## 2 Literature Review
### From Pure Manual to Semi-Automated Traditional Evaluations of Teaching Behaviors in PE Classrooms
#### 2.1.2 Semi-Automated Evaluation Stage  
The rapid advancement of information technology has led to the development of software tools for annotating and analyzing teaching behaviors in PE classrooms for evaluation purposes. Commonly used tools include NVivo, LessonNote, and Mixed Media Annotation (MMA) Video 2.0. NVivo is used to summarize the characteristics and influencing factors of PE classroom teaching by annotating and analyzing three dimensions of classroom teaching videos, namely teacher behaviors, student behaviors, and interaction behaviors [8]. However, in practice, this semi-automated approach is limited by major efficiency issues. Annotating a 45 min video typically requires several hours of manual work. Because of the delay caused by manual annotation, real-time analysis and rapid feedback cannot be achieved, which reduces its value for improving teaching promptly. While traditional tools such as CAFIAS, ORRPETB, and NIBS have laid the groundwork for behavior evaluation in PE classrooms, they are constrained by subjectivity, time-intensive coding, and a lack of real-time feedback. In highly dynamic physical education (PE) settings, such static frameworks struggle to capture complex, multimodal interactions between teachers and students. Semi-automated tools like NVivo and MMA Video 2.0 improve annotation efficiency but still rely heavily on manual input and post hoc analysis, limiting their scalability and timely instructional relevance. In contrast, AI-based models--such as CogVLM2-Video--integrate deep learning with motion tracking and real-time analysis, enabling high-resolution, continuous data processing with greater objectivity. These systems offer the potential for scalable, data-rich evaluation while also supporting long-term behavior pattern analysis. From Convolutional Neural Network-Based Video Action Recognition to Intelligent Evaluation of Teaching Behaviors in PE Classrooms with CogVLM2-Video Models  
To address the limitations of traditional manual or rule-based evaluations of teaching behaviors, particularly in dynamic physical education (PE) contexts, researchers have explored progressively advanced AI-driven behavior recognition models. These developments can be broadly categorized into three stages, described below. Exploratory Stage: Convolutional Neural Network and Temporal Segment Network (2014-2016)  
In 2014, [PERSON] et al. introduced a video classification method based on a convolutional neural network (CNN), marking the first application of deep learning tools for video data analysis [9]. This method effectively captures spatial features in videos, but their temporal dimensions are less readily captured. As a result, it performs poorly in identifying actions recorded in long-duration videos. To address this limitation, in 2016, [PERSON] et al. proposed integrating CNN within temporal segment networks (TSN) to extract spatiotemporal features, allowing video segmentation and sampling [10]. Although TSN improved the accuracy of action recognition in long videos, it remained inadequate for capturing short-term variations and subtle behaviors in complex PE classroom environments. Temporal Modeling Enhancements: Convolutional 3D, Inflated 3D, and Temporal Pyramid Network Models (2016-2024)  
The temporal pyramid network (TPN) was introduced to overcome the limitations of TSN. Its pyramid-like structure enabled flexible extraction of features at different temporal scales, thus effectively capturing short-term dynamics, while retaining long-term information [11]. Building on this model, researchers developed convolutional 3D (C3D) and inflated 3D ConvNet (I3D) models, which captured spatial and temporal information simultaneously. These models proved effective for analyzing students' attention and actions in complex teaching settings [12]. However, notwithstanding their theoretical advantages, they faced challenges such as high computational cost and reliance on large-scale video data, which limited their widespread application in evaluations of teaching behavior.