An algorithm for dynamic adjustment of personalized education management strategies driven by reinforcement learning

Individualized educational management cannot be avoided in meeting various learner needs in cases where teaching strategies are changed in line with the performance trends and preferences of students. Reinforcement learning provides a viable means of dynamically adjusting the instructional strategies to improve learning and engagement of the student in a real-time setting. On the contrary, traditional learning institutions tend to cling to fixed rules or established processes that do not change with their progression, thus limiting the opportunities to have further improvements. This lack of flexibility can have a negative impact on academic performance, reduce motivation, and raise dropout rates. The current paper presents the Reinforcement Learning-Based Adaptive Strategy Optimization Framework (RL-ASOF), which involves the use of a deep reinforcement learning agent that generates dynamically optimized teaching methods to address these issues. In order to determine the best instructional strategies, the framework analyzes real-time information about students, including engagement measures, test results, and personal learning preferences. The strategy selection of RL-ASOF was further polished after being deployed on an online learning platform and was constantly enhanced by continued interactions with learners. According to the experimental data, the framework increased the learning gains by 27(ppt), engagement by 22(ppt), and the number of tasks completed by 19(ppt) relative to the specific baseline strategies in the evaluated online platform and learner cohort. The average student satisfaction was 4.7 out of 5 in this study context, despite an 89% adaptation accuracy of the reinforcement learning agent. Besides, the system displayed a recommendation delay of only 405 ms, and the dropout rates reduced by 20% in the evaluated platform, thus supporting the efficacy of rl-asof in this context but not claiming broader generalizability.