Personalized recommendation system has been widely adopted in E-learning field that is adaptive to each learner’s own learning pace. With full utilization of learning behavior data, psychometric assessment models keep track of the learner’s proficiency on knowledge points, and then, the well-designed recommendation strategy selects a sequence of actions to meet the objective of maximizing learner’s learning efficiency. This article proposes a novel adaptive recommendation strategy under the framework of reinforcement learning. The proposed strategy is realized by the deep Q-learning algorithms, which are the techniques that contributed to the success of AlphaGo Zero to achieve the super-human level in playing the game of go. The proposed algorithm incorporates an early stopping to account for the possibility that learners may choose to stop learning. It can properly deal with missing data and can handle more individual-specific features for better recommendations. The recommendation strategy guides individual learners with efficient learning paths that vary from person to person. The authors showcase concrete examples with numeric analysis of substantive learning scenarios to further demonstrate the power of the proposed method.

中文翻译：

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基于深度Q学习的自适应学习推荐策略。

个性化推荐系统已被广泛应用于电子学习领域，以适应每个学习者自己的学习进度。通过充分利用学习行为数据，心理测评模型可以跟踪学习者在知识点上的熟练程度，然后精心设计的推荐策略选择一系列行动，以实现最大限度地提高学习者学习效率的目标。本文提出了一种在强化学习框架下的新型自适应推荐策略。所提出的策略是通过深度Q学习算法实现的，该算法为AlphaGo Zero的成功实现了实现围棋游戏中的超人水平做出了贡献。所提出的算法结合了提前停止来考虑学习者可能选择停止学习的可能性。它可以正确处理丢失的数据，并可以处理更多针对特定个人的功能以获得更好的建议。推荐策略指导个人学习者使用因人而异的有效学习路径。作者展示了对实际学习情景进行数值分析的具体示例，以进一步证明所提出方法的功能。