Optimal Hierarchical Learning Path Design With Reinforcement Learning

@article{Li2021OptimalHL,
  title={Optimal Hierarchical Learning Path Design With Reinforcement Learning},
  author={Xiao Li and Hanchen Xu and Jinming Zhang and Hua-hua Chang},
  journal={Applied Psychological Measurement},
  year={2021},
  volume={45},
  pages={54 - 70}
}
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… Expand
Deep Reinforcement Learning for Adaptive Learning Systems
TLDR
A model-free deep reinforcement learning algorithm is applied that can effectively find the optimal learning policy from data on learners' learning process without knowing the actual transition model of the learners' continuous latent traits. Expand

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