Optimization of Classroom Interaction Strategies Based on Deep Reinforcement Learning

With the rapid development of information technology, intelligent education has gradually become an important direction of modern education reform. Classroom interaction, as a key factor in improving students' learning and the quality of education, urgently needs new technical means to optimize it. Traditional classroom interaction strategies often have problems such as low student participation and low interaction quality. Therefore, how to optimize classroom interaction through intelligent means has become an important topic in educational research. In this paper, we propose an interaction optimization strategy based on Deep Reinforcement Learning (DRL), which aims to improve the quality of classroom interactions through intelligent algorithms, thus increasing students' engagement and satisfaction in the classroom. In this study, the Proximal Policy Optimization (PPO) algorithm in Deep Reinforcement Learning is used to model variables such as student behaviour, teacher strategy, and classroom state in relation to specific classroom interaction scenarios, and a system framework is constructed that can adaptively adjust the interaction strategy. The experimental data come from 300 students from two universities in Guangdong Province, covering interactive behaviours in several practical training courses.