## 2 Literature Review
### From Pure Manual to Semi-Automated Traditional Evaluations of Teaching Behaviors in PE Classrooms
#### 2.1.2 Semi-Automated Evaluation Stage
Emergence and Application Potential of the CogVLM2-Video Model (2024-2025)  
CogVLM2-Video, introduced in 2024, is a next-generation multimodal video understanding model. Its application in education--especially physical education--is still uncommon. To justify its adoption in this study, we first review recent deep learning models applied to classroom and PE behavior recognition. In recent years, studies have increasingly applied object detection and pose estimation models to classroom behavior analysis For example, [PERSON] et al. combined YOLOv5 with contextual attention and OpenPose for detecting student behaviors, achieving an mAP of 82.1% [13]. Similarly, [PERSON] et al. extended YOLOv8 with two-dimensional positional encoding and multi-head attention to enable accurate real-time detection in complex, multi-subject environments [14]. In parallel, researchers explored advanced temporal modeling techniques. For instance, [PERSON] et al. used 3D CNNs to capture motion dynamics from visual input alone [15]. Building on this, multimodal strategies also emerged. [PERSON] et al. (2024) introduced a behavior segmentation pipeline integrating visual, audio, and textual features for structured interpretation [16]. [PERSON] et al. (2021) applied ResNet and Temporal Convolutional Networks (TCN) to estimate student engagement patterns over time [17]. Despite these advances, many models are limited by rigid architectures, low-resolution inputs, and lack of explainable temporal alignment--factors which hindered their effectiveness in real-world PE classrooms. Addressing these limitations, CogVLM2-Video, introduced in 2024, represents a next-generation multimodal vision-language model. As an extension of CogVLM2, it accommodates high-resolution video inputs (up to 1344 \(\times\) 1344), aligns visual content across multiple frames using timestamps, and supports natural language querying of event context. It has achieved state-of-the-art results on benchmarks such as MVBench and VideoChatGPT-Bench, outperforming models like TimeSformer and VideoMAE [18]. While CogVLM2-Video's architecture enables visual-language alignment, this study focuses specifically on its visual encoding and temporal recognition components, given the nature of PE classroom videos. Nevertheless, its capacity for fine-grained and interpretable behavior recognition makes it well suited to educational use cases. Related models have already demonstrated promise in adjacent instructional contexts--for example, [PERSON] et al. (2025) applied a similar system to segment and label complex procedural steps in medical training videos, highlighting its effectiveness for structured, temporally grounded analysis [19]. This literature review traces the evolution of teaching behavior evaluation methods in PE classrooms--from manual observation to semi-automated tools and intelligent deep learning-based systems. While traditional methods are foundational, they are insufficient for large-scale, dynamic, and multimodal behavior analysis. The emergence of deep learning models, particularly the CogVLM2-Video framework, provides new technical pathways for efficient, accurate, and scalable evaluation. Building upon earlier systems, this study adopts the CogVLM2-Video model to design an AI-powered evaluation system for PE classrooms. Leveraging its spatiotemporal feature extraction capabilities, the system enables precise annotation and nuanced analysis of complex teaching behaviors. This establishes a robust technological foundation for developing personalized, real-time, and multidimensional feedback mechanisms that support instructional improvement.