## 2 Background
### Student Behavior Recognition
Then, the weighted sum method is used to fuse the temporal and spatial features, and the softmax classifier is improved for classification and recognition. [PERSON] et al. [16] proposed an improved YOLOv3 target detection algorithm. By inserting the attention mechanism CBAM module into the original YOLOv3 shortcut structure, the effective features of students' classroom behavior can be quickly and effectively learned. Experimental results show that the improved YOLO-CBAM algorithm can improve the detection ability of small targets. When GIoU and Focalloss were included in the model, the mAP value could reach 0.95 and the F1 value could reach 0.879. [PERSON] et al. [17] collected and constructed a dataset of teacher behavior using online open classroom teaching videos as data sources. Using this dataset, they explore action recognition methods based on RGB video and skeleton information and perform information fusion between them to improve recognition accuracy. Their experimental results show that the fusion of RGB information and skeleton information can improve recognition accuracy, and the early fusion effect is better than the later fusion effect. In 2021, [PERSON] et al. [18] proposed a student behavior identity based on skeletal posture estimation and human detection. They use OpenPose framework to collect skeleton data, and put forward an error correction scheme based on posture estimation and human detection technology to reduce false connections in skeleton data. The preprocessed skeleton data are then used to eliminate several joints that have weak influence on behavior classification. Secondly, feature vectors representing human posture are generated by feature extraction. The adopted features include standardized joint position, joint distance, and bone angle. Finally, the students' behaviors are classified to identify their behaviors. A deep neural network is constructed to classify actions. [PERSON] et al. [19] proposed to combine the traditional clustering algorithm with the random forest algorithm, improve the traditional algorithm, and identify students' classroom behavior in real time by combining the human skeleton model. [PERSON] et al. [20] proposed a moving target detection algorithm for student behavior recognition in class. Based on the region of interest (ROI) and face tracking, the authors proposed two algorithms to recognize the standing behavior of students in class. Moreover, a recognition algorithm was developed for hand raising in class based on skin color detection. Through experiments, the proposed algorithms were proved to be as effective in the recognition of student classroom behaviors. In 2022, [PERSON] et al. [21] proposed that an object detector extracts a single region from the keyframes as input to the network, and then the Multi-Task Heatmap Network (MTHN) module extracts an intermediate heatmap of multi-scale feature associations. Through the mapping relationship, the pose estimation and object detection tasks are constructed to obtain the keypoint and object position information. Finally, the key point behavior vector and measurement vector were used to model the behavior, and the classroom behavior detection algorithm based on the fully connected network was designed. [PERSON] et al. [22] proposed a new type of StuArt, a new automatic system designed to make personalized classroom observations that enable teachers to focus on the learning status of each student. StuArt can identify five representative student behaviors (hand raising, standing, sleeping, yawning, and smiling) that are highly correlated with engagement and track their changing trends throughout the course. To protect the privacy of students, all the changing trends are indexed by the seat number without any personal identifying information.