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.

中文翻译：

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个性化学习推荐系统的强化学习方法。

个性化学习是指根据每个学习者的需求优化学习进度和教学方法的教学。随着信息技术和数据科学的最新发展，拥有一台个人计算机的任何人都可以实现个性化学习，并由自动计划学习顺序的数据驱动推荐系统支持。这种推荐系统的引擎是一种推荐策略，它基于来自其他学习者的数据和当前学习者的表现，推荐合适的学习材料以优化某些学习结果。强大的引擎可以在基于当前知识做出最佳建议与探索可能获得回报的新学习轨迹之间取得平衡。制造这种发动机是一项艰巨的任务。我们在马尔可夫决策框架内制定此问题，并提出一种强化学习方法来解决该问题。