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Discrete Dynamic Modeling of Learner Behavior Analysis in Physical Education Teaching
Jia Shi1, Jun Sun1, Zhonghua Zheng1
1Department of Sports, Central China Normal University, Wuhan, Hubei 430070, China.
Computational Intelligence and Neuroscience
|April 22, 2022
Summary
Discrete dynamic modeling analyzes learner behavior in physical education, revealing video learning significantly impacts engagement. This technology enhances prediction accuracy for student achievement changes in online education.
Area of Science:
- Educational Technology
- Data Science in Education
- Sports Science
Background:
- The big data era necessitates integrating information technology with education for industry advancement.
- Predicting learner characteristics, behavior, and trends is crucial for large-scale teaching tasks.
- Online education platforms are increasingly important due to rapid network technology development.
Purpose of the Study:
- To investigate learner behavior in physical education within a big data environment.
- To explore influencing factors on learner behavior characteristics on online education platforms.
- To develop dynamic models for predicting achievement changes in physical education.
Main Methods:
- Utilizing discrete dynamic modeling technology for analyzing learner behavior.
- Quantifying learning process data to extract learner feature points for personalized dynamic data.
- Employing the grey model for achievement change system construction and dynamic modeling for trend reflection.
Main Results:
- Video learning identified as the primary factor influencing behavior change in physical education.
- Most students exhibit passive learning behaviors, lacking initiative.
- Discrete dynamic modeling improves the accuracy of predicting student achievement changes.
Conclusions:
- Discrete dynamic modeling provides effective data for research on physical education learner behavior and achievement.
- Understanding learner behavior patterns is key to optimizing online educational strategies.
- Personalized dynamic learning data construction can enhance educational outcomes.
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