About: Scaling up behavioral science interventions in online education

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Scaling up behavioral science interventions in online education
Creator	Williams, Joseph Brunskill, Emma Dann, Christoph Kizilcec, René Lopez, Glenn Reich, Justin Tingley, Dustin Turkay, Selen Yeomans, Michael
Source	Medline; PMC
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.
has issue date	2020-06-30(xsd:dateTime)
bibo:doi	10.1073/pnas.1921417117
bibo:pmid	32541050
has license	cc-by-nc-nd
sha1sum (hex)	cae45f3de4ae72558a32d60227c91302ef532f0f
schema:url	https://doi.org/10.1073/pnas.1921417117
resource representing a document's title	Scaling up behavioral science interventions in online education
has PubMed Central identifier	PMC7334459
has PubMed identifier	32541050
schema:publication	Proc Natl Acad Sci U S A
resource representing a document's body	covid:cae45f3de4ae72558a32d60227c91302ef532f0f#body_text
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