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.

电子学习系统能够为学习者提供比传统课堂环境更具适应性和效率的学习体验。这种系统的一个关键组成部分是学习策略。学习策略是一种算法，它根据学习者当前的进度和技能、学习材料内容等信息来设计学习路径，或者说是为学习者选择学习材料。在本文中，作者解决了寻找最佳学习策略的问题。为此，首先开发了电子学习系统中学习者分层技能的模型。基于分层技能模型和经典认知诊断模型，进一步开发了一个建模与分层技能相关的各种掌握水平的框架。考虑技能层次结构的最优学习路径是通过应用无模型强化学习方法找到的，该方法不需要对学习者的学习过渡过程进行任何假设。通过模拟研究证明了所提出框架的有效性。