A Dynamic Precision Evaluation System for Physical Education Classroom Teaching Behaviors Based on the CogVLM2-Video Model
Chao Liu, Fan Yang, Chengyu Ge, Zhiyu Shao · Applied Sciences · 2025
Analyses of teaching behaviors in physical education (PE) classrooms are critical for evaluating teaching quality. Traditional evaluation methods primarily rely on manual analysis, which suffers from complex coding procedures, low efficiency, and suboptimal accuracy, hindering long-term sustainability in teaching quality improvement. Artificial intelligence (AI) technology offers a novel approach by enabling real-time data collection, automated annotation, and in-depth analysis of teaching behaviors, thereby supporting sustainable PE teaching optimization. Leveraging the CogVLM2-Video model, the research presents a system for real-time data collection, automated annotation, and in-depth analysis of teaching behaviors. It consists of four key modules: The perception layer handles data acquisition and input providing foundational data for analysis. The platform layer manages data processing and storage, ensuring integrity and security for long-term evaluation. The model layer focuses on behavior recognition and analysis, employing advanced algorithms for precise interpretation of teaching behaviors. The application layer delivers real-time feedback and adaptive recommendations, promoting sustained teaching improvement. The system architecture was initially validated using 50 basketball lesson videos. Then, the recognition model was trained on a Kinetics-400 subset, achieving 92% accuracy and 95% consistency with manual annotations. These results demonstrate the system’s practical value and long-term applicability, offering an efficient, precise solution for PE classroom teaching behavior assessment.