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.
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.
In order to flexibly perceive and intervene in the "burnout state" and improve online learning processes and learning effectiveness, the authors design and construct various novel data analysis models and decision prediction methods using technological means and data-driven learning strategies. Their innovative methods, techniques, and decisions would benefit autonomous learning behavior tracking and stimulate the learning interest of online learning processes enabled by predictive learning analytics. By employing behavioral science research strategies, they build adaptive prediction and optimization measures for positive online learning patterns, improve learning behaviors, optimize learning states, and establish dynamic and sustainable knowledge tracing paths and behavior scheduling methods, enabling users to achieve self-organization and self-mobilization in their overall learning processes.
The title will appeal to scholars and students in Europe, North America, and Asia, especially those majoring in educational statistics and measurement, educational big data, learning analytics, educational psychology, artificial intelligence in education, computer science, and online collaborative learning.
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
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 theory. His research interests include educational big data, moral education, etc.
