A reinforcement learning approach to personalized learning recommendation systems

Xueying Tang, Yunxiao Chen, Xiaoou Li, Jingchen Liu, Zhiliang Ying

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)108-135
Number of pages28
JournalBritish Journal of Mathematical and Statistical Psychology
Volume72
Issue number1
DOIs
StatePublished - Feb 1 2019

Bibliographical note

Funding Information:
National Science Foundation DMS-1712657, Dr. Xiaoou Li; National Science Foundation SES-1826540, IIS-1633360, US Army Research Office W911NF-15-1-0159, Dr. Jingchen Liu; National Institutes of Health R01GM047845, Dr. Zhiliang Ying.

Keywords

  • Markov decision
  • adaptive learning
  • personalized learning
  • reinforcement learning
  • sequential design

PubMed: MeSH publication types

  • Journal Article

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