## 4 Results  
Implementation of the CogVLM2-Video Model-Based System for Evaluating Teaching Behaviors in PE Classrooms  
A framework based on the CogVLM2-Video toolkit has been developed for implementing the evaluation system (Figure 3). It covers four key stages: (1) constructing an evaluation index, (2) collecting intelligent data, (3) establishing the model, and (4) developing the platform. The first stage establishes the foundation of the evaluation process and defines the measurement dimensions. The second stage involves efficient collection of accurate teaching behavior data, which supports subsequent analysis. The third stage enables automated annotation and precise analyses of behaviors to support improved teaching quality. The final stage of platform development integrates all four stages, providing a stable and comprehensive operational environment for smooth system performance. 1.1 Construction of Diversified Evaluation Indicators for Teaching Behaviors as the Foundation for AI Integration and Precise Annotation  
The construction of a diversified indicator system for evaluating teaching behaviors in PE classrooms forms the foundation for integrating AI technologies and achieving precise machine annotation. This system translates complex, hard-to-quantify behavioral characteristics into actionable and measurable indicators. The indicator framework was developed based on prior studies and aligned with China's Physical Education and Health Curriculum Model. Its validity and applicability were confirmed through a two-round Delphi consultation with 22 experts in physical education, pedagogy, and psychology. In the first round, experts rated each indicator's importance and clarity on a 5-point Likert scale and provided qualitative feedback. Indicators with low average scores or high variability were revised or removed. In the second round, the revised indicators were re-evaluated. All indicators showed strong consensus, with coefficients of variation below 0.25 and average scores above 3.5. The Kendall's W  
Figure 3: Implementation framework of the system for evaluating teaching behaviors in PE classrooms. coefficients for the first-, second-, and third-level indicators were 0.146, 0.431, and 0.293, respectively, all statistically significant at \(p<0.01\). Based on the second-round ratings, weights were calculated for the three first-level indicators. Interaction behavior received the highest weight (0.659), followed by student behavior (0.185) and teacher behavior (0.156), reflecting the curriculum's emphasis on student-centered and interaction-driven instruction. The final evaluation system included three core dimensions--teacher behaviors, student behaviors, and interaction behaviors--along with 12 secondary indicators. These indicators were explanation and demonstration, guidance and evaluation, technology use, transitions, warm-up exercises, skill practice, fitness training, relaxation exercises, questioning and responses, cooperative learning, teacher-student competitions, and peer discussion and evaluation (see Figure 4) [21]. 1.2 Intelligent Data Collection: High-Precision Multi-Camera Setup for Comprehensive Non-Intrusive Data Collection and Pre-Processing  
The module on intelligent data collection uses a Sony Alpha A7 IV high-definition camera (Sony Corporation, Wuxi, China), which features a 1080p (1920 \(\times\) 1080) resolution and a frame rate of 30 fps, integrated with five-axis stabilization technology. Optimized for dynamic classroom settings, the camera's high resolution ensures accurate motion capture, enhancing action classification accuracy, video smoothness, and optical flow quality. The built-in five-axis stabilization allows for stable data collection under both handheld or motion (e.g., movement during physical activities or teacher-student interactions) conditions, effectively mitigating environmental interference and ensuring the acquisition of high-quality classroom interaction data.