A national experiment reveals where a growth mindset improves achievement

被引:0
|
作者
David S. Yeager
Paul Hanselman
Gregory M. Walton
Jared S. Murray
Robert Crosnoe
Chandra Muller
Elizabeth Tipton
Barbara Schneider
Chris S. Hulleman
Cintia P. Hinojosa
David Paunesku
Carissa Romero
Kate Flint
Alice Roberts
Jill Trott
Ronaldo Iachan
Jenny Buontempo
Sophia Man Yang
Carlos M. Carvalho
P. Richard Hahn
Maithreyi Gopalan
Pratik Mhatre
Ronald Ferguson
Angela L. Duckworth
Carol S. Dweck
机构
[1] University of Texas at Austin,
[2] University of California,undefined
[3] Irvine,undefined
[4] Stanford University,undefined
[5] Northwestern University,undefined
[6] Michigan State University,undefined
[7] University of Virginia,undefined
[8] University of Chicago,undefined
[9] Project for Education Research that Scales,undefined
[10] Paradigm Strategy Inc.,undefined
[11] ICF,undefined
[12] Arizona State University,undefined
[13] The Pennsylvania State University,undefined
[14] Harvard University,undefined
[15] University of Pennsylvania,undefined
来源
Nature | 2019年 / 573卷
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摘要
A global priority for the behavioural sciences is to develop cost-effective, scalable interventions that could improve the academic outcomes of adolescents at a population level, but no such interventions have so far been evaluated in a population-generalizable sample. Here we show 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 overall enrolment to advanced mathematics courses in a nationally representative sample of students in secondary education in the United States. Notably, the study identified school contexts that sustained the effects of the growth mindset intervention: the intervention changed grades when peer norms aligned with the messages of the intervention. Confidence in the conclusions of this study comes from independent data collection and processing, pre-registration of analyses, and corroboration of results by a blinded Bayesian analysis.
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页码:364 / 369
页数:5
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