Multimodal deep learning for sports teacher behavior analysis: design and evaluation of a personalized continuing education recommendation system

This study addresses the limitations of traditional continuing education approaches for sports teachers by developing a personalized recommendation system based on multimodal deep learning analysis of teaching behaviors. The system implements a comprehensive framework that captures video, audio, and motion data from teaching sessions to analyze instruction quality across multiple dimensions. A hierarchical classification system categorizes teaching behaviors while a multidimensional quality assessment model evaluates performance. The personalized recommendation algorithm integrates teacher ability profiles with resource characteristics through a multi-objective optimization approach that balances development needs, interests, and learning preferences. System evaluation with 124 physical education teachers demonstrated superior recommendation accuracy (F1 = 0.85) compared to traditional methods and significant improvements in teaching behaviors for the intervention group across instructional clarity (d = 0.68), demonstration quality (d = 0.72), and feedback specificity (d = 0.59). The findings indicate that multimodal behavior analysis can effectively identify specific development needs and generate targeted continuing education recommendations that significantly enhance sports teaching quality and professional development.