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

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 D3DCNN_ResNet is well-suited for classroom quality analysis and has the potential to enhance teaching strategies by providing real-time feedback on student engagement.