A Deep Learning-Based System for Monitoring Student Behavior and Analyzing Learning Situations

This text discusses the real-time monitoring of student behavior in the classroom using deep learning technology. By employing DeepSORT to track student postures and recognize interactive behaviors and utilizing the YOLOv8 model to detect student postures, this study constructs an improved deep learning algorithm to establish a classroom teaching evaluation system. Through statistical analysis and real-time monitoring of student classroom states, quantitative assessment criteria have been formulated to accurately evaluate the level of students' concentration. The research results not only provide specific scores for each student's classroom behaviors but also analyze the behavioral characteristics of students and suggest areas for improvement. The study emphasizes the significance of personalized teaching strategies and, based on students' behavior patterns and changes in class, offers tailored behavior correction strategies to enhance the quality of students' attention during lectures.