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Scaling up behavioral science interventions in online education

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Title: Scaling up behavioral science interventions in online education
Authors: Kizilcec, RF
Reich, J
Yeomans, M
Dann, C
Brunskill, E
Lopez, G
Turkay, S
Williams, JJ
Tingley, D
Item Type: Journal Article
Abstract: 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.
Issue Date: 30-Jun-2020
Date of Acceptance: 1-Jun-2020
URI: http://hdl.handle.net/10044/1/85126
DOI: 10.1073/pnas.1921417117
ISSN: 0027-8424
Publisher: Proceedings of the National Academy of Sciences
Start Page: 14900
End Page: 14905
Journal / Book Title: Proceedings of the National Academy of Sciences
Volume: 117
Issue: 26
Copyright Statement: © 2020 the Author(s). Published by PNAS. This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).
Publication Status: Published
Online Publication Date: 2020-06-15
Appears in Collections:Imperial College Business School

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