonstrate the burnout of students in online learning processes. The authors propose a series of feasible and reliable solutions to sufficiently obtain and analyze massive instances of online learning behavior.
Xiaona Xia is a professor at Qufu Normal University. She is a member of Institute of Electrical and Electronics Engineers and China Computer Federation. Her research interests include learning analytics, interactive learning environments, collaborative learning, educational big data, educational statistics, data mining, service computing, etc. Wanxue Qi is a professor at Qufu Normal University. He is an established educational expert in higher education and moral education. His research interests include educational big data, moral education, etc.
1. Introduction 2. Key Burnout Feature Selection and Association Prediction of Learning Behaviors 3. Learning Behavior Reasoning and Critical Path Fusion for Burnout Based on Multi-entity Association 4. Predicting Burnout States and Guiding Learning Behaviors Driven by Knowledge Graph Propagation 5. Adaptive Positioning of Temporal Intervals for Key Interventions and Burnout Tracking 6. Risk Prediction and Early Warning Routing Formation of Burnout State Propagation 7. Positive Guidance of Learning Behaviors Based on Effective Burnout Intervention 8. Conclusion
