软件导刊2024，Vol.23Issue(1)：150-155,6.DOI:10.11907/rjdk.231045
基于时空注意力机制的在线教育专注度检测
Engagement Detection Based on Spatio-Temporal Attention Mechanism for Online Education
摘要
Abstract
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.关键词
在线教育/专注度检测/ResNet18/LSTM/注意力机制Key words
online education/engagement detection/ResNet18/LSTM/attention mechanism分类
信息技术与安全科学引用本文复制引用
梁艳,周卓沂,黄伟聪,郭梓健..基于时空注意力机制的在线教育专注度检测[J].软件导刊,2024,23(1):150-155,6.基金项目
国家科技创新2030-"脑科学与类脑智能技术"重点项目(2022ZD0208900) （2022ZD0208900）
国家自然科学基金项目(62076103) （62076103）
