Bimodal Classroom Atmosphere Recognition Based on the Classroom Assessment Scoring System

The classroom atmosphere is an important factor affecting classroom learning experience, participation and teaching effectiveness. Accurate recognition of classroom atmosphere is vital for optimizing classroom teaching and promoting cognitive development. However, current classroom atmosphere recognition suffers from limitations such as one-sided representational dimensions and single data modality, which makes the existing research results difficult to directly serve classroom teaching practice. Therefore, a bimodal classroom atmosphere recognition method based on the classroom assessment scoring system was proposed in this paper: firstly, label according to classroom atmosphere indicators; then, use temporal-aware bi-directional multi-scale network integrated with multi-head self-attention and the video sliding window transformer model to conduct single-modal classroom atmosphere recognition based on audio data and based on video data, respectively; finally, adopt the random forest algorithm for dual-modal classroom atmosphere recognition to determine the classroom atmosphere level. Through a series of comparative experiments, this paper found out that the performance of classroom atmosphere recognition by integrating bimodal data was superior to that of single-modal data, and the disciplinary characteristic was an important influencing factor for classroom atmosphere recognition, and concluded that a multi-dimensional structure can more precisely represent classroom atmosphere, and bimodal data support can more accurately predict classroom atmosphere. The research in this paper can provide effective technical support for precise teaching diagnosis, and hold significant value for optimizing classroom teaching practice.