Large-scale randomized experiments reveals that machine learning-based instruction helps people memorize more effectively

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...

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Bibliographic Details
Published inNPJ science of learning Vol. 6; no. 1; pp. 26 - 3
Main Authors Upadhyay, Utkarsh, Lancashire, Graham, Moser, Christoph, Gomez-Rodriguez, Manuel
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 06.09.2021
Nature Publishing Group
Nature Portfolio
Subjects
631/378/1595
631/378/1595/2167
631/378/1595/2639
Algorithms
Artificial Intelligence
Biomedical and Life Sciences
Brief Communication
Cognitive Psychology
Cognitive Science
Educational Technology
Experiments
Item Analysis
Learner Engagement
Learning algorithms
Life Sciences
Machine learning
Mathematical Models
Mathematical Models of Cognitive Processes and Neural Networks
Memorization
Memory
Neurobiology
Neuropsychology
Neurosciences
Randomized Controlled Trials
Time
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Summary: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.
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ISSN:2056-7936
2056-7936
DOI:10.1038/s41539-021-00105-8