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Paying Attention to Mindset Measures: A Necessary Step to Move Beyond Mindset Controversies

心态 政治学 工程伦理学 计算机科学 工程类 人工智能
作者
Léa Tân Combette,Deborah Kelemen
标识
DOI:10.31219/osf.io/yr85t
摘要

Since the 1970s, intelligence growth mindset and related interventions have garnered significant attention, particularly in terms of their impact on motivation and learning. However, controversies have arisen since 2017, with reviews highlighting the substantial variability in the efficacy of mindset interventions. In response, significant attention has been paid to social contextual factors (e.g. SES, teachers’ mindset) that might underlie the variability in effects. However, in this paper we argue that an even more fundamental factor has been overlooked: the way mindsets are assessed. To illustrate our point, we focus on three pivotal areas in which mindset measurement can differ across studies (measuring fixed vs. fixed+growth mindset; domain-general vs. domain-specific abilities; and self-theories vs. other-theories). Drawing on original recommendations from Carol Dweck's seminal 1999 work and ones derived from more recent research, we make a set of recommendations about best practices. Using data from a preregistered rapid systematic review of mindset research published since 2017, we then examine the degree to which measurement approaches have varied and the degree to which these best practices are already in common use. Our findings compellingly demonstrate that discrepancies and incongruities in the mindset literature are at least partially attributable to variabilities in how researchers measure mindsets. There is also a clear need for the adoption of a set of best practices so that firmer conclusions about mindset effects can be drawn from future research.
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