## 5 Discussion  
Advancing Evaluations of Teaching Behaviors in PE Classrooms with Intelligent Technology to Achieve Automation and Precision in Behavioral Analysis  
An intelligent, CogVLM2-Video model-based system for evaluating teaching behaviors in PE classrooms was designed and implemented in this study. The system automatically captures, recognizes, and analyzes teacher and student behaviors and their interaction behaviors in PE classrooms and generates detailed reports to assist teachers in optimizing their instructional strategies. Its contributions relating to technological innovation, effective teaching evaluation, and practical applicability are thus significant. 1.1 Microservice Architecture and Dual-Layer Database: Novel Integration of Software and Database Design  
The system employs the Spring Cloud microservice architecture, dividing functionalities into independent service units to achieve enhanced scalability and maintainability [26]. A dual-layer database structure combined with data backup and recovery mechanisms ensures data security, reliability, and integrity. Inspired by the layered architecture concepts of [PERSON] and [PERSON], this design concept supports the system's clarity and stability [27]. Compared with other introduced systems, such as a virtual English teaching system [24] and an online education platform [28], this system supports not only small-scale classrooms but also large-scale PE classrooms using high-resolution cameras to capture dynamic and complex motion scenarios. Furthermore, its algorithmic fusion and visualization capabilities, along with the use of methods such as temporal and entropy analysis, enable precise evaluation of PE classroom teaching. Thus, it provides robust support for optimizing teaching strategies and clear advantages relating to technical implementation as well as educational applications. 1.2 Behavior Annotation Technology Based on the CogVLM2-Video Model: A New Approach for Automatically Capturing and Classifying Teaching Behaviors  
With the advancement of sensor technology, intelligent wearable devices have been widely adopted for evaluating teaching behaviors. For instance, [PERSON] et al. used eye-tracking devices combined with machine learning to predict teacher activities [29], and [PERSON] et al. evaluated student engagement using facial recognition technology [30]. However, these studies were primarily conducted in smart classroom environments and focused on cognitive behaviors, with insufficient attention given to dynamic and sustainable teaching behaviors, which are unique to PE classrooms. By incorporating the CogVLM2-Video model, this system overcomes the limitations of traditional sensors, which primarily collect cognitive data [31]. The automated annotation technology enables the system to capture and classify teaching behaviors automatically, identifying not only teacher and interaction behaviors but also cognitive and physical practices during student learning. It thus fills an existing research gap and meets the unique evaluation needs of PE classrooms, significantly enhancing the accuracy and comprehensiveness of evaluations of teaching behaviors. 1.3 Multi-Algorithm Fusion for Intelligent Analysis: A New Paradigm for Quantifying Teaching Behaviors in PE Classrooms  
Leveraging the high-precision CogVLM2-Video model and deep learning technologies, the system integrates multiple algorithms tailored to the characteristics of PE classrooms, including entropy, redundancy, ratio, and temporal analyses. These algorithms provide a multifaceted approach for uncovering teaching behavior patterns and deconstructing classroom structures. Compared with traditional in-person observations and qualitative assessments, entropy analysis offers more precise quantification, addressing the limitationsof accuracy and depth of qualitative methods, and revealing the complexity and diversity of teaching processes [32].