## 1 Introduction  
With the rapid development of artificial intelligence and big data, emerging technologies have become the core force driving innovation and development in education. These technologies are widely used to assist teaching [1, 2], manage classrooms, and analyze student behavior. For example, applications like the \"Rain Classroom\" mini-program make attendance and questioning more convenient, while advanced monitoring technologies simplify classroom management and provide accurate data for teaching evaluations. In particular, student behavior detection technology, through deep learning models, offers new perspectives for understanding student performance and teaching quality in classrooms. Despite challenges in data collection and technical application in university settings, this research holds great potential and significant value in improving educational management efficiency and teaching quality. Future research in this field is expected to elevate the quality of education to new levels [3, 4]. The rapid advancement of object detection technology in the field of computer vision has garnered widespread attention in academia, especially methods based on deep learning. These methods can be broadly categorized into three types: two-stage algorithms, single-stage algorithms, and recently emerging Transformer-based algorithms. Since the introduction of the AlexNet network with ReLU activation function and Dropout technology,deep learning has achieved significant progress in image recognition accuracy, marking a major breakthrough in the field [5]. Subsequently, the OverFeat algorithm demonstrated the potential of convolutional networks in object detection. The advent of R-CNN [6] and its derivative algorithms, Fast R-CNN [7] and Faster R-CNN [8], significantly improved object detection accuracy and speed by integrating convolutional neural networks with region proposal techniques, highlighting the application of deep learning in object detection. Following this, algorithms such as Mask R-CNN [9], Cascade R-CNN [10], and Libra R-CNN [11] further enhanced instance segmentation accuracy and the ability to handle complex scenes. In the realm of single-stage algorithms, the YOLO series [12; 13; 14; 15; 16] transformed the detection task into a regression problem, achieving fast and efficient object detection. Algorithms like SSD [17], RetinaNet [18], and EfficientDet [19] adopted various strategies to improve detection performance, especially in handling small objects and class imbalance issues. Recently, Transformer-based object detection algorithms, such as DETR [20] and its improved versions, Deformable DETR [21] and Conditional DETR [22], have demonstrated strong capabilities in processing global information and complex scenes by simplifying detection processes and optimizing training efficiency. Particularly, RT-DETR [23], designed for real-time object detection, enhances performance in accuracy and speed through optimized computational complexity and inference speed, making it highly suitable for detecting student classroom behaviors in complex scenes. In recent years, student classroom behavior detection has become a research hotspot, focusing on three deep learning-based methods: sensor analysis, skeleton key point detection, and convolutional network detection. Sensor methods, for instance, use Kinect One to capture 2D and 3D data and evaluate student attention through machine learning techniques [24]. Skeleton key point detection identifies specific behaviors by tracking changes in student skeletal points, such as the OpenPose model recognizing hand-raising behavior [25; 26; 27]. Convolutional network detection methods, like YOLO, have improved behavior recognition accuracy and speed through algorithm enhancements [28; 29; 30; 31]. Despite progress in student classroom behavior detection, the field still faces multiple challenges, such as outdated algorithms, insufficient detection efficiency, and a lack of diverse public datasets.