A reinforcement learning approach to personalized learning recommendation systems

• Published in: British journal of mathematical & statistical psychology Vol. 72; no. 1; pp. 108 - 135
• Main Authors: Tang, Xueying, Chen, Yunxiao, Li, Xiaoou, Liu, Jingchen, Ying, Zhiliang
• Format: Journal Article
• Language: English
• Published: England British Psychological Society 01.02.2019
• Subjects: adaptive learning; Algorithms; Computer Simulation; Computer-Assisted Instruction - methods; Decision Making; Educational Status; Humans; Information technology; Learning; Machine learning; Markov Chains; Markov decision; Markov processes; Personal computers; personalized learning; Recommender systems; Reinforcement (Psychology); reinforcement learning; Schedules; sequential design; Software; sequential design; Markov decision; adaptive learning; personalized learning; reinforcement learning
• ISSN: 0007-1102; 2044-8317
• DOI: 10.1111/bmsp.12144

Personalized learning refers to instruction in which the pace of learning and the instructional approach are optimized for the needs of each learner. With the latest advances in information technology and data science, personalized learning is becoming possible for anyone with a personal computer, supported by a data‐driven recommendation system that automatically schedules the learning sequence. The engine of such a recommendation system is a recommendation strategy that, based on data from other learners and the performance of the current learner, recommends suitable learning materials to optimize certain learning outcomes. A powerful engine achieves a balance between making the best possible recommendations based on the current knowledge and exploring new learning trajectories that may potentially pay off. Building such an engine is a challenging task. We formulate this problem within the Markov decision framework and propose a reinforcement learning approach to solving the problem.