A reinforcement learning approach to personalized learning recommendation systems
A reinforcement learning approach to personalized learning recommendation systems
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DOI：
10.1111/bmsp.12144
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发表时间：
2019-02-01
影响因子：
2.6
通讯作者：
Ying, Zhiliang
中科院分区：
文献类型：
--
作者：
Tang, Xueying;Chen, Yunxiao;Ying, Zhiliang
个性化学习是指根据每个学习者的需要对学习速度和教学方法进行优化的教学。随着信息技术和数据科学的最新进展，任何拥有个人电脑的人都可以进行个性化学习，并由数据驱动的推荐系统提供支持，该系统可以自动安排学习顺序。这种推荐系统的引擎是一种推荐策略，它基于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。一个强大的引擎可以在基于当前知识的最佳建议和探索可能有回报的新学习轨迹之间取得平衡。制造这样一台发动机是一项具有挑战性的任务。我们在马尔可夫决策框架内提出了这个问题，并提出了一种强化学习方法来解决这个问题。
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.