Understanding Latino Boys’ Motivation to Pursue STEM while Navigating School Inequalities

References

• Anandarajan, M., Hill, C., & Nolan, T. (2019). Text Preprocessing. In:Practical Text Analytics. Advances in Analytics and Data Science, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-319-95663-3_4
   Google Scholar
• Andersen, L., & Ward, T. (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
   Web of Science ®Google Scholar
• Aschbacher, P. R., Li, E., & Roth, E. J. (2010). Is science me? High school students’ identities, participation and aspirations in science, engineering, and medicine. Journal of Research in Science Teaching, 47(5), 564–582. https://doi.org/10.1002/tea.20353
   Web of Science ®Google Scholar
• Barron, K. E., & Hulleman, C. S. (2015). Expectancy-value-cost model of motivation. J. S. Eccles, & K. Salmela-Aro (Eds.). International encyclopedia of social and behavioral sciences: Motivational psychology (2nd ed., pp. 503–509). New York: NY: Elsevier. https://doi.org/10.1016/B978-0-08-097086-8.26099-6
   Google Scholar
• Braun, H., Coley, R., Jia, Y., & Trapani, C. (2009). Exploring what works in science instruction: A look at the eighth grade science classroom. ETS Policy Information Center. https://files.eric.ed.gov/fulltext/ED507837.pdf
   Google Scholar
• Delgado‐Gaitan, C. (1994). Consejos: The power of cultural narratives. Anthropology & Education Quarterly, 25(3), 298–316. https://doi.org/10.1525/aeq.1994.25.3.04x0146p
   Web of Science ®Google Scholar
• Eccles (Parsons), J., Adler, T. F., Futterman, R., Goff, S. B., Kaczala, C. M., Meece, J. L., & Midgley, C. (1983). Expectancies, values, and academic behaviors. In J. T. Spence (Ed.), Achievement and achievement motivation (pp. 75–146). W. H. Freeman.
   Google Scholar
• Eccles, J. (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), 79–89. https://doi.org/10.1080/00461520902832368
   Web of Science ®Google Scholar
• 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
   PubMed Web of Science ®Google Scholar
• Else-Quest, N., Mineo, C., & Higgins, A. (2013). Math and science attitudes and achievement at the intersection of gender and ethnicity. Psychology of Women Quarterly, 37(3), 293–309. https://doi.org/10.1177/0361684313480694
   Web of Science ®Google Scholar
• Fereday, J., & Muir-Cochrane, E. (2006). Demonstrating rigor using thematic analysis: A hybrid approach of inductive and deductive coding and theme development. International Journal of Qualitative Methods, 5(1), 80–92. https://doi.org/10.1177/160940690600500107
   Google Scholar
• Frenzel, A., Goetz, T., Pekrun, R., & Watt, H. (2010). Development of mathematics interest in adolescence: Influences of gender, family, and school context. Journal of Research on Adolescence, 20(2), 507–537. https://doi.org/10.1111/j.1532-7795.2010.00645.x
   Web of Science ®Google Scholar
• Gaspard, H., Dicke, A., Flunger, B., Schreier, B., Häfner, I., Trautwein, U., & Nagengast, B. (2015). More value through greater differentiation: Gender differences in value beliefs about math. Journal of Educational Psychology, 107(3), 663–677. https://doi.org/10.1037/edu00000
   Web of Science ®Google Scholar
• Grossman, J. M., & Porche, M. V. (2013). Perceived gender and racial/ethnic barriers to STEM success. Urban Education, 49(6), 698–727. https://doi.org/10.1177/0042085913481364
   Web of Science ®Google Scholar
• Harper, S. R. (2010). An anti‐deficit achievement framework for research on students of color in STEM. New Directions for Institutional Research, 2010(148), 63–74. https://doi.org/10.1002/ir.362
   Google Scholar
• Howard, T. C., & Navarro, O. (2016). Critical race theory 20 years later: Where do we go from here? Urban Education, 51(3), 253–273. https://doi.org/10.1177/0042085915622541
   Web of Science ®Google Scholar
• Ladson-Billings, G. (2009). Race still matters: Critical race theory in education. In M. W. Apple, W. Au, & L. A. Gandin (Eds.), The Routledge international handbook of critical education (pp. 110–122). Routledge.
   Google Scholar
• Ledesma, M., & Calderón, D. (2015). Critical race theory in education: A review of past literature and a look to the future. Qualitative Inquiry, 21(3), 206–222. https://doi.org/10.1177/1077800414557825
   Web of Science ®Google Scholar
• 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. https://doi.org/10.1080/09500690902792385
   Web of Science ®Google Scholar
• National Science Board. (2015, February). Revisiting the STEM workforce, a companion to science and engineering indicators. National Science Foundation (NSB-2015-10). https://www.nsf.gov/nsb/publications/2015/nsb201510.pdf
   Google Scholar
• National Science Foundation. (2017, January). Women, minorities, and persons with disabilities in science and engineering: 2017. www.nsf.gov/statistics/wmpd/.
   Google Scholar
• Ostrander, R. (2015). School funding: Inequality in district funding and the disparate impact on urban and migrant school children. Brigham Young University Education and Law Journal, 2015(1), 271–296. https://heinonline.org/HOL/LandingPage?handle=hein.journals/byuelj2015&div=12&id=&page=
   Google Scholar
• Ramirez, F. O., & Kwak, N. (2015). Women’s enrollments in STEM in higher education: Cross-national trends, 1970–2010. In W. Pearson Jr., L. Frehill, & C. McNeely (Eds.), Advancing women in science. Springer.
   Google Scholar
• Sáenz, V. B., García-Louis, C., Drake, A. P., & Guida, T. (2018). Leveraging their family capital: How Latino males successfully navigate the community college. Community College Review, 46(1), 40–61. https://doi.org/10.1177/0091552117743567
   Web of Science ®Google Scholar
• Safavian, N., & Conley, A. (2016). Expectancy-value beliefs of early-adolescent Hispanic and non-Hispanic youth: Predictors of mathematics achievement and enrollment. AERA Open, 2(4), 17. https://doi.org/10.1177/2332858416673357
   Web of Science ®Google Scholar
• Segal, E. A., Gerdes, K. E., Mullins, J., Wagaman, M. A., & Androff, D. (2011). Social empathy attitudes: Do Latino students have more? Journal of Human Behavior in the Social Environment, 21(4), 438–454. https://doi.org/10.1080/10911359.2011.566445
   Google Scholar
• Smith, P. S., Trygstad, P. J., & Banilower, E. R. (2016). Widening the gap: Unequal distribution of resources for K–12 science instruction. Education Policy Analysis Archives, 24(8), 8. https://doi.org/10.14507/epaa.24.2207
   Google Scholar
• Tomlinson, C. A., & Jarvis, J. M. (2014). Case studies of success: Supporting academic success for students with high potential from ethnic minority and economically disadvantaged backgrounds. Journal for the Education of the Gifted, 37(3), 191–219. https://doi.org/10.1177/0162353214540826
   Google Scholar
• Turner, S., & Ireson, G. (2010). Fifteen pupils’ positive approach to primary school science: When does it decline? Educational Studies, 36(2), 119–141. https://doi.org/10.1080/03055690903148662
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Valenzuela, A., & Rubio, B. (2018). Subtractive schooling. In J. I. Liontas, T. International Association & M. DelliCarpini, (Eds.). The TESOL encyclopedia of English language teaching. (pp. 1–7). New York: Wiley. https://doi.org/10.1002/9781118784235.eelt0139
   Google Scholar
• Verhoeven, M., Poorthuis, A. M., & Volman, M. (2019). The role of school in adolescents’ identity development. A literature review. Educational Psychology Review, 31(1), 35–63. https://doi.org/10.1007/s10648-018-9457-3
   Web of Science ®Google Scholar
• Wang, M. T., & Degol, J. (2013). Motivational pathways to STEM career choices: Using expectancy–value perspective to understand individual and gender differences in STEM fields. Developmental Review, 33(4), 304–340. https://doi.org/10.1016/j.dr.2013.08.001
   Web of Science ®Google Scholar
• Wang, X. (2013). Why students choose STEM majors: Motivation, high school learning, and postsecondary context of support. American Educational Research Journal, 50(5), 1081–1121. https://doi.org/10.3102/0002831213488622
   Web of Science ®Google Scholar
• Wigfield, A., Tonks, S. M., & Klauda, S. L. (2016). Expectancy-value theory. In K. R. Wentzel & D. B. Miele (Eds.), Handbook of motivation at school (2nd ed., pp. 55–74). Routledge.
   Google Scholar
• Wigfield, A., & Gladstone, J. R. (2019). What does expectancy-value theory have to say about motivation and achievement in times of change and uncertainty? In E. N. Gonida & M. S. Lemos (Eds.), Motivation in education at a time of global change: Theory, research, and implications for practice (pp. 15–32). Emerald Publishing Limited.
   Google Scholar
• Wigfield, A., & Eccles, J. S. (2000). Expectancy–value theory of achievement motivation. Contemporary Educational Psychology, 25(1), 68–8. https://doi.org/10.1006/ceps.1999.1015
   Google Scholar
• Wyss, V. L., Heulskamp, D., & Siebert, C. J. (2012). Increasing middle school student interest in STEM careers with videos of scientists. International Journal of Environmental & Science Education, 7(4), 501–522. https://files.eric.ed.gov/fulltext/EJ997137.pdf
   Google Scholar