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E-grāmata: Early Warning Mechanisms for Online Learning Behaviors Driven by Educational Big Data

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  • Formāts: 208 pages
  • Izdošanas datums: 14-Jun-2024
  • Izdevniecība: Routledge
  • Valoda: eng
  • ISBN-13: 9781040044476
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Early Warning Mechanisms for Online Learning Behaviors Driven by Educational Big DataPalielināt
  • Formāts: 208 pages
  • Izdošanas datums: 14-Jun-2024
  • Izdevniecība: Routledge
  • Valoda: eng
  • ISBN-13: 9781040044476
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"The book aims to design and construct early warming mechanisms based on the dynamic temporal tracking technology for online learning behaviors, driven by educational big data. By studying a massive amount of learning behavior instances generated in various interactive learning environments worldwide, the book explores the continuous sequences of correlated learning behaviors and characteristics. From various angles, the authors have devised a series of early warning measures that could effectively solvemultiple issues in learning behaviors driven by educational big data. Additionally, the book predicts patterns and identifies risks by analyzing the temporal sequences of the entire learning process. While presenting a range of theoretical achievements and technical solutions to improve and design new online learning mode, it also provides relevant technical ideas and methodologies for research on similar problems. The book will attract scholars and students working on learning analytics and educational big data worldwide"--

The book aims to design and construct early warming mechanisms based on the dynamic temporal tracking technology for online learning behaviors, driven by educational big data.



The book aims to design and construct early warning mechanisms based on the dynamic temporal tracking technology for online learning behaviors, driven by educational big data.

By studying a massive amount of learning behavior instances generated in various interactive learning environments worldwide, the book explores the continuous sequences of correlated learning behaviors and characteristics. From various angles, the authors have devised a series of early warning measures that could effectively solve multiple issues in learning behaviors driven by educational big data. Additionally, the book predicts patterns and identifies risks by analyzing the temporal sequences of the entire learning process. While presenting a range of theoretical achievements and technical solutions to improve and design new online learning mode, it also provides relevant technical ideas and methodologies for research on similar problems.

The book will attract scholars and students working on learning analytics and educational big data worldwide.

1 Introduction
2. Multidimensional Temporal Fusion and Risk Prediction
in Interactive Learning Process
3. Learning Enthusiasm Enabled Dynamic Early
Warning Sequence Model
4. Early Warning Value Propagation Network for
Continuous Learning Behaviors
5. Early Warning Pivot Space Model of
Multi-Temporal Interactive Learning Process
6. Early Warning Model Design and
Decision Application of Unbalanced Interactive Learning Behaviors
7. Cost
Sensitivity Analysis and Adaptive Prediction of Unbalanced Interactive
Learning Behaviors
8. Diagnostic Analysis Framework and Early Warning
Mechanism of Forgettable Learning Behaviors 9 Conclusion
Xiaona Xia is a professor and earned her PhD from Qufu Normal University. She is also a member of IEEE Computer Society and China Computer Federation (CCF). Her research interests include learning analytics, interactive learning environments, collaborative learning, educational big data, educational statistics, data mining and service computing.

Wanxue Qi is a PhD supervisor of Qufu Normal University. He is a famous education expert and has made remarkable achievements in higher education and moral education theory. His research interests include educational big data and moral education.
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