Item request has been placed! ×
Item request cannot be made. ×
loading  Processing Request

Mindset × Context: Schools, Classrooms, and the Unequal Translation of Expectations into Math Achievement.

Item request has been placed! ×
Item request cannot be made. ×
loading   Processing Request
  • معلومة اضافية
    • المصدر:
      Publisher: Blackwell Country of Publication: United States NLM ID: 7508397 Publication Model: Print Cited Medium: Internet ISSN: 1540-5834 (Electronic) Linking ISSN: 0037976X NLM ISO Abbreviation: Monogr Soc Res Child Dev Subsets: MEDLINE
    • بيانات النشر:
      Publication: <2005->: Malden, MA : Blackwell
      Original Publication: Chicago, Univ. of Chicago Press [etc.]
    • الموضوع:
    • نبذة مختصرة :
      When do adolescents' dreams of promising journeys through high school translate into academic success? This monograph reports the results of a collaborative effort among sociologists and psychologists to systematically examine the role of schools and classrooms in disrupting or facilitating the link between adolescents' expectations for success in math and their subsequent progress in the early high school math curriculum. Our primary focus was on gendered patterns of socioeconomic inequality in math and how they are tethered to the school's peer culture and to students' perceptions of gender stereotyping in the classroom. To do this, this monograph advances Mindset × Context Theory. This orients research on educational equity to the reciprocal influence between students' psychological motivations and their school-based opportunities to enact those motivations. Mindset × Context Theory predicts that a student's mindset will be more strongly linked to developmental outcomes among groups of students who are at risk for poor outcomes, but only in a school or classroom context where there is sufficient need and support for the mindset. Our application of this theory centers on expectations for success in high school math as a foundational belief for students' math progress early in high school. We examine how this mindset varies across interpersonal and cultural dynamics in schools and classrooms. Following this perspective, we ask: 1. Which gender and socioeconomic identity groups showed the weakest or strongest links between expectations for success in math and progress through the math curriculum? 2. How did the school's peer culture shape the links between student expectations for success in math and math progress across gender and socioeconomic identity groups? 3. How did perceptions of classroom gender stereotyping shape the links between student expectations for success in math and math progress across gender and socioeconomic identity groups? We used nationally representative data from about 10,000 U.S. public school 9th graders in the National Study of Learning Mindsets (NSLM) collected in 2015-2016-the most recent, national, longitudinal study of adolescents' mindsets in U.S. public schools. The sample was representative with respect to a large number of observable characteristics, such as gender, race, ethnicity, English Language Learners (ELLs), free or reduced price lunch, poverty, food stamps, neighborhood income and labor market participation, and school curricular opportunities. This allowed for generalization to the U.S. public school population and for the systematic investigation of school- and classroom-level contextual factors. The NSLM's complete sampling of students within schools also allowed for a comparison of students from different gender and socioeconomic groups with the same expectations in the same educational contexts. To analyze these data, we used the Bayesian Causal Forest (BCF) algorithm, a best-in-class machine-learning method for discovering complex, replicable interaction effects. Chapter IV examined the interplay of expectations, gender, and socioeconomic status (SES; operationalized with maternal educational attainment). Adolescents' expectations for success in math were meaningful predictors of their early math progress, even when controlling for other psychological factors, prior achievement in math, and racial and ethnic identities. Boys from low-SES families were the most vulnerable identity group. They were over three times more likely to not make adequate progress in math from 9th to 10th grade relative to girls from high-SES families. Boys from low-SES families also benefited the most from their expectations for success in math. Overall, these results were consistent with Mindset × Context Theory's predictions. Chapters V and VI examined the moderating role of school-level and classroom-level factors in the patterns reported in Chapter IV. Expectations were least predictive of math progress in the highest-achieving schools and schools with the most academically oriented peer norms, that is, schools with the most formal and informal resources. School resources appeared to compensate for lower levels of expectations. Conversely, expectations most strongly predicted math progress in the low/medium-achieving schools with less academically oriented peers, especially for boys from low-SES families. This chapter aligns with aspects of Mindset × Context Theory. A context that was not already optimally supporting student success was where outcomes for vulnerable students depended the most on student expectations. Finally, perceptions of classroom stereotyping mattered. Perceptions of gender stereotyping predicted less progress in math, but expectations for success in math more strongly predicted progress in classrooms with high perceived stereotyping. Gender stereotyping interactions emerged for all sociodemographic groups except for boys from high-SES families. The findings across these three analytical chapters demonstrate the value of integrating psychological and sociological perspectives to capture multiple levels of schooling. It also drew on the contextual variability afforded by representative sampling and explored the interplay of lab-tested psychological processes (expectations) with field-developed levers of policy intervention (school contexts). This monograph also leverages developmental and ecological insights to identify which groups of students might profit from different efforts to improve educational equity, such as interventions to increase expectations for success in math, or school programs that improve the school or classroom cultures.
      (© 2023 The Authors. Monographs of the Society for Research in Child Development published by Wiley Periodicals LLC on behalf of Society for Research in Child Development.)
    • References:
      Adelman, C. (2006). The toolbox revisited: Paths to degree completion from high school through college. U.S. Department of Education.
      Aiken, L. R. (1991). Detecting, understanding, and controlling for cheating on tests. Research in Higher Education, 32(6), 725-736. https://doi.org/10.1007/BF00974740.
      Alan, S., Ertac, S., & Mumcu, I. (2018). Gender stereotypes in the classroom and effects on achievement. The Review of Economics and Statistics, 100(5), 876-890. https://doi.org/10.1162/rest_a_00756.
      Albert, D., Chein, J., & Steinberg, L. (2013). The teenage brain: Peer influences on adolescent decision making. Current Directions in Psychological Science, 22(2), 114-120. https://doi.org/10.1177/0963721412471347.
      Alexander, K., Entwisle, D., & Olson, L. (2014). The long shadow: Family background, disadvantaged urban youth, and the transition to adulthood. Russell Sage Foundation.
      Allen, J., Gregory, A., Mikami, A., Lun, J., Hamre, B., & Pianta, R. (2013). Observations of effective teacher-student interactions in secondary school classrooms: Predicting student achievement with the classroom assessment scoring system-secondary. School Psychology Review, 42(1), 76-98. https://doi.org/10.1080/02796015.2013.12087492.
      Allison, P. D. (2001). Missing data. Sage Publications.
      Andersen, L., & Ward, T. J. (2014). Expectancy-value models for the STEM persistence plans of ninth-grade, high-ability students: A comparison between Black, Hispanic, and White students. Science Education, 98(2), 216-242. https://doi.org/10.1002/sce.21092.
      Andersen, S. C., & Nielsen, H. S. (2016). Reading intervention with a growth mindset approach improves children's skills. Proceedings of the National Academy of Sciences of the United States of America, 113(43), 12111-12113. https://doi.org/10.1073/pnas.1607946113.
      Attewell, P., Lavin, D., Domina, T., & Levey, T. (2007). Passing the torch: Does higher education for the disadvantaged pay off across the generations? Russell Sage Foundation.
      Autor, D. H. (2014). Skills, education, and the rise of earnings inequality among the “other 99 percent”. Science, 344(6186), 843-851.
      Bailey, D. H., Duncan, G. J., Cunha, F., Foorman, B. R., & Yeager, D. S. (2020). Persistence and fade-out of educational-intervention effects: Mechanisms and potential solutions. Psychological Science in the Public Interest, 21(2), 55-97. https://doi.org/10.1177/1529100620915848.
      Bandura, A. (1982). Self-efficacy mechanism in human agency. American Psychologist, 37(2), 122-147. https://doi.org/10.1037/0003-066X.37.2.122.
      Barth, J. M., & Masters, S. (2020). Effects of classroom quality, gender stereotypes, and efficacy on math and science interest over school transitions. International Journal of Gender, Science and Technology, 12(1), 4-31.
      Beasley, M. A., & Fischer, M. J. (2012). Why they leave: The impact of stereotype threat on the attrition of women and minorities from science, math and engineering majors. Social Psychology of Education, 15(4), 427-448. https://doi.org/10.1007/s11218-012-9185-3.
      Beilock, S. L., Gunderson, E. A., Ramirez, G., & Levine, S. C. (2010). Female teachers' math anxiety affects girls' math achievement. Proceedings of the National Academy of Sciences of the United States of America, 107, 1860-1863. https://doi.org/10.1073/pnas.0910967107.
      Belkin, D. (2021). A generation of American men give up on college: ‘I Just Feel Lost’. Wall Street Journal. https://www.wsj.com/articles/college-university-fall-higher-education-men-women-enrollment-admissions-back-to-school-11630948233.
      Benner, A. D., & Graham, S. (2009). The transition to high school as a developmental process among multiethnic urban youth. Child Development, 80(2), 356-376. https://doi.org/10.1111/j.1467-8624.2009.01265.x.
      Bigler, R. S., & Liben, L. S. (2006). A developmental intergroup theory of social stereotypes and prejudice. Advances in Child Development and Behavior, 34, 39-89.
      Black, S. E., Muller, C., Spitz-Oener, A., He, Z., Hung, K., & Warren, J. R. (2021). The importance of STEM: High school knowledge, skills and occupations in an era of growing inequality. Research Policy, 50(7), 104249.
      Bozick, R., Alexander, K., Entwisle, D., Dauber, S., & Kerr, K. (2010). Framing the future: Revisiting the place of educational expectations in status attainment. Social Forces, 88(5), 2027-2052. https://doi.org/10.1353/sof.2010.0033.
      Breda, T., Jouini, E., Napp, C., & Thebault, G. (2020). Gender stereotypes can explain the gender-equality paradox. Proceedings of the National Academy of Sciences of thr United States of America, 117(49), 31063-31069. https://doi.org/10.1073/pnas.2008704117.
      Bronfenbrenner, U. (1981). The ecology of human development: Experiments by nature and design. Harvard University Press.
      Bryan, C. J., Tipton, E., & Yeager, D. S. (2021). Behavioural science is unlikely to change the world without a heterogeneity revolution. Nature Human Behaviour, 5(8), 980-989. https://doi.org/10.1038/s41562-021-01143-3.
      Bryk, A. S., & Schneider, B. (2003). Trust in schools: A core resource for school reform. Educational Leadership, 60(6), 40-45.
      Buchmann, C., DiPrete, T. A., & McDaniel, A. (2008). Gender inequalities in education. Annual Review of Sociology, 34(1), 319-337. https://doi.org/10.1146/annurev.soc.34.040507.134719.
      Budig, M. J., Lim, M., & Hodges, M. J. (2021). Racial and gender pay disparities: The role of education. Social Science Research, 98, 102580.
      Carroll, J. M., Muller, C., Grodsky, E., & Warren, J. R. (2017). Tracking health inequalities from high school to midlife. Social Forces, 96(2), 591-628. https://doi.org/10.1093/sf/sox065.
      Carter, P. L. (2018). Education's limitations and its radical possibilities. Contexts, 17(2), 22-27. https://doi.org/10.1177/1536504218776956.
      Chetty, R., Hendren, N., Kline, P., Saez, E., & Turner, N. (2014). Is the United States still a land of opportunity? Recent trends in intergenerational mobility. American Economic Review, 104(5), 141-147. https://doi.org/10.1257/aer.104.5.141.
      Chin, M. J., Quinn, D. M., Dhaliwal, T. K., & Lovison, V. S. (2020). Bias in the air: A nationwide exploration of teachers' implicit racial attitudes, aggregate bias, and student outcomes. Educational Researcher, 49(8), 566-578.
      Chipman, H. A., George, E. I., & McCulloch, R. E. (2010). BART: Bayesian additive regression trees. The Annals of Applied Statistics, 4(1), 266-298. https://doi.org/10.1214/09-AOAS285.
      Choo, H. Y., & Ferree, M. M. (2010). Practicing intersectionality in sociological research: A critical analysis of inclusions, interactions, and institutions in the study of inequalities. Sociological Theory, 28(2), 129-149. https://doi.org/10.1111/j.1467-9558.2010.01370.x.
      Choukas-Bradley, S., Giletta, M., Widman, L., Cohen, G. L., & Prinstein, M. J. (2014). Experimentally measured susceptibility to peer influence and adolescent sexual behavior trajectories: A preliminary study. Developmental Psychology, 50(9), 2221-2227. https://doi.org/10.1037/a0037300.
      Cialdini, R. B., Kallgren, C. A., & Reno, R. R. (1991). A focus theory of normative conduct: A theoretical refinement and reevaluation of the role of norms in human behavior. Advances in Experimental Social Psychology, 24, 201-234.
      Cimpian, A., Mu, Y., & Erickson, L. C. (2012). Who is good at this game? Linking an activity to a social category undermines children's achievement. Psychological Science, 23(5), 533-541. https://doi.org/10.1177/0956797611429803.
      Cimpian, A., & Salomon, E. (2014). The inherence heuristic: An intuitive means of making sense of the world, and a potential precursor to psychological essentialism. Behavioral and Brain Sciences, 37(5), 461-480. https://doi.org/10.1017/S0140525X13002197.
      Clark, K. B., & Clark, M. P. (1947). Racial identification and preference in negro children. In Readings in social psychology (pp. 169-178). Henry Holt and Company.
      Coleman, J. S. (1961). The adolescent society (p. xvi, 368). Free Press of Glencoe.
      Coleman, J. S., & Hoffer, T. (1987). Public and private high schools: The impact of communities. Basic Books.
      Conger, D., & Long, M. C. (2010). Why are men falling behind? Gender gaps in college performance and persistence. The Annals of the American Academy of Political and Social Science, 627(1), 184-214. https://doi.org/10.1177/0002716209348751.
      Crosnoe, R. (2009). Low-income students and the socioeconomic composition of public high schools. American Sociological Review, 74(5), 709-730. https://doi.org/10.1177/000312240907400502.
      Crosnoe, R. (2011). Fitting in, standing out: Navigating the social challenges of high school to get an education. Cambridge University Press.
      Crosnoe, R., & Huston, A. C. (2007). Socioeconomic status, schooling, and the developmental trajectories of adolescents. Developmental Psychology, 43(5), 1097.
      Crosnoe, R., & Muller, C. (2014). Family socioeconomic status, peers, and the path to college. Social Problems, 61(4), 602-624. https://doi.org/10.1525/sp.2014.12255.
      Crosnoe, R., Riegle-Crumb, C., Field, S., Frank, K., & Muller, C. (2008). Peer group contexts of girls' and boys' academic experiences. Child Development, 79(1), 139-155. https://doi.org/10.1111/j.1467-8624.2007.01116.x.
      Crosnoe, R., & Schneider, B. (2010). Social capital, information, and socioeconomic disparities in math course work. American Journal of Education, 117(1), 79-107. https://doi.org/10.1086/656347.
      Crum, A. J., Jamieson, J. P., & Akinola, M. (2020). Optimizing stress: An integrated intervention for regulating stress responses. Emotion, 20(1), 120-125. https://doi.org/10.1037/emo0000670.
      Crum, A. J., Salovey, P., & Achor, S. (2013). Rethinking stress: The role of mindsets in determining the stress response. Journal of Personality and Social Psychology, 104(4), 716-733. https://doi.org/10.1037/a0031201.
      Cvencek, D., Meltzoff, A. N., & Greenwald, A. G. (2011). Math-gender stereotypes in elementary school children. Child Development, 82(3), 766-779.
      Dahl, R. E., Allen, N. B., Wilbrecht, L., & Suleiman, A. B. (2018). Importance of investing in adolescence from a developmental science perspective. Nature, 554(7693), 441-450. https://doi.org/10.1038/nature25770.
      Damon, W., Menon, J., & Bronk, K. C. (2003). The development of purpose during adolescence. Applied Developmental Science, 7(3), 119-128. https://doi.org/10.1207/S1532480XADS0703_2.
      Destin, M., Hanselman, P., Buontempo, J., Tipton, E., & Yeager, D. S. (2019). Do student mindsets differ by socioeconomic status and explain disparities in academic achievement in the United States? AERA Open, 5(3), 1-12. https://doi.org/10.1177/2332858419857706.
      DiPrete, T. A., & Buchmann, C. (2013). The rise of women: The growing gender gap in education and what it means for American schools. Russell Sage Foundation.
      Domina, T., Penner, A., & Penner, E. (2017). Categorical inequality: Schools as sorting machines. Annual Review of Sociology, 43, 311-330.
      Domina, T., & Saldana, J. (2012). Does raising the bar level the playing field?: Mathematics curricular intensification and inequality in american high schools, 1982-2004. American Educational Research Journal, 49(4), 685-708. https://doi.org/10.3102/0002831211426347.
      Dorie, V., Hill, J., Shalit, U., Scott, M., & Cervone, D. (2019). Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competition. Statistical Science, 34(1), 43-68. https://doi.org/10.1214/18-STS667.
      Douglas, D., & Attewell, P. (2017). School mathematics as gatekeeper. The Sociological Quarterly, 58(4), 648-669. https://doi.org/10.1080/00380253.2017.1354733.
      Downey, D. B., & Condron, D. J. (2016). Fifty years since the Coleman Report: Rethinking the relationship between schools and inequality. Sociology of Education, 89(3), 207-220. https://doi.org/10.1177/0038040716651676.
      Duncan, G. J., & Murnane, R. J. (2011). Whither opportunity?: Rising inequality, schools, and children's life chances. Russell Sage Foundation.
      Dweck, C. S. (2006). Mindset: The new psychology of success. Random House.
      Dweck, C. S. (2017). From needs to goals and representations: Foundations for a unified theory of motivation, personality, and development. Psychological Review, 124(6), 689-719. https://doi.org/10.1037/rev0000082.
      Dweck, C. S., & Yeager, D. S. (2019). Mindsets: A view from two eras. Perspectives on Psychological Science, 14(3), 481-496. https://doi.org/10.1177/1745691618804166.
      Eccles, J. S. (2005). Studying gender and ethnic differences in participation in math, physical science, and information technology. New Directions for Child and Adolescent Development, 2005(110), 7-14. https://doi.org/10.1002/cd.146.
      Eccles, J. S. (2009). Who am I and what am I going to do with my life? Personal and collective identities as motivators of action. Educational Psychologist, 44(2), 78-89. https://doi.org/10.1080/00461520902832368.
      Eccles, J. S., & Wigfield, A. (2002). Motivational beliefs, values, and goals. Annual Review of Psychology, 53, 109-132. https://doi.org/10.1146/annurev.psych.53.100901.135153.
      Eccles, J. S., & Wigfield, A. (2020). From expectancy-value theory to situated expectancy-value theory: A developmental, social cognitive, and sociocultural perspective on motivation. Contemporary Educational Psychology, 61, 101859. https://doi.org/10.1016/j.cedpsych.2020.101859.
      Elliot, A. J., & Church, M. A. (1997). A hierarchical model of approach and avoidance achievement motivation. Journal of Personality and Social Psychology, 72(1), 218.
      Elliott, E. S., & Dweck, C. S. (1988). Goals: An approach to motivation and achievement. Journal of Personality and Social Psychology, 54(1), 5-12. https://doi.org/10.1037//0022-3514.54.1.5.
      Fahle, E. M., Reardon, S. F., Kalogrides, D., Weathers, E. S., & Jang, H. (2020). Racial segregation and school poverty in the United States, 1999-2016. Race and Social Problems, 12(1), 42-56. https://doi.org/10.1007/s12552-019-09277-w.
      Ferguson, R. F., & Danielson, C. (2015). How framework for teaching and Tripod 7Cs evidence distinguish key components of effective teaching. In T. J. Kane, K. A. Kerr, & R. C. Pianta (Eds.), Designing teacher evaluation systems (pp. 98-143). John Wiley & Sons, Ltd. https://doi.org/10.1002/9781119210856.ch4.
      Fischer, C. S., & Hout, M. (2006). Century of difference. Russell Sage Foundation.
      Foley, A. E., Herts, J. B., Borgonovi, F., Guerriero, S., Levine, S. C., & Beilock, S. L. (2017). The math anxiety-performance link: A global phenomenon. Current Directions in Psychological Science, 26(1), 52-58. https://doi.org/10.1177/0963721416672463.
      Frank, K. A., Muller, C., Schiller, K. S., Riegle-Crumb, C., Mueller, A. S., Crosnoe, R., & Pearson, J. (2008). The social dynamics of mathematics coursetaking in high school. American Journal of Sociology, 113(6), 1645-1696. https://doi.org/10.1086/587153.
      Gelman, A. (2006). Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper). Bayesian Analysis, 1(3), 515-534. https://doi.org/10.1214/06-BA117A.
      Gopalan, M., & Tipton, E. (2018). Is the national study of learning mindsets nationally-representative? https://Psyarxiv.Com/Dvmr7/.
      Gopnik, A., & Wellman, H. M. (2012). Reconstructing constructivism: Causal models, Bayesian learning mechanisms, and the theory. Psychological Bulletin, 138(6), 1085-1108. https://doi.org/10.1037/a0028044.
      Gordon, R. A., Crosnoe, R., & Wang, X. (2013). Physical attractiveness and the accumulation of social and human capital in adolescence and young adulthood: Assets and distractions. Monographs of the Society for Research in Child Development, 78(6), 1-137. https://doi.org/10.1002/mono.12060.
      Hahn, P. R., Carvalho, C. M., Puelz, D., & He, J. (2018). Regularization and Confounding in Linear Regression for Treatment Effect Estimation. Bayesian Analysis, 13(1), 163-182. https://doi.org/10.1214/16-BA1044.
      Hahn, P. R., Dorie, V., & Murray, J. S. (2019). Atlantic causal inference conference (ACIC) data analysis challenge 2017. ArXiv Preprint. ArXiv:1905.09515.
      Hahn, P. R., Murray, J. S., & Carvalho, C. M. (2020). Bayesian regression tree models for causal inference: Regularization, confounding, and heterogeneous effects. Bayesian Analysis, 15(3), 965-1056. https://doi.org/10.1214/19-BA1195.
      Hanselman, P., & Fiel, J. E. (2017). School opportunity hoarding? Racial segregation and access to high growth schools. Social Forces, 95(3), 1077-1104. https://doi.org/10.1093/sf/sow088.
      Harackiewicz, J. M., Canning, E. A., Tibbetts, Y., Priniski, S. J., & Hyde, J. S. (2016). Closing achievement gaps with a utility-value intervention: Disentangling race and social class. Journal of Personality and Social Psychology, 111, 745-765. https://doi.org/10.1037/pspp0000075.
      Harter, S., & Pike, R. (1984). The pictorial scale of perceived competence and social acceptance for young children. Child Development, 55(6), 1969-1982. https://doi.org/10.2307/1129772.
      Hartley, B. L., & Sutton, R. M. (2013). A stereotype threat account of boys' academic underachievement. Child Development, 84(5), 1716-1733. https://doi.org/10.1111/cdev.12079.
      Hattie, J. (2012). Visible learning for teachers: Maximizing impact on learning. Routledge.
      Hecht, C. A., Dweck, C. S., Murphy, M. C., Kroeper, K. M., & Yeager, D. S. (2023). Efficiently exploring the causal role of contextual moderators in behavioral science. Proceedings of the National Academy of Sciences of the United States of America, 120(1), e2216315120. https://doi.org/10.1073/pnas.2216315120.
      Hecht, C. A., Yeager, D. S., Dweck, C. S., & Murphy, M. C. (2021). Beliefs, affordances, and adolescent development: Lessons from a decade of growth mindset interventions. In J. J. Lockman (Ed.), Advances in child development and behavior (Vol. 61, pp. 169-197). JAI. https://doi.org/10.1016/bs.acdb.2021.04.004.
      Helms, S. W., Choukas-Bradley, S., Widman, L., Giletta, M., Cohen, G. L., & Prinstein, M. J. (2014). Adolescents misperceive and are influenced by high-status peers' health risk, deviant, and adaptive behavior. Developmental Psychology, 50(12), 2697-2714. https://doi.org/10.1037/a0038178.
      Heyder, A., & Kessels, U. (2013). Is school feminine? Implicit gender stereotyping of school as a predictor of academic achievement. Sex Roles, 69(11), 605-617. https://doi.org/10.1007/s11199-013-0309-9.
      Heyder, A., & Kessels, U. (2017). Boys Don't Work? On the Psychological Benefits of Showing Low Effort in High School. Sex Roles, 77(1), 72-85. https://doi.org/10.1007/s11199-016-0683-1.
      Hill, J., Linero, A., & Murray, J. (2020). Bayesian Additive Regression Trees: A Review and Look Forward. Annual Review of Statistics and Its Application, 7(1), 251-278. https://doi.org/10.1146/annurev-statistics-031219-041110.
      Hill, P. L., Burrow, A. L., Brandenberger, J. W., Lapsley, D. K., & Quaranto, J. C. (2010). Collegiate purpose orientations and well-being in early and middle adulthood. Journal of Applied Developmental Psychology, 31(2), 173-179. https://doi.org/10.1016/j.appdev.2009.12.001.
      Hout, M. (2012). Social and economic returns to college education in the United States. Annual Review of Sociology, 38(1), 379-400. https://doi.org/10.1146/annurev.soc.012809.102503.
      Hulleman, C. S., & Harackiewicz, J. M. (2009). Promoting interest and performance in high school science classes. Science, 326(5958), 1410-1412. https://doi.org/10.1126/science.1177067.
      Ingels, S. J., Pratt, D. J., Herget, D. R., Bryan, M., Fritch, L. B., Ottem, R., Rogers, J. E., & Wilson, D. (2015). High school longitudinal study of 2009 (HSLS: 09) 2013 update and high school transcript (p. 154).
      Institute of Education Sciences. (2020). The condition of education 2020. Institute of Education Sciences. https://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2020144.
      Irizarry, Y. (2021). On track or derailed? Race, advanced math, and the transition to high school. Socius, 7, 2378023120980293. https://doi.org/10.1177/2378023120980293.
      Jiang, S., Simpkins, S. D., & Eccles, J. S. (2020). Individuals' math and science motivation and their subsequent STEM choices and achievement in high school and college: A longitudinal study of gender and college generation status differences. Developmental Psychology, 56(11), 2137-2151. https://doi.org/10.1037/dev0001110.
      Kalev, A. (2014). How you downsize is who you downsize: Biased formalization, accountability, and managerial diversity. American Sociological Review, 79(1), 109-135.
      King, D. K. (1988). Multiple jeopardy, multiple consciousness: The context of a Black feminist ideology. Signs: Journal of Women in Culture and Society, 14(1), 42-72.
      Kohli, R., Pizarro, M., & Nevárez, A. (2017). The “New Racism” of K-12 Schools: Centering Critical Research on Racism. Review of Research in Education, 41(1), 182-202. https://doi.org/10.3102/0091732X16686949.
      Langenkamp, A. G. (2010). Academic vulnerability and resilience during the transition to high school: The role of social relationships and district context. Sociology of Education, 83(1), 1-19. https://doi.org/10.1177/0038040709356563.
      Lareau, A. (2011). Unequal childhoods: Class, race, and family life. University of California Press.
      Lauermann, F., Tsai, Y.-M., & Eccles, J. (2017). Math-related career aspirations and choices within Eccles et al's expectancy-value theory of achievement-related behaviors. Developmental Psychology, 53(8), 1540-1559. https://doi.org/10.1037/dev0000367.
      Leslie, S.-J., Cimpian, A., Meyer, M., & Freeland, E. (2015). Expectations of brilliance underlie gender distributions across academic disciplines. Science, 347(6219), 262-265. https://doi.org/10.1126/science.1261375.
      Lewis, A. E., & Diamond, J. B. (2015). Despite the best intentions: How racial inequality thrives in good schools. Oxford University Press.
      Liaw, A., Wiener, M., Breiman, L., & Cutler, A. (2015). Package ‘randomForest’.
      Lucas, S. R. (2001). Effectively maintained inequality: Education transitions, track mobility, and social background effects. American Journal of Sociology, 106(6), 1642-1690. https://doi.org/10.1086/321300.
      Macnamara, B. (2018). Schools are buying “growth mindset” interventions despite scant evidence that they work well. The Conversation. http://theconversation.com/schools-are-buying-growth-mindset-interventions-despite-scant-evidence-that-they-work-well-96001.
      Marsh, H. W. (1990). The structure of academic self-concept: The Marsh/Shavelson model. Journal of Educational Psychology, 82(4), 623-636. https://doi.org/10.1037/0022-0663.82.4.623.
      Master, A., Meltzoff, A. N., & Cheryan, S. (2021). Gender stereotypes about interests start early and cause gender disparities in computer science and engineering. Proceedings of the National Academy of Sciences, 118(48), e2100030118.
      McConnell, K. J., & Lindner, S. (2019). Estimating treatment effects with machine learning. Health Services Research, 54(6), 1273-1282. https://doi.org/10.1111/1475-6773.13212.
      McGrew, W. (2019). U.S. school segregation in the 21st century. Equitable Growth. http://www.equitablegrowth.org/research-paper/u-s-school-segregation-in-the-21st-century/.
      Meyer, M., Cimpian, A., & Leslie, S.-J. (2015). Women are underrepresented in fields where success is believed to require brilliance. Frontiers in Psychology, 6, 235. https://doi.org/10.3389/fpsyg.2015.00235.
      Mijs, J. J. B., & Roe, E. L. (2021). Is America coming apart? Socioeconomic segregation in neighborhoods, schools, workplaces, and social networks, 1970-2020. Sociology Compass, 15(6), e12884. https://doi.org/10.1111/soc4.12884.
      Miller, D. I., Eagly, A. H., & Linn, M. C. (2015). Women's representation in science predicts national gender-science stereotypes: Evidence from 66 nations. Journal of Educational Psychology, 107(3), 631-644. https://doi.org/10.1037/edu0000005.
      Miller, E. B., Farkas, G., Vandell, D. L., & Duncan, G. J. (2014). Do the effects of head start vary by parental preacademic stimulation? Child Development, 85(4), 1385-1400. https://doi.org/10.1111/cdev.12233.
      Morgan, S. L. (2005). On the edge of commitment: Educational attainment and race in the United States. Stanford University Press.
      Mouw, T., & Kalleberg, A. L. (2010). Occupations and the Structure of Wage Inequality in the United States, 1980s to 2000s. American Sociological Review, 75(3), 402-431. https://doi.org/10.1177/0003122410363564.
      Muenks, K., Wigfield, A., & Eccles, J. S. (2018). I can do this! The development and calibration of children's expectations for success and competence beliefs. Developmental Review, 48, 24-39. https://doi.org/10.1016/j.dr.2018.04.001.
      Murphy, M. C., Fryberg, S. A., Brady, L. M., Canning, E. A., & Hecht, C. A. (2021). Global mindset initiative paper 1: Growth mindset cultures and teacher practices. Yidan Prize Foundation.
      Murphy, M. C., Gopalan, M., Carter, E. R., Emerson, K. T. U., Bottoms, B. L., & Walton, G. M. (2020). A customized belonging intervention improves retention of socially disadvantaged students at a broad-access university. Science Advances, 6, eaba4677. https://doi.org/10.1126/sciadv.aba4677.
      National Forum on Education Statistics. (2014). Forum guide to school courses for the exchange of data (SCED) classification system. U.S. Department of Education. National Center for Education Statistics.
      National Science Foundation. (2020). Stem education for the future: A visioning report.
      Nosek, B. A., Smyth, F., Sriram, N., Lindner, N., Devos, T., Ayala, A., Bar-Anan, Y., Bergh, R., Cai, H., Gonsalkorale, K., Kesebir, S., Maliszewski, N., Neto, F., Olli, E., Park, J., Schnabel, K., Shiomura, K., Tudor Tulbure, B., Wiers, R., … Greenwald, A. (2009). National differences in gender-science stereotypes predict national sex differences in science and math achievement. Proceedings of the National Academy of Sciences of the United States of America, 106(26), 10593-10597. https://doi.org/10.1073/pnas.0809921106.
      Paluck, E. L., & Shepherd, H. (2012). The salience of social referents: A field experiment on collective norms and harassment behavior in a school social network. Journal of Personality and Social Psychology, 103(6), 899-915. https://doi.org/10.1037/a0030015.
      Paschall, K. W., Gershoff, E. T., & Kuhfeld, M. (2018). A two decade examination of historical race/ethnicity disparities in academic achievement by poverty status. Journal of Youth and Adolescence, 47(6), 1164-1177. https://doi.org/10.1007/s10964-017-0800-7.
      Penner, A. M. (2015). Gender inequality in science. Science, 347(6219), 234-235. https://doi.org/10.1126/science.aaa3781.
      Penner, A. M., & Paret, M. (2008). Gender differences in mathematics achievement: Exploring the early grades and the extremes. Social Science Research, 37(1), 239-253. https://doi.org/10.1016/j.ssresearch.2007.06.012.
      Porter, T., Molina, D., Cimpian, A., Roberts, S., Fredericks, A., Blackwell, L., & Trzesniewski, K. (2021). Growth mindset intervention delivered by teachers boosts achievement in early adolescence. Psychological Science, 33, 1086-1096.
      Quillian, L. (2014). Does segregation create winners and losers? Residential segregation and inequality in educational attainment. Social Problems, 61(3), 402-426. https://doi.org/10.1525/sp.2014.12193.
      Reardon, S. F. (2011). The widening academic achievement gap between the rich and the poor: New evidence and possible explanations. In G. J. Duncan & R. J. Murnane (Eds.), Whither opportunity?: Rising inequality, schools, and children's life chances (pp. 91-116). Russell Sage Foundation.
      Reardon, S. F., Fahle, E. M., Kalogrides, D., Podolsky, A., & Zárate, R. C. (2019). Gender achievement gaps in U.S. school districts. American Educational Research Journal, 56(6), 2474-2508. https://doi.org/10.3102/0002831219843824.
      Reardon, S. F., & Robinson, J. P. (2008). Patterns and trends in racial/ethnic and socioeconomic academic achievement gaps. Handbook of Research in Education Finance and Policy (pp. 497-516). Taylor and Francis.
      Reeves, S. L., Henderson, M. D., Cohen, G. L., Steingut, R. R., Hirschi, Q., & Yeager, D. S. (2020). Psychological affordances help explain where a self-transcendent purpose intervention improves performance. Journal of Personality and Social Psychology, 120, 1-15. https://doi.org/10.1037/pspa0000246.
      Rege, M., Hanselman, P., Solli, I. F., Dweck, C. S., Ludvigsen, S., Bettinger, E., Crosnoe, R., Muller, C., Walton, G., Duckworth, A., & Yeager, D. S. (2020). How can we inspire nations of learners? An investigation of growth mindset and challenge-seeking in two countries. American Psychologist, 76, 755-767. https://doi.org/10.1037/amp0000647.
      Riegle-Crumb, C. (2006). The path through math: Course sequences and academic performance at the intersection of race-ethnicity and gender. American Journal of Education, 113(1), 101-122. https://doi.org/10.1086/506495.
      Riegle-Crumb, C., & Grodsky, E. (2010). Racial-ethnic differences at the intersection of math course-taking and achievement. Sociology of Education, 83(3), 248-270. https://doi.org/10.1177/0038040710375689.
      Riegle-Crumb, C., & Humphries, M. (2012). Exploring bias in math teachers' perceptions of students' ability by gender and race/ethnicity. Gender & Society, 26(2), 290-322. https://doi.org/10.1177/0891243211434614.
      Riegle-Crumb, C., Kyte, S. B., & Morton, K. (2018). Gender and racial/ethnic differences in educational outcomes: Examining patterns, explanations, and new directions for research. In B. Schneider (Ed.), Handbook of the Sociology of Education in the 21st Century (pp. 131-152). Springer International Publishing. https://doi.org/10.1007/978-3-319-76694-2_6.
      Riegle-Crumb, C., & Morton, K. (2017). Gendered expectations: Examining how peers shape female students' intent to pursue stem fields. Frontiers in Psychology, 8, 329. https://doi.org/10.3389/fpsyg.2017.00329.
      Riegle-Crumb, C., & Peng, M. (2021). Examining high school students' gendered beliefs about math: Predictors and implications for choice of stem college majors. Sociology of Education, 94(3), 227-248. https://doi.org/10.1177/00380407211014777.
      Rogers, T., & Feller, A. (2016). Discouraged by peer excellence: Exposure to exemplary peer performance causes quitting. Psychological Science, 27(3), 365-374.
      Rose, H., & Betts, J. R. (2004). The effect of high school courses on earnings. The Review of Economics and Statistics, 86(2), 497-513. https://doi.org/10.1162/003465304323031076.
      Saujani, R. (2017). Girls who code: Learn to code and change the world. Penguin.
      Schiller, K. S. (1999). Effects of feeder patterns on students' transition to high school. Sociology of Education, 72(4), 216-233. https://doi.org/10.2307/2673154.
      Schneider, B., & Stevenson, D. (1999). The ambitious generation: America's teenagers, motivated but directionless. Yale University Press.
      Schunk, D. H., & DiBenedetto, M. K. (2021). Chapter four-Self-efficacy and human motivation. In A. J. Elliot (Ed.), Advances in Motivation Science (Vol. 8, pp. 153-179). Elsevier. https://doi.org/10.1016/bs.adms.2020.10.001.
      Schwarzer, R., & Warner, L. M. (2013). Perceived self-efficacy and its relationship to resilience. In S. Prince-Embury & D. Saklofske (Eds.), Resilience in children, adolescents, and adults (pp. 139-150). Springer.
      Sharkey, P. (2013). Stuck in place: Urban neighborhoods and the end of progress toward racial equality. University of Chicago Press. https://press.uchicago.edu/ucp/books/book/chicago/S/bo14365260.html.
      Spencer, S. J., Steele, C. M., & Quinn, D. M. (1999). Stereotype threat and women's math performance. Journal of Experimental Social Psychology, 35(1), 4-28. https://doi.org/10.1006/jesp.1998.1373.
      Starling, J. E., Murray, J. S., Carvalho, C. M., Bukowski, R. K., & Scott, J. G. (2020). Bart with targeted smoothing: An analysis of patient-specific stillbirth risk. Annals of Applied Statistics, 14(1), 28-50.
      Starling, J. E., Murray, J. S., Lohr, P. A., Aiken, A. R. A., Carvalho, C. M., & Scott, J. G. (2020). Targeted smooth Bayesian causal forests: An analysis of heterogeneous treatment effects for simultaneous versus interval medical abortion regimens over gestation. ArXiv:1905.09405 [Stat]. http://arxiv.org/abs/1905.09405.
      Steele, C. M. (1997). A threat in the air: How stereotypes shape intellectual identity and performance. American Psychologist, 52(6), 613-629. https://doi.org/10.1037/0003-066X.52.6.613.
      Stephens, N. M., Markus, H. R., & Phillips, L. T. (2014). Social class culture cycles: How three gateway contexts shape selves and fuel inequality. Annual Review of Psychology, 65(1), 611-634. https://doi.org/10.1146/annurev-psych-010213-115143.
      Stinebrickner, R., & Stinebrickner, T. (2014). Academic performance and college dropout: Using longitudinal expectations data to estimate a learning model. Journal of Labor Economics, 32(3), 601-644. https://doi.org/10.1086/675308.
      Szaszi, B., Higney, A., Charlton, A., Gelman, A., Ziano, I., Aczel, B., Goldstein, D. G., Yeager, D. S., & Tipton, E. (2022). No reason to expect large and consistent effects of nudge interventions. Proceedings of the National Academy of Sciences of the United States of America, 119(31), e2200732119. https://doi.org/10.1073/pnas.2200732119.
      Tipton, E. (2014). How generalizable is your experiment? An index for comparing experimental samples and populations. Journal of Educational and Behavioral Statistics, 39(6), 478-501. https://doi.org/10.3102/1076998614558486.
      Tipton, E., Yeager, D. S., Iachan, R., & Schneider, B. (2019). Designing probability samples to study treatment effect heterogeneity. In P. J. Lavrakas (Ed.), Experimental methods in survey research: Techniques that combine random sampling with random assignment (pp. 435-456). Wiley. https://doi.org/10.1002/9781119083771.ch22.
      Tyson, W., & Roksa, J. (2016). How schools structure opportunity: The role of curriculum and placement in math attainment. Research in Social Stratification and Mobility, 44, 124-135. https://doi.org/10.1016/j.rssm.2016.04.003.
      Walton, G. M., & Cohen, G. L. (2003). Stereotype lift. Journal of Experimental Social Psychology, 39, 456-467. https://doi.org/10.1016/S0022-1031(03)00019-2.
      Walton, G. M., & Cohen, G. L. (2007). A question of belonging: Race, social fit, and achievement. Journal of Personality and Social Psychology, 92(1), 82-96. https://doi.org/10.1037/0022-3514.92.1.82.
      Walton, G. M., & Cohen, G. L. (2011). A brief social-belonging intervention improves academic and health outcomes of minority students. Science, 331(6023), 1447-1451. https://doi.org/10.1126/science.1198364.
      Walton, G. M., Logel, C., Peach, J. M., Spencer, S. J., & Zanna, M. P. (2015). Two brief interventions to mitigate a “chilly climate” transform women's experience, relationships, and achievement in engineering. Journal of Educational Psychology, 107(2), 468-485.
      Walton, G. M., Murphy, M. C., Logel, C., Yeager, D. S., Goyer, J. P., Brady, S. T., Paunesku, D., Fotuhi, O., Blodorn, A., Boucher, K. L., Carter, E. R., Gopalan, M., Henderson, A., Kroeper, K. M., Murdock-Perriera, L. A., Ablorh, T., Chen, S., Fisher, P., Galvan, M., … Krol, N. (2023). Where and with whom does a brief social-belonging intervention promote progress in college? Science. 380(664): 499-505.
      Walton, G. M., & Wilson, T. D. (2018). Wise interventions: Psychological remedies for social and personal problems. Psychological Review, 125(5), 617-655. https://doi.org/10.1037/rev0000115.
      Walton, G. M., & Yeager, D. S. (2020). Seed and soil: Psychological affordances in contexts help to explain where wise interventions succeed or fail. Current Directions in Psychological Science, 29(3), 219-226. https://doi.org/10.1177/0963721420904453.
      Warren, E., & Supreme Court Of The United States. (1953). U.S. reports: Brown v. Board of Education (347 U.S. 483). The Library of Congress. https://www.loc.gov/item/usrep347483/.
      Weininger, E. B., Lareau, A., & Conley, D. (2015). What money doesn't buy: Class resources and children's participation in organized extracurricular activities. Social Forces, 94(2), 479-503. https://doi.org/10.1093/sf/sov071.
      Wendling, T., Jung, K., Callahan, A., Schuler, A., Shah, N., & Gallego, B. (2018). Comparing methods for estimation of heterogeneous treatment effects using observational data from health care databases. Statistics in Medicine, 37(23), 3309-3324. https://doi.org/10.1002/sim.7820.
      Wentzel, K. R. (1999). Social-motivational processes and interpersonal relationships: Implications for understanding motivation at school. Journal of Educational Psychology, 91, 76-97. https://doi.org/10.1037/0022-0663.91.1.76.
      Wentzel, K. R., & Looney, L. (2007). Socialization in school settings. In J. E. Grusec & P. D. Hastings (Eds.), Handbook of socialization: Theory and research (pp. 382-403). The Guilford Press.
      Wigfield, A., & Eccles, J. S. (2000). Expectancy-value theory of achievement motivation. Contemporary Educational Psychology, 25(1), 68-81. https://doi.org/10.1006/ceps.1999.1015.
      Woody, S., Carvalho, C. M., & Murray, J. S. (2021). Model interpretation through lower-dimensional posterior summarization. Journal of Computational and Graphical Statistics, 30(1), 144-161. https://doi.org/10.1080/10618600.2020.1796684.
      Yeager, D. S., & Bundick, M. J. (2009). The role of purposeful work goals in promoting meaning in life and in schoolwork during adolescence. Journal of Adolescent Research, 24(4), 423-452.
      Yeager, D. S., Carroll, J. M., Buontempo, J., Cimpian, A., Woody, S., Crosnoe, R., Muller, C., Murray, J., Mhatre, P., Kersting, N., Hulleman, C., Kudym, M., Murphy, M., Duckworth, A. L., Walton, G. M., & Dweck, C. S. (2022). Teacher mindsets help explain where a growth-mindset intervention does and doesn't work. Psychological Science, 33(1), 18-32. https://doi.org/10.1177/09567976211028984.
      Yeager, D. S., Dahl, R. E., & Dweck, C. S. (2018). Why interventions to influence adolescent behavior often fail but could succeed. Perspectives on Psychological Science, 13(1), 101-122. https://doi.org/10.1177/1745691617722620.
      Yeager, D. S., & Dweck, C. S. (2012). Mindsets that promote resilience: When students believe that personal characteristics can be developed. Educational Psychologist, 47(4), 302-314. https://doi.org/10.1080/00461520.2012.722805.
      Yeager, D. S., & Dweck, C. S. (2020). What can be learned from growth mindset controversies? American Psychologist, 75, 1269-1284. https://doi.org/10.1037/amp0000794.
      Yeager, D. S., Hanselman, P., Walton, G. M., Murray, J. S., Crosnoe, R., Muller, C., Tipton, E., Schneider, B., Hulleman, C. S., Hinojosa, C. P., Paunesku, D., Romero, C., Flint, K., Roberts, A., Trott, J., Iachan, R., Buontempo, J., Yang, S. M., Carvalho, C. M., … Dweck, C. S. (2019). A national experiment reveals where a growth mindset improves achievement. Nature, 573(7774), 364-369. https://doi.org/10.1038/s41586-019-1466-y.
      Yeager, D. S., Henderson, M. D., Paunesku, D., Walton, G. M., D'Mello, S., Spitzer, B. J., & Duckworth, A. L. (2014). Boring but important: A self-transcendent purpose for learning fosters academic self-regulation. Journal of Personality and Social Psychology, 107(4), 559-580. https://doi.org/10.1037/a0037637.
      Yeager, D. S., Walton, G. M., Brady, S. T., Akcinar, E. N., Paunesku, D., Keane, L., Kamentz, D., Ritter, G., Duckworth, A. L., Urstein, R., Gomez, E. M., Markus, H. R., Cohen, G. L., & Dweck, C. S. (2016). Teaching a lay theory before college narrows achievement gaps at scale. Proceedings of the National Academy of Sciences of the United States of America, 113(24), E3341-E3348. https://doi.org/10.1073/pnas.1524360113.
      Zhao, S., Setoh, P., Storage, D., & Cimpian, A. (2022). The acquisition of the gender-brilliance stereotype: Age trajectory, relation to parents' stereotypes, and intersections with race/ethnicity. Child Development, 93, e581-e597. https://doi.org/10.1111/cdev.13809.
    • Grant Information:
      P2C HD042849 United States HD NICHD NIH HHS; T32 HD007081 United States HD NICHD NIH HHS; R01 HD084772 United States HD NICHD NIH HHS
    • الموضوع:
      Date Created: 20230814 Date Completed: 20230818 Latest Revision: 20231003
    • الموضوع:
      20231004
    • الرقم المعرف:
      10.1111/mono.12471
    • الرقم المعرف:
      37574937