OPTIMIZATION OF STUDENT CLASSROOM BEHAVIOR RECOGNITION ALGORITHM BASED ON DEEP LEARNING

The identification and analysis of student behaviors in the classroom are beneficial for educators to understand and monitor students' learning dynamics and outcomes. Currently, existing deep learning-based classroom behavior recognition models face issues such as low recognition accuracy, limited generalization capabilities, and a narrow dataset coverage, which adversely affect the effectiveness of educational assessments. To address these challenges, this study collected a classroom behavior dataset comprising data from elementary, middle, and high school students and proposed a DCB-YOLOv11 model for student behavior recognition. This model incorporates deformable convolution (DCNv4) in the backbone and detection head of YOLOv8, along with a redesigned CBAM attention module in the pooling layer. The proposed model achieved an average precision of 92.43%, representing a 2.1% improvement over the baseline model, while also significantly reducing computational overhead. This research, combining mobile networks and educational big data, facilitates the personalized development of intelligent learning environments and enhances the effectiveness of the educational process.