Reinforcement Learning with Experience Sharing for Intelligent Educational Systems

With higher education pushing toward larger class sizes, a large portion of current methodology focuses on one-size-fits-all approaches that can effectively educate a large class. However, when these approaches fail, students can be left behind and fail classes due to simple misunderstandings. Inspired by these issues, this paper proposes a modular reinforcement learning system that can be used in intelligent educational systems to inform personalized student support. Based on a similar method detailed in prior work, this paper proposes experience sharing with tutor agents as a computationally light approach to improve reinforcement learning training speed on the task of student support. We also provide preliminary results obtained from student simulations to demonstrate the effectiveness of the proposed method on reinforcement learning agent performance.