Optimizing Human Learning
Optimizing Human Learning
复制标题
优化人类学习
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
M. Gomez
中科院分区：
文献类型：
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作者：
Behzad Tabibian;U. Upadhyay;A. De;Ali Zarezade;B. Scholkopf;M. Gomez
间隔重复是一种有效记忆的技术，它使用重复的、间隔的内容回顾来提高长期记忆能力。我们能否找到最佳的复习时间表，以最大限度地发挥间隔重复的好处？在这篇文章中，我们引入了一种新的，灵活的表示空间重复的标记时点过程的框架，然后将上述问题作为带跳跃的随机微分方程解的最优控制问题。对于两个著名的人类记忆模型，我们证明了最优复习时间表是由待学习内容的回忆概率给出的。因此，我们可以开发一个简单的、可伸缩的在线算法Memory，来采样最佳的复习时间。在流行的在线语言学习平台Duolingo上收集的合成和真实数据上的实验表明，我们的算法可能能够帮助学习者比其他算法更有效地记忆。
Spaced repetition is a technique for efficient memorization which uses repeated, spaced review of content to improve long-term retention. Can we find the optimal reviewing schedule to maximize the benefits of spaced repetition? In this paper, we introduce a novel, flexible representation of spaced repetition using the framework of marked temporal point processes and then address the above question as an optimal control problem for stochastic differential equations with jumps. For two well-known human memory models, we show that the optimal reviewing schedule is given by the recall probability of the content to be learned. As a result, we can then develop a simple, scalable online algorithm, Memorize, to sample the optimal reviewing times. Experiments on both synthetic and real data gathered from Duolingo, a popular language-learning online platform, show that our algorithm may be able to help learners memorize more effectively than alternatives.