A novel parameter dense three-dimensional convolution residual network method and its application in classroom teaching

Introduction: Improving the rationality and accuracy of classroom quality analysis is crucial in modern education. Traditional methods, such as questionnaires and manual recordings, are resource-intensive and subjective, leading to inconsistent results. As a solution, computer vision (CV) technologies have emerged as powerful tools for real-time classroom monitoring. This study proposes a novel Dense 3D Convolutional Residual Network (D3DCNNResNet) to recognize students' expressions and behaviors in English classrooms. Methods: The proposed method combines Single Shot Multibox Detector (SSD) for target detection with an improved D3DCNNResNet model. The network applies 3D convolution in both spatial and temporal domains, with shortcut connections from residual blocks to increase network depth. Dense connections are introduced to enhance the flow of high- and low-level features. The model was tested on two datasets: the CK+ dataset for expression recognition and the KTH dataset for behavior recognition. Results and Discussion: The experiments show that the proposed method is highly efficient in optimizing model training and improving recognition accuracy. On the CK+ dataset, the model achieved an expression recognition accuracy of 97. 94%, while on the KTH dataset, the behavior recognition accuracy reached 98. 86%. The combination of residual blocks and dense connections reduced feature redundancy and improved gradient flow, leading to better model performance. The results demonstrate that the D3DCNNResNet is well-suited for classroom quality analysis and has the potential to enhance teaching strategies by providing real-time feedback on student engagement.