Abstract
We perform a large-scale randomized controlled trial to evaluate the potential of machine learning-based instruction sequencing to improve memorization while allowing the learners the freedom to choose their review times. After controlling for the length and frequency of study, we find that learners for whom a machine learning algorithm determines which questions to include in their study sessions remember the content over ~69% longer. We also find that the sequencing algorithm has an effect on users’ engagement.
Highlights
The greater degree of personalization offered today by learning apps promises to facilitate the design and implementation of automated, data-driven teaching policies that adapt to each learner’s knowledge over time
Research in the computer science literature has been typically focused on finding teaching policies that either enjoy optimality guarantees under simplified mathematical models of the learner’s knowledge[3,4,5,6,7], adapt empirically to learners[8,9,10], or optimize engagement[11,12]
Research in cognitive sciences has focused on measuring the effectiveness of a variety of heuristics to optimize the review times informed by psychologically valid models of the learner’s knowledge using randomized control trials[13–17]
Summary
The greater degree of personalization offered today by learning apps promises to facilitate the design and implementation of automated, data-driven teaching policies that adapt to each learner’s knowledge over time. Rather than optimizing the rate of study as in Tabibian et al, which is typically chosen by the learner, the algorithm determines which questions to include in a learner’s sessions of study over time.
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