To What Extent and Under Which Circumstances Are Growth Mind-Sets Important to Academic Achievement? Two Meta-Analyses

To What Extent and Under Which Circumstances Are Growth Mind-Sets Important to Academic Achievement? Two Meta-Analyses

Show all authors
Victoria F. Sisk, Alexander P. Burgoyne, Jingze Sun, ...
First Published March 5, 2018 Research Article 

Abstract

Mind-sets (aka implicit theories) are beliefs about the nature of human attributes (e.g., intelligence). The theory holds that individuals with growth mind-sets (beliefs that attributes are malleable with effort) enjoy many positive outcomes—including higher academic achievement—while their peers who have fixed mind-sets experience negative outcomes. Given this relationship, interventions designed to increase students’ growth mind-sets—thereby increasing their academic achievement—have been implemented in schools around the world. In our first meta-analysis (k = 273, N = 365,915), we examined the strength of the relationship between mind-set and academic achievement and potential moderating factors. In our second meta-analysis (k = 43, N = 57,155), we examined the effectiveness of mind-set interventions on academic achievement and potential moderating factors. Overall effects were weak for both meta-analyses. However, some results supported specific tenets of the theory, namely, that students with low socioeconomic status or who are academically at risk might benefit from mind-set interventions.

Keywords mind-set, implicit theories, education, academic achievement, open data

References

Alferes, V. R. (2012). Methods of randomization in experimental design. Thousand Oaks, CA: Sage. Google Scholar, Crossref
Becker, G. (1986). Correcting the point-biserial correlation for attenuation owing to unequal sample size. The Journal of Experimental Education, 55, 5–8. Google Scholar, Crossref
Bishop, D. V. M., Thompson, P. A. (2016). Problems in using p-curve analysis and text-mining to detect rate of p-hacking and evidential value. PeerJ, 4, Article e1715. doi:10.7717/peerj.1715 Google Scholar, Crossref
Blackwell, L. S., Trzesniewski, K. H., Dweck, C. S. (2007). Implicit theories of intelligence predict achievement across an adolescent transition: A longitudinal study and an intervention. Child Development, 78, 246–263. doi:10.1111/j.1467-8624.2007.00995.x Google Scholar, Crossref, Medline
Boaler, J. (2013). Ability and mathematics: The mindset revolution that is reshaping education. Forum, 55, 143–152. Google Scholar, Crossref
Borenstein, M., Hedges, L., Higgins, J., Rothstein, H. (2005). Comprehensive Meta-Analysis (version 2) [Computer software]. Englewood, NJ: Biostat. Google Scholar
Burnette, J. L., Finkel, E. J. (2012). Buffering against weight gain following dieting setbacks: An implicit theory intervention. Journal of Experimental Social Psychology, 48, 721–725. doi:10.1016/j.jesp.2011.12.020 Google Scholar, Crossref
Burnette, J. L., O’Boyle, E. H., VanEpps, E. M., Pollack, J. M., Finkel, E. J. (2013). Mind-sets matter: A meta-analytic review of implicit theories and self-regulation. Psychological Bulletin, 139, 655–701. Google Scholar, Crossref, Medline
Cheung, S. F., Chan, D. K.-S. (2004). Dependent effect sizes in meta-analysis: Incorporating the degree of interdependence. Journal of Applied Psychology, 89, 780–791. Google Scholar, Crossref, Medline
Cheung, S. F., Chan, D. K.-S. (2008). Dependent correlations in meta-analysis: The case of heterogeneous dependence. Educational and Psychological Measurement, 68, 760–777. doi:10.1177/0013164408315263Google Scholar, Link
Claro, S., Paunesku, D., Dweck, C. S. (2016). Growth mindset tempers the effects of poverty on academic achievement. Proceedings of the National Academy of Sciences, USA, 113, 8664–8668. doi:10.1073/pnas.1608207113 Google Scholar, Crossref, Medline
Dweck, C. S. (2000). Self-theories: Their role in motivation, personality, and development. East Sussex, England: Psychology Press. Google Scholar
Dweck, C. S. (2006). Mindset: The new psychology of success. New York, NY: Random House. Google Scholar
Dweck, C. S. (2008, Winter). Brainology: Transforming students’ motivation to learn. Independent School. Retrieved from https://www.nais.org/magazine/independent-school/winter-2008/braino... Google Scholar
Dweck, C. S. (2012). Mindsets and human nature: Promoting change in the Middle East, the schoolyard, the racial divide, and willpower. American Psychologist, 67, 614–622. doi:10.1037/a0029783 Google Scholar, Crossref, Medline
Dweck, C. S. (2016). The remarkable reach of growth mind-sets. Scientific American Mind, 27, 36–41. doi:10.1038/scientificamericanmind0116-36 Google Scholar, Crossref
Dweck, C. S., Chiu, C. Y., Hong, Y. Y. (1995). Implicit theories and their role in judgments and reactions: A word from two perspectives. Psychological Inquiry, 6, 267–285. Google Scholar, Crossref
Egger, M., Smith, G. D., Schneider, M., Minder, C. (1997). Bias in meta-analysis detected by a simple, graphical test. British Medical Journal, 315, 629–634. Google Scholar, Crossref, Medline
Eisenberg, D. (2005, November 6). How to help them succeed. Time. Retrieved from http://content.time.com/time/magazine/article/0,9171,1126743,00.html Google Scholar
Hattie, J., Biggs, J., Purdie, N. (1996). Effects of learning skills interventions on student learning: A meta-analysis. Review of Educational Research, 66, 99–136. Google Scholar, Link, ISI
Hox, J. (1998). Multilevel modeling: When and why. In Balderjahn, I., Mathar, R., Schader, M. (Eds.), Classification, data analysis, and data highways (pp. 147–154). Berlin, Germany: Springer. Google Scholar, Crossref
Hunter, J. E., Schmidt, F. L. (1990). Dichotomization of continuous variables: The implications for meta-analysis. Journal of Applied Psychology, 75, 334–339. doi:10.1037/0021-9010.75.3.334 Google Scholar, Crossref
Kish, L. (1965). Survey sampling. New York, NY: Wiley. Google Scholar
Melby-Lervåg, M., Redick, T. S., Hulme, C. (2016). Working memory training does not improve performance on measures of intelligence or other measures of “far transfer”: Evidence from a meta-analytic review. Perspectives on Psychological Science, 11, 512–534. doi:10.1177/1745691616635612 Google Scholar, Link, ISI
Mendoza-Denton, R., Kahn, K., Chan, W. (2008). Can fixed views of ability boost performance in the context of favorable stereotypes? Journal of Experimental Social Psychology, 44, 1187–1193. doi:10.1016/j.jesp.2008.03.005 Google Scholar, Crossref
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G. & The PRISMA Group. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLOS Medicine, 6(7), Article e1000097. doi:10.1371/journal.pmed1000097 Google Scholar, Crossref
Paul, A. M. (2013, June 11). The science of smart: Eight ways of looking at intelligence. PBS. Retrieved from http://www.pbs.org/wgbh/nova/blogs/secretlife/blogposts/the-science... Google Scholar
Paunesku, D., Walton, G. M., Romero, C., Smith, E. N., Yeager, D. S., Dweck, C. S. (2015). Mind-set interventions are a scalable treatment for academic underachievement. Psychological Science, 26, 784–793. doi:10.1177/0956797615571017 Google Scholar, Link
Protzko, J. (2015). The environment in raising early intelligence: A meta-analysis of the fadeout effect. Intelligence, 53, 202–210. Google Scholar, Crossref
Raizada, R. D., Kishiyama, M. M. (2010). Effects of socioeconomic status on brain development, and how cognitive neuroscience may contribute to levelling the playing field. Frontiers in Human Neuroscience, 4, Article 3. doi:10.3389/neuro.09.003.2010 Google Scholar, Crossref, Medline
Rattan, A., Savani, K., Chugh, D., Dweck, C. S. (2015). Leveraging mindsets to promote academic achievement: Policy recommendations. Perspectives on Psychological Science, 10, 721–726. doi:10.1177/1745691615599383 Google Scholar, Link, ISI
Romero, C., Master, A., Paunesku, D., Dweck, C. S., Gross, J. J. (2014). Academic and emotional functioning in middle school: The role of implicit theories. Emotion, 14, 227–234. doi:10.1037/a0035490 Google Scholar, Crossref, Medline
Rosenthal, R. (1979). The file drawer problem and tolerance for null results. Psychological Bulletin, 86, 638–641. Google Scholar, Crossref
Simmons, J. P., Simonsohn, U. (2017). Power posing: P-curving the evidence. Psychological Science, 28, 687–693. Google Scholar, Link, ISI
Simonsohn, U., Nelson, L. D., Simmons, J. P. (2014a). P-curve: A key to the file-drawer. Journal of Experimental Psychology: General, 143, 534–547. Google Scholar, Crossref, Medline
Simonsohn, U., Nelson, L. D., Simmons, J. P. (2014b). p-curve and effect size: Correcting for publication bias using only significant results. Perspectives on Psychological Science, 9, 666–681. Google Scholar, Link, ISI
Smith, T. (2014, March 17). Does teaching kids to get ‘gritty’ help them to get ahead? NPR. Retrieved from https://www.npr.org/sections/ed/2014/03/17/290089998/does-teaching-...Google Scholar
Sterne, J. A. C., Sutton, A. J., Ioannidis, J. P. A., Terrin, N., Jones, D. R., Lau, J., , . . . Higgins, J. P. T. (2011). Recommendations for examining and interpreting funnel plot asymmetry in meta-analyses of randomised controlled trials. British Medical Journal, 343, Article d4002. doi:10.1136/bmj.d4002 Google Scholar, Crossref
van Aert, R. C. M., Wicherts, J. M., van Assen, M. A. L. M. (2016). Conducting meta-analyses based on pvalues: Reservations and recommendations for applying p-uniform and p-curve. Perspectives on Psychological Science, 11, 713–729. Google Scholar, Link
Walton, G. M., Cohen, G. L. (2003). Stereotype lift. Journal of Experimental Social Psychology, 39, 456–467. doi:10.1016/S0022-1031(03)00019-2 Google Scholar, Crossref
Williams, R. (2012, May). Moderator analyses: Categorical models and meta-regression. Paper presented at the annual Campbell Collaboration Colloquium, Copenhagen, Denmark. Google Scholar
Yeager, D. S., Dweck, C. S. (2012). Mindsets that promote resilience: When students believe that personal characteristics can be developed. Educational Psychologist, 47, 302–314. doi:10.1080/00461520.2012.722805 Google Scholar, Crossref
Yeager, D. S., Walton, G. M. (2011). Social-psychological interventions in education: They’re not magic. Review of Educational Research, 81, 267–301. doi:10.3102/0034654311405999 Google Scholar, Link, ISI
Yeager, D. S., Walton, G., Cohen, G. L. (2013). Addressing achievement gaps with psychological interventions. Phi Delta Kappan, 94(5), 62–65. Google Scholar, Link, ISI

Views: 178

Comment

You need to be a member of School Leadership 2.0 to add comments!

Join School Leadership 2.0

JOIN SL 2.0

SUBSCRIBE TO

SCHOOL LEADERSHIP 2.0

School Leadership 2.0 is the premier virtual learning community for school leaders from around the globe.  Our community is a subscription based paid service ($19.95/year or only $1.99 per month for a trial membership)  which will provide school leaders with outstanding resources. Learn more about membership to this service by clicking one our links below.

 

Click HERE to subscribe as an individual.

 

Click HERE to learn about group membership (i.e. association, leadership teams)

__________________

CREATE AN EMPLOYER PROFILE AND GET JOB ALERTS AT 

SCHOOLLEADERSHIPJOBS.COM

FOLLOW SL 2.0

© 2024   Created by William Brennan and Michael Keany   Powered by

Badges  |  Report an Issue  |  Terms of Service