Student Behavior Recognition and Intervention Methods in Intelligent Classrooms Based on Deep Reinforcement Learning

Abstract

In response to the problem that traditional classroom student behavior analysis relies on manual observation and is difficult to achieve real-time, precise and personalized intervention. This paper constructs an end-to-end intelligent classroom intervention system based on deep reinforcement learning. This system adopts the Multi-Modal Fusion Spatio-Temporal Graph Convolutional Network (MM-ST-GCN), integrating visual skeletons, seat pressure and classroom interaction data, to achieve fine-grained and high-precision recognition of students' classroom behaviors. It models the classroom environment as a Partially Observable Markov Decision Process (POMDP) and uses the improved Soft Actor Critic (SAC) algorithm to generate the optimal intervention strategy that takes into account the learning benefits of students and the intervention costs of teachers. Experiments on the MMAct dataset show that the proposed behavior recognition model achieves 93.8% accuracy and a macro-average F1 score of 0.925. Simulation experiments indicate that the system has the potential to improve students' concentration and reduce distraction behavior. However, the above results are derived from the simulated environment and need to be further verified in the real classroom.