Enhancing human learning via spaced repetition optimization
Enhancing human learning via spaced repetition optimization
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DOI：
10.1073/pnas.1815156116
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发表时间：
2019-03-05
影响因子：
11.1
通讯作者：
Gomez-Rodriguez, Manuel
中科院分区：
文献类型：
--
作者：
Tabibian, Behzad;Upadhyay, Utkarsh;Gomez-Rodriguez, Manuel
间隔重复是一种有效记忆的技术，它使用重复复习的内容，遵循由间隔重复算法确定的时间表，以提高长期记忆力。然而，当前的间隔重复算法是简单的基于规则的算法，具有一些硬编码的参数。在这里，我们引入了一个灵活的表示间隔重复使用的框架标记的时间点过程，然后解决设计的间隔重复算法与可证明的保证作为一个最优控制问题的随机微分方程跳跃。对于两个著名的人类记忆模型，我们表明，如果学习者的目标是最大限度地提高回忆概率的内容要学习的审查频率的成本，最佳的审查时间表是由回忆概率本身。因此，我们可以开发一个简单的，可扩展的在线间隔重复算法，MEMORIZE，以确定最佳的复习时间。我们使用流行的语言学习在线平台Duolingo的数据进行了一个大规模的自然实验，结果表明，遵循我们的算法确定的复习时间表的学习者比遵循几种算法确定的替代时间表的学习者更有效地记忆。
Spaced repetition is a technique for efficient memorization which uses repeated review of content following a schedule determined by a spaced repetition algorithm to improve long-term retention. However, current spaced repetition algorithms are simple rule-based heuristics with a few hard-coded parameters. Here, we introduce a flexible representation of spaced repetition using the framework of marked temporal point processes and then address the design of spaced repetition algorithms with provable guarantees as an optimal control problem for stochastic differential equations with jumps. For two well-known human memory models, we show that, if the learner aims to maximize recall probability of the content to be learned subject to a cost on the reviewing frequency, the optimal reviewing schedule is given by the recall probability itself. As a result, we can then develop a simple, scalable online spaced repetition algorithm, MEMORIZE, to determine the optimal reviewing times. We perform a large-scale natural experiment using data from Duolingo, a popular language-learning online platform, and show that learners who follow a reviewing schedule determined by our algorithm memorize more effectively than learners who follow alternative schedules determined by several heuristics.