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.

Games allow players to perceive themselves in alternate ways in imagined worlds. Player identification is one of the outcomes of gameplay experiences in these worlds and has been shown to affect enjoyment and reduce self-discrepancy. Avatar-based customization has potential to impact player identification by shaping the relationship between the player and the character. This mixed method study aims to fill the gap in the identification literature by examining the effects of avatar-based customization on players' identification with and empathy towards their characters in a massively multiplayer online game, Lord of the Rings Online (LotRO). Participants (N = 66) played LotRO either in customization or in no-customization groups for about ten hours in four sessions over two weeks in a controlled lab setting. Data were collected through interviews, surveys and observations. Results showed both time and avatar-based customization positively impacted players' identification with their avatars. Self-Determination Theory is used to interpret results.

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