Understanding human memory has been a long-standing problem in various scientific disciplines. Early works focused on characterizing human memory using small-scale controlled
experiments and these empirical studies later motivated the design of spaced repetition algorithms for efficient memorization. However, current spaced repetition algorithms are rulebased heuristics with hard-coded parameters, which do not leverage the automated finegrained monitoring and greater degree of control offered by modern online learning platforms.
In this talk, I will present a computational framework to derive optimal spaced repetition algorithms, specially designed to adapt to the learners’ performance. Moreover, I will show the
results of a large-scale natural experiment using data from a popular language-learning online platform, which provides empirical evidence that the spaced repetition algorithms derived using our framework are significantly superior to alternatives.

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