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Scaling up behavioral science interventions in online education

Proceedings of the National Academy of Sciences - PNAS, 2020-06, Vol.117 (26), p.14900-14905 [Peer Reviewed Journal]

Copyright © 2020 the Author(s). Published by PNAS. ;Copyright National Academy of Sciences Jun 30, 2020 ;Copyright © 2020 the Author(s). Published by PNAS. 2020 ;ISSN: 0027-8424 ;EISSN: 1091-6490 ;DOI: 10.1073/pnas.1921417117 ;PMID: 32541050

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  • Title:
    Scaling up behavioral science interventions in online education
  • Author: Kizilcec, René F. ; Reich, Justin ; Yeomans, Michael ; Dann, Christoph ; Brunskill, Emma ; Lopez, Glenn ; Turkay, Selen ; Williams, Joseph Jay ; Tingley, Dustin
  • Subjects: Behavior ; Behavioral sciences ; Behavioral Sciences - methods ; CAI ; Computer assisted instruction ; Continuing education ; Data collection ; Developing countries ; Education ; Education, Distance ; Goals ; Humans ; Internet ; LDCs ; Learning ; Learning algorithms ; Machine learning ; Online instruction ; Social Sciences ; Students ; Students - psychology
  • Is Part Of: Proceedings of the National Academy of Sciences - PNAS, 2020-06, Vol.117 (26), p.14900-14905
  • Description: Online education is rapidly expanding in response to rising demand for higher and continuing education, but many online students struggle to achieve their educational goals. Several behavioral science interventions have shown promise in raising student persistence and completion rates in a handful of courses, but evidence of their effectiveness across diverse educational contexts is limited. In this study, we test a set of established interventions over 2.5 y, with one-quarter million students, from nearly every country, across 247 online courses offered by Harvard, the Massachusetts Institute of Technology, and Stanford. We hypothesized that the interventions would produce medium-to-large effects as in prior studies, but this is not supported by our results. Instead, using an iterative scientific process of cyclically preregistering new hypotheses in between waves of data collection, we identified individual, contextual, and temporal conditions under which the interventions benefit students. Self-regulation interventions raised student engagement in the first few weeks but not final completion rates. Value-relevance interventions raised completion rates in developing countries to close the global achievement gap, but only in courses with a global gap. We found minimal evidence that state-of-the-art machine learning methods can forecast the occurrence of a global gap or learn effective individualized intervention policies. Scaling behavioral science interventions across various online learning contexts can reduce their average effectiveness by an order-of-magnitude. However, iterative scientific investigations can uncover what works where for whom.
  • Publisher: United States: National Academy of Sciences
  • Language: English
  • Identifier: ISSN: 0027-8424
    EISSN: 1091-6490
    DOI: 10.1073/pnas.1921417117
    PMID: 32541050
  • Source: GFMER Free Medical Journals
    MEDLINE
    PubMed Central
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