, Volume 46, Issue 2, pp 215–234 | Cite as

Building bridges between psychological science and education: Cultural stereotypes, STEM, and equity

  • Allison MasterEmail author
  • Andrew N. Meltzoff
Open File


There is a gender gap in science, technology, engineering, and mathematics (STEM) education. This presents a worldwide problem of inequity. Sociocultural stereotypes associating STEM with males act as barriers that prevent girls from developing interests in STEM. This article aims to show that we can increase equity and enhance outcomes for a broader number of children around the world by integrating psychological and educational science. The article discusses four strands of research in an effort to build a bridge between psychological science and educational practice and policy. First, it describes how stereotypes can act as barriers that prevent girls from developing interests in STEM. Second, it summarizes psychological experiments demonstrating that counteracting stereotypes can increase girls’ interest in enrolling in STEM courses. Third, it examines new results showing that children adopt the pervasive stereotypes of their culture starting from surprisingly young ages, and it shows that children’s stereotypes influence their academic attitudes and performance. Fourth, it describes innovative practical interventions that can increase and equalize motivation and engagement in STEM for both boys and girls. In each of these sections, the authors link scientific findings with educational applications. Cultural stereotypes contribute to educational inequities, but scientists, educators, and policymakers can together make a difference to reduce stereotyping and boost girls’ interest in STEM worldwide.


STEM Gender Stereotypes Equity Psychology Identity Inclusion 


  1. Bailey, D. H., Watts, T. W., Littlefield, A. K., & Geary, D. C. (2014). State and trait effects on individual differences in children’s mathematical development. Psychological Science, 25(11), 2017–2026. doi: 10.1177/0956797614547539.CrossRefGoogle Scholar
  2. Baumeister, R. F., & Leary, M. R. (1995). The need to belong: Desire for interpersonal attachments as a fundamental human motivation. Psychological Bulletin, 117(3), 497–529. doi: 10.1037/0033-2909.117.3.497.CrossRefGoogle Scholar
  3. Beilock, S. L., Rydell, R. J., & McConnell, A. R. (2007). Stereotype threat and working memory: Mechanisms, alleviation, and spillover. Journal of Experimental Psychology: General, 136(2), 256–276. doi: 10.1037/0096-3445.136.2.256.CrossRefGoogle Scholar
  4. 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(1), 246–263. doi: 10.1111/j.1467-8624.2007.00995.x.CrossRefGoogle Scholar
  5. Brown, E. (2016, April 26). Top business leaders, 27 governors, urge Congress to boost computer science education. Washington Post.
  6. Carli, L. L., Alawa, L., Lee, Y., Zhao, B., & Kim, E. (2016). Stereotypes about gender and science: Women ≠ scientists. Psychology of Women Quarterly, 40(2), 244–260. doi: 10.1177/0361684315622645.CrossRefGoogle Scholar
  7. Ceci, S. J., & Williams, W. M. (2010). Sex differences in math-intensive fields. Current Directions in Psychological Science, 19(5), 275–279. doi: 10.1177/0963721410383241.CrossRefGoogle Scholar
  8. Ceci, S. J., Williams, W. M., & Barnett, S. M. (2009). Women’s underrepresentation in science: Sociocultural and biological considerations. Psychological Bulletin, 135(2), 218–261. doi: 10.1037/a0014412.CrossRefGoogle Scholar
  9. Charles, M., & Bradley, K. (2009). Indulging our gendered selves? Sex segregation by field of study in 44 countries. American Journal of Sociology, 114(4), 924–976. doi: 10.1086/595942.CrossRefGoogle Scholar
  10. Cherney, I. D., & London, K. (2006). Gender-linked differences in the toys, television shows, computer games, and outdoor activities of 5- to 13-year-old children. Sex Roles, 54(9–10), 717–726. doi: 10.1007/s11199-006-9037-8.CrossRefGoogle Scholar
  11. Cheryan, S., Drury, B., & Vichayapai, M. (2013). Enduring influence of stereotypical computer science role models on women’s academic aspirations. Psychology of Women Quarterly, 37(1), 72–79. doi: 10.1177/0361684312459328.CrossRefGoogle Scholar
  12. Cheryan, S., Master, A., & Meltzoff, A. N. (2015). Cultural stereotypes as gatekeepers: Increasing girls’ interest in computer science and engineering by diversifying stereotypes. Frontiers in Psychology, 6, 49. doi: 10.3389/fpsyg.2015.00049.CrossRefGoogle Scholar
  13. Cheryan, S., Meltzoff, A. N., & Kim, S. (2011). Classrooms matter: The design of virtual classrooms influences gender disparities in computer science classes. Computers & Education, 57(2), 1825–1835. doi: 10.1016/j.compedu.2011.02.004.CrossRefGoogle Scholar
  14. Cheryan, S., Plaut, V. C., Davies, P. G., & Steele, C. M. (2009). Ambient belonging: How stereotypical cues impact gender participation in computer science. Journal of Personality and Social Psychology, 97(6), 1045–1060. doi: 10.1037/a0016239.CrossRefGoogle Scholar
  15. Cheryan, S., Plaut, V. C., Handron, C., & Hudson, L. (2013). The stereotypical computer scientist: Gendered media representations as a barrier to inclusion for women. Sex Roles, 69(1–2), 58–71. doi: 10.1007/s11199-013-0296-x.CrossRefGoogle Scholar
  16. Cheryan, S., Siy, J. O., Vichayapai, M., Drury, B. J., & Kim, S. (2011). Do female and male role models who embody STEM stereotypes hinder women’s anticipated success in STEM? Social Psychological and Personality Science, 2(6), 656–664. doi: 10.1177/1948550611405218.CrossRefGoogle Scholar
  17. Cheryan, S., Ziegler, S. A., Montoya, A., & Jiang, L. (2017). Why are some STEM fields more gender balanced than others? Psychological Bulletin, 143, 1–35. doi: 10.1037/bul0000052.CrossRefGoogle Scholar
  18. Cheryan, S., Ziegler, S., Plaut, V. C., & Meltzoff, A. N. (2014). Designing classrooms to maximize student achievement. Policy Insights from the Behavioral and Brain Science, 1(1), 4–12. doi: 10.1177/2372732214548677.CrossRefGoogle Scholar
  19. Cohen, G. L., Garcia, J., Apfel, N., & Master, A. (2006). Reducing the racial achievement gap: A social-psychological intervention. Science, 313(5791), 1307–1310. doi: 10.1126/science.1128317.CrossRefGoogle Scholar
  20. Cohen, G. L., Purdie-Vaughns, V., & Garcia, J. (2012). An identity threat perspective on intervention. In M. Inzlicht & T. Schmader (Eds.), Stereotype threat: Theory, process, and application (pp. 280–296). New York, NY: Oxford University Press.Google Scholar
  21. Cook, J. E., Purdie-Vaughns, V., Garcia, J., & Cohen, G. L. (2012). Chronic threat and contingent belonging: Protective benefits of values affirmation on identity development. Journal of Personality and Social Psychology, 102(3), 479–496. doi: 10.1037/a0026312.CrossRefGoogle Scholar
  22. Correll, S. J. (2001). Gender and the career choice process: The role of biased self-assessments. American Journal of Sociology, 106(6), 1691–1730. doi: 10.1086/321299.CrossRefGoogle Scholar
  23. Cvencek, D., Kapur, M., & Meltzoff, A. N. (2015). Math achievement, stereotypes, and math self-concepts among elementary-school students in Singapore. Learning and Instruction, 39, 1–10. doi: 10.1016/j.learninstruc.2015.04.002.CrossRefGoogle Scholar
  24. Cvencek, D., Meltzoff, A. N., & Greenwald, A. G. (2011). Math-gender stereotypes in elementary school children. Child Development, 82(3), 766–779. doi: 10.1111/j.1467-8624.2010.01529.x.CrossRefGoogle Scholar
  25. Cvencek, D., Meltzoff, A. N., & Kapur, M. (2014). Cognitive consistency and math-gender stereotypes in Singaporean children. Journal of Experimental Child Psychology, 117, 73–91. doi: 10.1016/j.jecp.2013.07.018.CrossRefGoogle Scholar
  26. Dasgupta, N. (2011). Ingroup experts and peers as social vaccines who inoculate the self-concept: The stereotype inoculation model. Psychological Inquiry, 22(4), 231–246. doi: 10.1080/1047840X.2011.607313.CrossRefGoogle Scholar
  27. de Cohen, C. C., & Deterding, N. (2009). Widening the net: National estimates of gender disparities in engineering. The Journal of Engineering Education, 98(3), 211–226. doi: 10.1002/j.2168-9830.2009.tb01020.x.CrossRefGoogle Scholar
  28. DeJarnette, N. K. (2012). America’s children: Providing early exposure to STEM (science, technology, engineering and math) initiatives. Education, 133(1), 77–84.Google Scholar
  29. Diekman, A. B., Brown, E. R., Johnston, A. M., & Clark, E. K. (2010). Seeking congruity between goals and roles: A new look at why women opt out of science, technology, engineering, and mathematics careers. Psychological Science, 21(8), 1051–1057. doi: 10.1177/0956797610377342.CrossRefGoogle Scholar
  30. Diekman, A. B., Clark, E. K., Johnston, A. M., Brown, E. R., & Steinberg, M. (2011). Malleability in communal goals and beliefs influences attraction to stem careers: Evidence for a goal congruity perspective. Journal of Personality and Social Psychology, 101(5), 902–918. doi: 10.1037/a0025199.CrossRefGoogle Scholar
  31. Diekman, A. B., Weisgram, E. S., & Belanger, A. L. (2015). New routes to recruiting and retaining women in STEM: Policy implications of a communal goal congruity perspective. Social Issues and Policy Review, 9(1), 52–88. doi: 10.1111/sipr.12010.CrossRefGoogle Scholar
  32. Dweck, C. S. (2007). Is math a gift? Beliefs that put females at risk. In S. J. Ceci & W. M. Williams (Eds.), Why aren’t more women in science? Top researchers debate the evidence (pp. 47–55). Washington, DC: American Psychological Association.CrossRefGoogle Scholar
  33. Dweck, C. S., & Master, A. (2009). Self-theories and motivation: Students’ beliefs about intelligence. In K. R. Wenzel & A. Wigfield (Eds.), Handbook of motivation at school (pp. 123–140). New York, NY: Routledge.Google Scholar
  34. Eccles, J. S., Jacobs, J. E., & Harold, R. D. (1990). Gender role stereotypes, expectancy effects, and parents’ socialization of gender differences. Journal of Social Issues, 46(2), 183–201. doi: 10.1111/j.1540-4560.1990.tb01929.x.CrossRefGoogle Scholar
  35. Ehrlinger, J., & Dunning, D. (2003). How chronic self-views influence (and potentially mislead) estimates of performance. Journal of Personality and Social Psychology, 84(1), 5–17. doi: 10.1037/0022-3514.84.1.5.CrossRefGoogle Scholar
  36. Else-Quest, N. M., Hyde, J. S., & Linn, M. C. (2010). Cross-national patterns of gender differences in mathematics: A meta-analysis. Psychological Bulletin, 136(1), 103–127. doi: 10.1037/a0018053.CrossRefGoogle Scholar
  37. European Round Table of Industrialists (2009). Societal changes: Mathematics, science & technology education report. Brussels: European Round Table of Industrialists.Google Scholar
  38. European Union (2009). She figures 2009—Statistics and indicators on gender equity in science. Brussels: European Commission.Google Scholar
  39. Feng, J., Spence, I., & Pratt, J. (2007). Playing an action video game reduces gender differences in spatial cognition. Psychological Science, 18(10), 850–855. doi: 10.1111/j.1467-9280.2007.01990.x.CrossRefGoogle Scholar
  40. Flore, P. C., & Wicherts, J. M. (2015). Does stereotype threat influence performance of girls in stereotyped domains? A meta-analysis. Journal of School Psychology, 53(1), 25–44. doi: 10.1016/j.jsp.2014.10.002.CrossRefGoogle Scholar
  41. Galdi, S., Cadinu, M., & Tomasetto, C. (2014). The roots of stereotype threat: When automatic associations disrupt girls’ math performance. Child Development, 85(1), 250–263. doi: 10.1111/cdev.12128.CrossRefGoogle Scholar
  42. Good, C., Rattan, A., & Dweck, C. S. (2012). Why do women opt out? Sense of belonging and women’s representation in mathematics. Journal of Personality and Social Psychology, 102(4), 700–717. doi: 10.1037/a0026659.CrossRefGoogle Scholar
  43. Guiso, L., Monte, F., Sapienza, P., & Zingales, L. (2008). Culture, gender, and math. Science, 320(5880), 1164–1165. doi: 10.1126/science.1154094.CrossRefGoogle Scholar
  44. Hanselman, P., Bruch, S. K., Gamoran, A., & Borman, G. D. (2014). Threat in context: School moderation of the impact of social identity threat on racial/ethnic achievement gaps. Sociology of Education, 87(2), 106–124. doi: 10.1177/0038040714525970.CrossRefGoogle Scholar
  45. 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(5), 745–765. doi: 10.1037/pspp0000075.CrossRefGoogle Scholar
  46. Heckman, J. J. (2006). Skill formation and the economics of investing in disadvantaged children. Science, 312(5782), 1900–1902. doi: 10.1126/science.1128898.CrossRefGoogle Scholar
  47. Hewlett, S. A., Luce, C. B., Servon, L. J., Sherbin, L., Shiller, P., Sosnovich, E., et al. (2008). The Athena factor: Reversing the brain drain in science, engineering, and technology. Harvard Business Review Research Report. Boston, MA: Harvard Business Publishing.Google Scholar
  48. Heyman, G. D., & Legare, C. H. (2004). Children’s beliefs about gender differences in the academic and social domains. Sex Roles, 50(3–4), 227–239. doi: 10.1023/B:SERS.0000015554.12336.30.CrossRefGoogle Scholar
  49. Hidi, S., & Renninger, K. A. (2006). The four-phase model of interest development. Educational Psychologist, 41(2), 111–127. doi: 10.1207/s15326985ep4102_4.CrossRefGoogle Scholar
  50. Hong, H., & Lin-Siegler, X. (2012). How learning about scientists’ struggles influences students’ interest and learning in physics. Journal of Educational Psychology, 104(2), 469–484. doi: 10.1037/a0026224.CrossRefGoogle Scholar
  51. Hulleman, C. S., & Barron, K. E. (2016). Motivation interventions in education: Bridging theory, research, and practice. In L. Corno & E. M. Anderman (Eds.), Handbook of educational psychology (3rd ed., pp. 160–171). New York, NY: Routledge.Google Scholar
  52. Hyde, J. S. (2014). Gender similarities and differences. Annual Review of Psychology, 65(1), 373–398. doi: 10.1146/annurev-psych-010213-115057.CrossRefGoogle Scholar
  53. Hyde, J. S., Lindberg, S. M., Linn, M. C., Ellis, A. B., & Williams, C. C. (2008). Gender similarities characterize math performance. Science, 321(5888), 494–495. doi: 10.1126/science.1160364.CrossRefGoogle Scholar
  54. Jirout, J. J., & Newcombe, N. S. (2015). Building blocks for developing spatial skills: Evidence from a large, representative U.S. sample. Psychological Science, 26(3), 302–310. doi: 10.1177/0956797614563338.CrossRefGoogle Scholar
  55. Jones, M. G., Howe, A., & Rua, M. J. (2000). Gender differences in students’ experiences, interests, and attitudes toward science and scientists. Science Education, 84(2), 180–192. doi: 10.1002/(SICI)1098-237X(200003)84:2<180:AID-SCE3>3.0.CO;2-X.CrossRefGoogle Scholar
  56. Kurtz-Costes, B., Rowley, S. J., Harris-Britt, A., & Woods, T. (2008). Gender stereotypes about mathematics and science and self-perceptions of ability in late childhood and early adolescence. Merrill-Palmer Quarterly, 54(3), 386–409. doi: 10.1353/mpq.0.0001.CrossRefGoogle Scholar
  57. Leslie, S. J., Cimpian, A., Meyer, M., & Freeland, E. (2015). Expectations of brilliance underlie gender distributions across academic disciplines. Science, 347(6219), 262–265. doi: 10.1126/science.1261375.CrossRefGoogle Scholar
  58. Levine, S. C., Ratliff, K. R., Huttenlocher, J., & Cannon, J. (2012). Early puzzle play: A predictor of preschoolers’ spatial transformation skill. Developmental Psychology, 48(2), 530–542. doi: 10.1037/a0025913.CrossRefGoogle Scholar
  59. Levine, S. C., Vasilyeva, M., Lourenco, S. F., Newcombe, N. S., & Huttenlocher, J. (2005). Socioeconomic status modifies the sex difference in spatial skill. Psychological Science, 16(11), 841–845. doi: 10.1111/j.1467-9280.2005.01623.x.CrossRefGoogle Scholar
  60. Lin-Siegler, X., Ahn, J. N., Chen, J., Fang, F. A., & Luna-Lucero, M. (2016). Even Einstein struggled: Effects of learning about great scientists’ struggles on high school students’ motivation to learn science. Journal of Educational Psychology, 108(3), 314–328. doi: 10.1037/edu0000092.CrossRefGoogle Scholar
  61. Maeda, Y., & Yoon, S. Y. (2013). A meta-analysis on gender differences in mental rotation ability measured by the Purdue spatial visualization tests: Visualization of rotations (PSVT: R). Educational Psychology Review, 25(1), 69–94. doi: 10.1007/s10648-012-9215-x.CrossRefGoogle Scholar
  62. Maltese, A. V., & Tai, R. H. (2010). Eyeballs in the fridge: Sources of early interest in science. International Journal of Science Education, 32(5), 669–685. doi: 10.1080/09500690902792385.CrossRefGoogle Scholar
  63. Margolis, J., & Fisher, A. (2002). Unlocking the clubhouse: Women in computing. Cambridge, MA: MIT Press.Google Scholar
  64. Martin, C. L., & Dinella, L. M. (2002). Children’s gender cognitions, the social environment, and sex differences in cognitive domains. In A. McGillicuddy-DeLisi & R. De Lisi (Eds.), Biology, society, and behavior: The development of sex differences in cognition (pp. 207–239). Westport, CT: Ablex.Google Scholar
  65. Master, A., Butler, L. P., & Walton, G. W. (2017). How the subjective relationship between the self, others, and a task drives interest. In P. A. O’Keefe & J. M. Harackiewicz (Eds.), The science of interest. New York, NY: Springer.Google Scholar
  66. Master, A., Cheryan, S., & Meltzoff, A. N. (2014). Reducing adolescent girls’ concerns about STEM stereotypes: When do female teachers matter? International Review of Social Psychology, 27(3–4), 79–102.Google Scholar
  67. Master, A., Cheryan, S., & Meltzoff, A. N. (2016). Computing whether she belongs: Stereotypes undermine girls’ interest and sense of belonging in computer science. Journal of Educational Psychology, 108(3), 424–437. doi: 10.1037/edu0000061.CrossRefGoogle Scholar
  68. Master, A., Cheryan, S., & Meltzoff, A. N. (2017). Social group membership increases STEM engagement among preschoolers. Developmental Psychology, 53, 201–209. doi: 10.1037/dev0000195.CrossRefGoogle Scholar
  69. Master, A., Cheryan, S., Moscatelli, A., & Meltzoff, A. N. (in press). Providing programming experience leads to higher STEM motivation for first-grade girls. Journal of Experimental Child Psychology.Google Scholar
  70. Master, A., & Walton, G. M. (2013). Minimal groups increase young children’s motivation and learning on group-relevant tasks. Child Development, 84(2), 737–751. doi: 10.1111/j.1467-8624.2012.01867.x.CrossRefGoogle Scholar
  71. Meltzoff, A. N. (2007). “Like me”: A foundation for social cognition. Developmental Science, 10(1), 126–134. doi: 10.1111/j.1467-7687.2007.00574.x.CrossRefGoogle Scholar
  72. Meltzoff, A. N. (2013). Origins of social cognition: Bidirectional self-other mapping and the “like-me” hypothesis. In M. Banaji & S. Gelman (Eds.), Navigating the social world: What infants, children, and other species can teach us (pp. 139–144). New York, NY: Oxford University Press. doi: 10.1093/acprof:oso/9780199890712.003.0025.CrossRefGoogle Scholar
  73. Meltzoff, A. N., Kuhl, P. K., Movellan, J., & Sejnowski, T. J. (2009). Foundations for a new science of learning. Science, 325(5938), 284–288. doi: 10.1126/science.1175626.CrossRefGoogle Scholar
  74. 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. doi: 10.1037/edu0000005.CrossRefGoogle Scholar
  75. Miller, D. I., & Wai, J. (2015). The bachelor’s to Ph.D. STEM pipeline no longer leaks more women than men: A 30-year analysis. Frontiers in Psychology, 6, 37. doi: 10.3389/fpsyg.2015.00037.Google Scholar
  76. Mohammadpour, E., Shekarchizadeh, A., & Kalantarrashidi, S. A. (2015). Multilevel modeling of science achievement in the TIMSS participating countries. The Journal of Educational Research, 108(6), 449–464. doi: 10.1080/00220671.2014.917254.CrossRefGoogle Scholar
  77. Moses, M. S., Howe, K. R., & Niesz, T. (1999). The pipeline and student perceptions of schooling: Good news and bad news. Educational Policy, 13(4), 573–591. doi: 10.1177/0895904899013004005.CrossRefGoogle Scholar
  78. Moss-Racusin, C. A., Dovidio, J. F., Brescoll, V. L., Graham, M. J., & Handelsman, J. (2012). Science faculty’s subtle gender biases favor male students. Proceedings of the National Academy of Sciences of the United States of America, 109(41), 16464–16479. doi: 10.1073/pnas.1211286109.CrossRefGoogle Scholar
  79. Mullis, I. V. S., Martin, M. O., & Foy, P. (with Olson, J. F., Preuschoff, C., Erberber, E., Arora, A., & Galia, J.) (2008). TIMSS 2007 International Mathematics Report: Findings from IEA’s Trends in International Mathematics and Science Study at the fourth and eighth grades. Chestnut Hill, MA: TIMSS & PIRLS International Study Center, Boston College.Google Scholar
  80. Murphy, M. C., Steele, C. M., & Gross, J. J. (2007). Signaling threat: How situational cues affect women in math, science, and engineering settings. Psychological Science, 18(10), 879–885. doi: 10.1111/j.1467-9280.2007.01995.x.CrossRefGoogle Scholar
  81. Muzzatti, B., & Agnoli, F. (2007). Gender and mathematics: Attitudes and stereotype threat susceptibility in Italian children. Developmental Psychology, 43(3), 747–759. doi: 10.1037/0012-1649.43.3.747.CrossRefGoogle Scholar
  82. National Science Foundation (2015). TABLE 5–1. Bachelor’s degrees awarded, by sex and field: 2002–2012.
  83. Newcombe, N. S., & Frick, A. (2010). Early education for spatial intelligence: Why, what, and how. Mind, Brain, and Education, 4(3), 102–111. doi: 10.1111/j.1751-228X.2010.01089.x.CrossRefGoogle Scholar
  84. Nosek, B. A., Smyth, F. L., Sriram, N., Lindner, N. M., Devos, T., Ayala, A., et al. (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. doi: 10.1073/pnas.0809921106.CrossRefGoogle Scholar
  85. OECD (2011). Report on the gender initiative: Gender equality in education, employment, and entrepreneurship. Paris: OECD.Google Scholar
  86. OECD (2015a). OECD science, technology, and industry scoreboard 2015: Innovation for growth and society. Paris: OECD. doi: 10.1787/sti_scoreboard-2015-en.CrossRefGoogle Scholar
  87. OECD (2015b). Women in scientific production. Paris: OECD.
  88. Passolunghi, M. C., Rueda Ferreira, T. I., & Tomasetto, C. (2014). Math–gender stereotypes and math-related beliefs in childhood and early adolescence. Learning and Individual Differences, 34, 70–76. doi: 10.1016/j.lindif.2014.05.005.CrossRefGoogle Scholar
  89. 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(6), 784–793. doi: 10.1177/0956797615571017.CrossRefGoogle Scholar
  90. Plante, I., de la Sablonnière, R., Aronson, J. M., & Théorêt, M. (2013). Gender stereotype endorsement and achievement-related outcomes: The role of competence beliefs and task values. Contemporary Educational Psychology, 38(3), 225–235. doi: 10.1016/j.cedpsych.2013.03.004.CrossRefGoogle Scholar
  91. Powers, J. T., Cook, J. E., Purdie-Vaughns, V., Garcia, J., Apfel, N., & Cohen, G. L. (2016). Changing environments by changing individuals: The emergent effects of psychological intervention. Psychological Science, 27(2), 150–160. doi: 10.1177/0956797615614591.CrossRefGoogle Scholar
  92. Ramani, G. B., & Siegler, R. S. (2008). Promoting broad and stable improvements in low-income children’s numerical knowledge through playing number board games. Child Development, 79(2), 375–394. doi: 10.1111/j.1467-8624.2007.01131.x.CrossRefGoogle Scholar
  93. Reuben, E., Sapienza, P., & Zingales, L. (2014). How stereotypes impair women’s careers in science. Proceedings of the National Academy of Sciences, 111(12), 4403–4408. doi: 10.1073/pnas.1314788111.CrossRefGoogle Scholar
  94. Riegle-Crumb, C., King, B., Grodsky, E., & Muller, C. (2012). The more things change, the more they stay the same? Prior achievement fails to explain gender inequality in entry into STEM college majors over time. American Educational Research Journal, 49(6), 1048–1073. doi: 10.3102/0002831211435229.CrossRefGoogle Scholar
  95. Rodríguez, R. J., & Garg, K. (2016). Supporting our youngest innovators: STEM starts early!
  96. Sadker, M., & Sadker, D. (1994). Failing at fairness: How America’s schools cheat girls. New York: Scribner.Google Scholar
  97. Schmader, T., Johns, M., & Barquissau, M. (2004). The costs of accepting gender differences: The role of stereotype endorsement in women’s experience in the math domain. Sex Roles, 50(11–12), 835–850. doi: 10.1023/B:SERS.0000029101.74557.a0.CrossRefGoogle Scholar
  98. Shapiro, J. R. (2011). Different groups, different threats: A multi-threat approach to the experience of stereotype threats. Personality and Social Psychology Bulletin, 37(4), 464–480. doi: 10.1177/0146167211398140.CrossRefGoogle Scholar
  99. Shenouda, C. K., & Danovitch, J. H. (2014). Effects of gender stereotypes and stereotype threat on children’s performance on a spatial task. International Review of Social Psychology, 27(3–4), 53–77.Google Scholar
  100. Siegler, R. S. (2009). Improving the numerical understanding of children from low-income families. Child Development Perspectives, 3(2), 118–124. doi: 10.1111/j.1750-8606.2009.00090.x.CrossRefGoogle Scholar
  101. Sjøberg, S. & Schreiner, C. (2010). The ROSE project: An overview and key findings. Oslo: University of Oslo.
  102. Skwarchuk, S. L., Sowinski, C., & LeFevre, J. A. (2014). Formal and informal home learning activities in relation to children’s early numeracy and literacy skills: The development of a home numeracy model. Journal of Experimental Child Psychology, 121, 63–84. doi: 10.1016/j.jecp.2013.11.006.CrossRefGoogle Scholar
  103. Smith, J. L., Brown, E. R., Thoman, D. B., & Deemer, E. D. (2015). Losing its expected communal value: How stereotype threat undermines women’s identity as research scientists. Social Psychology of Education, 18(3), 443–466. doi: 10.1007/s11218-015-9296-8.CrossRefGoogle Scholar
  104. Smith, J. L., Lewis, K. L., Hawthorne, L., & Hodges, S. D. (2013). When trying hard isn’t natural: Women’s belonging with and motivation for male-dominated STEM fields as a function of effort expenditure concerns. Personality and Social Psychology Bulletin, 39(2), 131–143. doi: 10.1177/0146167212468332.CrossRefGoogle Scholar
  105. 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. doi: 10.1006/jesp.1998.1373.CrossRefGoogle Scholar
  106. Steele, C. M. (1997). A threat in the air: How stereotypes shape intellectual identity and performance. American Psychologist, 52(6), 613–629. doi: 10.1037/0003-066X.52.6.613.CrossRefGoogle Scholar
  107. Steele, J. (2003). Children’s gender stereotypes about math: The role of stereotype stratification. Journal of Applied Social Psychology, 33(12), 2587–2606. doi: 10.1111/j.1559-1816.2003.tb02782.x.CrossRefGoogle Scholar
  108. Steffens, M. C., Jelenec, P., & Noack, P. (2010). On the leaky math pipeline: Comparing implicit math-gender stereotypes and math withdrawal in female and male children and adolescents. Journal of Educational Psychology, 102(4), 947–963. doi: 10.1037/a0019920.CrossRefGoogle Scholar
  109. Terlecki, M. S., & Newcombe, N. S. (2005). How important is the digital divide? The relation of computer and videogame usage to gender differences in mental rotation ability. Sex Roles, 53(5), 433–441. doi: 10.1007/s11199-005-6765-0.CrossRefGoogle Scholar
  110. The College Board (2015). Number of schools offering AP exams (by subject).
  111. UNESCO (2004). Gender sensitivity: A training manual for sensitizing education managers, curriculum and material developers and media professionals to gender concerns.
  112. UNESCO (2015). Education 2030: Towards inclusive and equitable quality education and lifelong learning for all.
  113. U.S. Department of Education (2003). Teaching mathematics in seven countries: Results from the TIMSS 1999 video study. Washington, DC: National Center for Education Statistics.Google Scholar
  114. Uttal, D. H., Meadow, N. G., Tipton, E., Hand, L. L., Alden, A. R., Warren, C., et al. (2013a). The malleability of spatial skills: A meta-analysis of training studies. Psychological Bulletin, 139(2), 352–402. doi: 10.1037/a0028446.CrossRefGoogle Scholar
  115. Uttal, D. H., Miller, D. I., & Newcombe, N. S. (2013b). Exploring and enhancing spatial thinking: Links to achievement in science, technology, engineering, and mathematics? Current Directions in Psychological Science, 22(5), 367–373. doi: 10.1177/0963721413484756.CrossRefGoogle Scholar
  116. Van Langen, A., & Dekkers, H. (2005). Cross-national differences in participating in tertiary science, technology, engineering and mathematics education. Comparative Education, 41(3), 329–350. doi: 10.1080/03050060500211708.CrossRefGoogle Scholar
  117. Voyer, D. (2011). Time limits and gender differences on paper-and-pencil tests of mental rotation: A meta-analysis. Psychonomic Bulletin & Review, 18(2), 267–277. doi: 10.3758/s13423-010-0042-0.CrossRefGoogle Scholar
  118. 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. doi: 10.1037/0022-3514.92.1.82.CrossRefGoogle Scholar
  119. Walton, G. M., Cohen, G. L., Cwir, D., & Spencer, S. J. (2012). Mere belonging: The power of social connections. Journal of Personality and Social Psychology, 102(3), 513–532. doi: 10.1037/a0025731.CrossRefGoogle Scholar
  120. 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. doi: 10.1037/a0037461.CrossRefGoogle Scholar
  121. Yeager, D. S., Walton, G. M., Brady, S. T., Akcinar, E. N., Paunesku, D., Keane, L., et al. (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. doi: 10.1073/pnas.1524360113.CrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.Institute for Learning and Brain SciencesUniversity of WashingtonSeattleUSA

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