Developmental Stage

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Students from higher–socioeconomic status (SES) backgrounds show a persistent advantage in academic outcomes over lower-SES students. It is possible that students’ beliefs about academic ability, or mindsets, play some role in contributing to these disparities. Data from a recent nationally representative sample of ninth-grade students in U.S. public schools provided evidence that higher SES was associated with fewer fixed beliefs about academic ability (a group difference of .22 standard deviations). Also, there was a negative association between a fixed mindset and grades that was similar regardless of a student’s SES. Finally, student mindsets were a significant but small factor in explaining the existing relationship between SES and achievement. Altogether, mindsets appear to be associated with socioeconomic circumstances and academic achievement; however, the vast majority of the existing socioeconomic achievement gap in the U.S. is likely driven by the root causes of inequality.

This article reports findings from the largest-ever randomized controlled trial of a growth mindset program in the United States in K-12 settings. The study combined a test for cause-and-effect (a randomized experiment) with a sample that enables claims about an entire population (a nationally representative probability sample). The study found that a short (less than one hour), online growth mindset intervention—which teaches that intellectual abilities can be developed—improved grades among lower-achieving students and increased enrollment in advanced mathematics courses among both higher- and lower-achieving students in a nationally representative sample of regular public high schools in the United States. Notably, the study identified school contexts that moderated the effects of the growth mindset intervention: the intervention had a stronger effect on grades when peer norms aligned with the messages of the intervention. In addition to its rigorous design, the study also featured independent data collection and processing, pre-registration of analyses, and corroboration of results by a blinded Bayesian analysis.