Content area
To address poor skill acquisition in online physical education due to a lack of real-time feedback, we developed and evaluated a pose recognition-based system. An 8-week randomized controlled trial study in a university Baduanjin course compared the AI system against a traditional Massive Open Online Course format. Results showed the system significantly enhanced students' movement quality, fluency, learning interest, and self-directed learning. Crucially, mediation analysis identified increased learning duration as the primary significant mechanism driving this skill acquisition, outweighing changes in interest or self-direction within our model. While promising, the technology has limitations in accuracy and interactivity. Future research should focus on optimizing algorithms and integrating adaptive learning to create more effective OLPE strategies.
Details
Independent Study;
Competence;
Guidance;
Learning Motivation;
Influence of Technology;
Learning Processes;
Learning Theories;
Imitation;
Blended Learning;
Feedback (Response);
Databases;
Access to Education;
Electronic Learning;
Artificial Intelligence;
Instructional Effectiveness;
Student Motivation;
MOOCs;
Educational Environment;
Database Management Systems;
Cognitive Ability;
Learner Engagement;
Cognitive Psychology;
Educational Equity (Finance);
Algorithms
; Ma, Lianzhen 1 ; Qi, Shilong 2 ; Zhang, Bo 1 ; Ruan, Wenpian 1 1 South China Normal University, School of Physical Education and Sports Science, Guangzhou, China (GRID:grid.263785.d) (ISNI:0000 0004 0368 7397)
2 South China Normal University, School of Physical Education and Sports Science, Guangzhou, China (GRID:grid.263785.d) (ISNI:0000 0004 0368 7397); Liaocheng University, School of Physical Education, Liaocheng, China (GRID:grid.411351.3) (ISNI:0000 0001 1119 5892)