Author 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 two-and-a-half years, with ¼ million students, from nearly every country, across 247 online courses offered by Harvard, MIT, 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 pre-registering new hypotheses in between waves of data collection, we identify 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 find minimal evidence that state-of-the-art machine learning methods can forecast the occurrence of a global gap or learn effective individualized intervention policies. Our findings demonstrate how scaling behavioral science interventions across various online learning contexts can reduce their average effectiveness by an order of magnitude and the promise of iterative scientific investigations to uncover what works where for whom.
Paper Information
- Full Working Paper Text
- Working Paper Publication Date: June 2020
- HBS Working Paper Number: Proceedings of the National Academy of Sciences of the United States of America
- Faculty Unit(s): Negotiation, Organizations & Markets