Optimal Hierarchical Learning Path Design With Reinforcement Learning

Xiao Li, Hanchen Xu, Jinming Zhang, Hua Hua Chang

Research output: Contribution to journalArticlepeer-review


E-learning systems are capable of providing more adaptive and efficient learning experiences for learners than traditional classroom settings. A key component of such systems is the learning policy. The learning policy is an algorithm that designs the learning paths or rather it selects learning materials for learners based on information such as the learners’ current progresses and skills, learning material contents. In this article, the authors address the problem of finding the optimal learning policy. To this end, a model for learners’ hierarchical skills in the E-learning system is first developed. Based on the hierarchical skill model and the classical cognitive diagnosis model, a framework to model various mastery levels related to hierarchical skills is further developed. The optimal learning path in consideration of the hierarchical structure of skills is found by applying a model-free reinforcement learning method, which does not require any assumption about learners’ learning transition processes. The effectiveness of the proposed framework is demonstrated via simulation studies.

Original languageEnglish (US)
Pages (from-to)54-70
Number of pages17
JournalApplied Psychological Measurement
Issue number1
StatePublished - Jan 2021


  • Markov decision process
  • attribute hierarchy model
  • cognitive diagnostic model
  • hidden Markov model
  • personalized learning
  • reinforcement learning

ASJC Scopus subject areas

  • Social Sciences (miscellaneous)
  • Psychology (miscellaneous)


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