Detection and recognition of learner attention status based on behaviors in distance synchronous classrooms

Synchronous classroom is an innovative practice in China that aims to build a community of urban and rural schools and promote a balanced development of education. As the distance learners are physically separated from the local teacher, they are more prone to situations of losing attention. Therefore, real-time detection and recognition of the attention status of distance learners is an important means to enhance the effectiveness and quality of teaching. This research achieved real-time detection and recognition of attention status from the perspective of classroom behavior recognition. Specifically, the research used the SAM algorithm to construct a dataset of classroom behavior data containing two typical behaviors for attentive status and three for inattentive status and utilized the YOWO network model that integrates two-dimensional spatial features and three-dimensional spatio-temporal features to achieve recognition and classification of five typical classroom behaviors, with an accuracy of 89.7%. Through the experiment, the correlation between the attention detection results of this model and the evaluation results by professional teachers reached 0.863, proving the effective evaluation of the attention status of distance learners.