Decades of research has shown that spacing practice trials over time can improve later memory, but there are few concrete recommendations concerning how to optimally space practice. We show that existing recommendations are inherently suboptimal due to their insensitivity to time costs and individual- and item-level differences. We introduce an alternative approach that optimally schedules practice with a computational model of spacing in tandem with microeconomic principles. We simulated conventional spacing schedules and our adaptive model-based approach. Simulations indicated that practicing according to microeconomic principles of efficiency resulted in substantially better memory retention than alternatives. The simulation results provided quantitative estimates of optimal difficulty that differed markedly from prior recommendations but still supported a desirable difficulty framework. Experimental results supported simulation predictions, with up to 40% more items recalled in conditions where practice was scheduled optimally according to the model of practice. Our approach can be readily implemented in online educational systems that adaptively schedule practice and has significant implications for millions of students currently learning with educational technology.

数十年的研究表明，随着时间的推移，间隔练习试验可以提高以后的记忆力，但是关于如何优化空间练习的具体建议很少。我们表明，由于现有建议对时间成本不敏感以及个人和项目级别的差异，因此它们在本质上是次优的。我们介绍了一种替代方法，该方法通过与微观经济学原理相结合的间隔计算模型来最佳地安排实践。我们模拟了传统的间隔计划和基于自适应模型的方法。模拟表明，按照效率的微观经济学原理进行练习导致内存保留比其他方法好得多。仿真结果提供了最佳难度的定量估计值，该估计值与先前的建议明显不同，但仍支持理想的难度框架。实验结果支持模拟预测，在根据练习模型对练习进行最佳安排的情况下，最多可以召回40％的项目。我们的方法很容易在在线教育系统中实施，该系统可以自适应地安排练习时间，并且对当前正在学习教育技术的数百万学生具有重大意义。