Research on student classroom attention recognition based on multimodality

With the continuous improvement of teaching contents and methods, the concept of students as the main body of teaching has been deepened. Efficient and accurate identification of students’ classroom concentration has become an effective means to improve classroom efficiency and is also a focus of attention in the field of education. In this paper, we propose a concentration recognition model based on the multimodal fusion of students’ head posture and facial expressions and validate and experiment on a self-built concentration database. The experimental results demonstrate that non-negative emotions are beneficial to concentration, and the student’s head orientation can reflect the student’s concentration more clearly. The multimodal fusion method performs better than the unimodal method. This will reduce the workload of manual data collection and effectively improve the accuracy of data collection. It also strengthens the home-school association and has implications for the development of educational theory and practice.