The precise recognition of entire classroom meta-actions is a crucial challenge for the tailored adaptive interpretation of student behavior, given the intricacy of these actions. This paper proposes a Dynamic Position Embedding-based Model for Student Classroom Complete Meta-Action Recognition (DPE-SAR) based on the Video Swin Transformer. The model utilizes a dynamic positional embedding technique to perform conditional positional encoding. Additionally, it incorporates a deep convolutional network to improve the parsing ability of the spatial structure of meta-actions. The full attention mechanism of ViT3D is used to extract the potential spatial features of actions and capture the global spatial–temporal information of meta-actions. The proposed model exhibits exceptional performance compared to baseline models in action recognition as observed in evaluations on public datasets and smart classroom meta-action recognition datasets. The experimental results confirm the superiority of the model in meta-action recognition.

The complexity of various factors influencing online learning makes it difficult to characterize learning concentration, while Accurately estimating students’ gaze points during learning video sessions represents a critical scientific challenge in assessing and enhancing the attentiveness of online learners. However, current appearance-based gaze estimation models lack a focus on extracting essential features and fail to effectively model the spatio-temporal relationships among the head, face, and eye regions, which limits their ability to achieve lower angular errors. This paper proposes an appearance-based gaze estimation model (RSP-MCGaze). The model constructs a feature extraction backbone network for gaze estimation (ResNetSC) by integrating ResNet and SCConv; this integration enhances the model’s ability to extract important features while reducing spatial and channel redundancy. Based on the ResNetSC backbone, the method for video gaze estimation was further optimized by jointly locating the head, eyes, and face. The experimental results demonstrate that our model achieves significantly higher performance compared to existing baseline models on public datasets, thereby fully confirming the superiority of our method in the gaze estimation task. The model achieves a detection error of 9.86 on the Gaze360 dataset and a detection error of 7.11 on the detectable face subset of Gaze360.

Estimating human posture in crowded smart teaching environments is a fundamental technical challenge for measuringlearners’ level of engagement. This work presents a model for detecting critical points in human posture using ECAv2-HRNetin crowded situations. The paper introduces a method called ECAv2Net, which combines a channel feature reinforcementmethod with the ECANet attention mechanism network. This innovation improves the performance of the network. Additionally,ECAv2Net is integrated into the high-resolution network HRNet to create ECAv2-HRNet. This fusion allows for the incorporationof more useful feature information without increasing the model parameters. The paper also presents a human posture datasetcalled GUET CLASS PICTURE, which is designed for dense scenes. Experimental results using this dataset, as well asa public dataset, demonstrate the superior performance of the human posture estimation model based on ECAv2-HRNetproposed in this paper.

Estimating human posture in crowded smart teaching environments is a fundamental technical challenge for measuring learners’ engagement levels. This work presents a model for detecting critical points in human posture using ECAv2-HRNet in crowded situations. The paper introduces a method called ECAv2Net, which combines a channel feature reinforcement method with the ECANet attention mechanism network, this innovation improves the performance of the network. Additionally, ECAv2Net is integrated into the high-resolution network HRNet to create ECAv2-HRNet. This fusion allows for the incorporation of more useful feature information without increasing the model parameters. The paper also presents a human posture dataset called GUET CLASS PICTURE, which is designed for dense scenes. The experimental results when using this dataset, as well as a public dataset, demonstrate the superior performance of the human posture estimation model based on ECAv2-HRNet proposed in this paper.