Engagement Detection Based on Spatio-Temporal Attention Mechanism for Online Education
Aiming at the problem that it is difficult for teachers to timely learn students'engagement due to the separation of time and space in the online education environment,a lightweight deep learning network model is designed for the detection of students'engagement.The model makes decisions based on the student's facial expression information.It uses a deep residual network to extract spatial features and a long short-term memory network to extract temporal features.The Shuffle Attention and the Global Attention are added to optimize the feature extraction ability of the model to improve the effect of engagement detection.The experimental results show that the proposed method achieves high accuracy on both public and self-collected datasets.It is better suited to the needs of practical online learning scenarios in terms of accu-racy and time cost.
国家科技期刊平台
NSTL
万方数据
维普
