Charting a path for growth in middle school students’ attitudes toward computer programming

References

• Adrion, W. R., Dunton, S. T., Ericson, B., Fall, R., Fletcher, C., & Guzdial, M. (2020, Dec). U.S. States must broaden participation while expanding access to computer science education. Communications of the ACM, 63(12), 22–25. https://cacm.acm.org/magazines/2020/12/248789-u-s-states-must-broaden-participation-while-expanding-access-to-computer-science-education/fulltext
   Web of Science ®Google Scholar
• Ahlqvist, S., London, B., & Rosenthal, L. (2013). Unstable identity compatibility: How gender rejection sensitivity undermines the success of women in science, technology, engineering, and mathematics fields. Psychological Science, 24(9), 1644–1652. https://doi.org/10.1177/0956797613476048
   PubMed Web of Science ®Google Scholar
• Ahmed, W. (2018). Developmental trajectories of math anxiety during adolescence: Associations with stem career choice. Journal of Adolescence (London, England.), 67, 158–166. https://doi.org/10.1016/j.adolescence.2018.06.010
   Google Scholar
• Angeli, C., & Giannakos, M. (2020). Computational thinking education: Issues and challenges. Computers in Human Behavior, 105, 106185. https://doi.org/10.1016/j.chb.2019.106185
   Web of Science ®Google Scholar
• Ball, C., Huang, K. T., Cotten, S. R., & Rikard, R. V. (2017). Pressurizing the STEM pipeline: An expectancy-value theory analysis of youths’ STEM attitudes. Journal of Science Education and Technology, 26(4), 372–382. https://doi.org/10.1007/s10956-017-9685-1
   Web of Science ®Google Scholar
• Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1). https://doi.org/10.18637/jss.v067.i01
   Web of Science ®Google Scholar
• Bernacki, M. L., Nokes-Malach, T. J., & Aleven, V. (2015). Examining self-efficacy during learning: Variability and relations to behavior, performance, and learning. Metacognition and Learning, 10(1), 99–117. https://doi.org/10.1007/s11409-014-9127-x
   Web of Science ®Google Scholar
• Blickenstaff, J. C. (2005). Women and science careers: Leaky pipeline or gender filter? Gender and Education, 17(4), 369–386. https://doi.org/10.1080/09540250500145072
   Web of Science ®Google Scholar
• Blotnicky, K. A., Franz-Odendaal, T., French, F., & Joy, P. (2018). A study of the correlation between STEM career knowledge, mathematics self-efficacy, career interests, and career activities on the likelihood of pursuing a STEM career among middle school students. International Journal of STEM Education, 5(1), 22. https://doi.org/10.1186/s40594-018-0118-3
   PubMedGoogle Scholar
• Bohrnstedt, G. W., Zhang, J., Park, B. J., Ikoma, S., Broer, M., & Ogut, B. (2020). Mathematics identity, self-efficacy, and interest and their relationships to mathematics achievement: A longitudinal analysis. In R. T. Serpe R. Stryker & B. Powell (Eds.), Identity and Symbolic Interaction (pp. 169–210). Springer. https://doi.org/10.1007/978-3-030-41231-9_7
   Google Scholar
• Brown, V. A. (2021). An introduction to linear mixed-effects modeling in R. Advances in Methods and Practices in Psychological Science, 4(1), 2515245920960351. https://doi.org/10.1177/2515245920960351
   Google Scholar
• Butler-Barnes, S. T., Cheeks, B., Barnes, D. L., & Ibrahim, H. (2021). STEM pipeline: Mathematics beliefs, attitudes, and opportunities of racial/ethnic minority girls. Journal for STEM Education Research, 1–28. https://doi.org/10.1007/s41979-021-00059-x
   Google Scholar
• Camp, T., Adrion, W., Bizot, B., Davidson, S., Hall, M., Hambrusch, S., Walker, E., & Zweben, S. (2017). Generation CS: The growth of computer science. ACM Inroads, 8(2), 44–50. https://doi.org/10.1145/3084362
   Google Scholar
• Campe, S., Denner, J., Green, E., & Torres, D. (2020). Pair programming in middle school: Variations in interactions and behaviors. Computer Science Education, 30(1), 22–46. https://doi.org/10.1080/08993408.2019.1648119
   Google Scholar
• Casad, B. J., Oyler, D. L., Sullivan, E. T., McClellan, E. M., Tierney, D. N., Anderson, D. A., Greeley, P. A., Fague, M. A., & Flammang, B. J. (2018). Wise psychological interventions to improve gender and racial equality in STEM. Group Processes & Intergroup Relations, 21(5), 767–787. https://doi.org/10.1177/1368430218767034
   Web of Science ®Google Scholar
• Catsambis, S. (1994). The path to math: gender and racial-ethnic differences in mathematics participation from middle school to high school. Sociology of Education, 67(3), 199–215. https://doi.org/10.2307/2112791
   Web of Science ®Google Scholar
• Cheryan, S., Ziegler, S. A., Montoya, A. K., & Jiang, L. (2017). Why are some STEM fields more gender balanced than others? Psychological Bulletin, 143(1), 1–35. https://doi.org/10.1037/bul0000052
   PubMed Web of Science ®Google Scholar
• Ching, Y. H., Yang, D., Wang, S., Baek, Y., Swanson, S., & Chittoori, B. (2019). Elementary school student development of STEM attitudes and perceived learning in a STEM integrated robotics curriculum. TechTrends, 63(5), 590–601. https://doi.org/10.1007/s11528-019-00388-0
   Web of Science ®Google Scholar
• Clements, D. H., & Sarama, J. (2016). Math, science, and technology in the early grades. The Future of Children, 26(2), 75–94. doi:https://doi.org/10.1353/foc.2016.0013
   Web of Science ®Google Scholar
• Code.org, C. S. T. A., & Alliance, E. C. E. P. (2022). 2022 State of Computer Science Education: Illuminating Disparities. https://advocacy.code.org/stateofcs
   Google Scholar
• Degenhart, S. H., Wingenbach, G. J., Dooley, K. E., Lindner, J. R., Mowen, D. L., & Johnson, L. (2007). Middle school students’ attitudes toward pursuing careers in science, technology, engineering, and math. NACTA Journal, 52–59. https://www.jstor.org/stable/43766229
   Google Scholar
• Denissen, J. J., Zarrett, N. R., & Eccles, J. S. (2007). I like to do it, I’m able, and I know I am: Longitudinal couplings between domain‐specific achievement, self‐concept, and interest. Child Development, 78(2), 430–447. https://doi.org/10.1111/j.1467-8624.2007.01007.x
   PubMed Web of Science ®Google Scholar
• Doerschuk, P., Liu, J., & Mann, J. (2007). Pilot summer camps in computing for middle school girls: From organization through assessment. In Proceedings of the 12th annual SIGCSE conference on Innovation and technology in computer science education, Dundee, Scotland (pp. 4–8). https://doi.org/10.1145/1268784.1268789
   Google Scholar
• Donohue, M. C., Gamst, A. C., & Edland, S. D. (2021). Package ‘longpower’. CRAN. https://cran.r-project.org/web/packages/longpower/longpower.pdf
   Google Scholar
• Downes, T., & Looker, D. (2011). Factors that influence students’ plans to take computing and information technology subjects in senior secondary school. Computer Science Education, 21(2), 175–199. https://doi.org/10.1080/08993408.2011.579811
   Google Scholar
• Du, J., & Wimmer, H. (2019). Hour of code: A study of gender differences in computing. Information Systems Education Journal, 17(4), 91–100. http://isedj.org/2019-17/n4/ISEDJv17n4p91.html
   Google Scholar
• Else-Quest, N., Mineo, C. 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
• Estrada, M., Burnett, M., Campbell, A. G., Campbell, P. B., Denetclaw, W. F., Gutiérrez, C. G., Okpodu, C. M., John, G. H., Matsui, J., McGee, R., Okpodu, C. M., Robinson, T. J., Summers, M. F., Werner-Washburne, M., & Zavala, M. (2016). Improving underrepresented minority student persistence in STEM. CBE—Life Sciences Education, 15(3), es5. https://doi.org/10.1187/cbe.16-01-0038
   PubMed Web of Science ®Google Scholar
• Friend, M. (2015). Middle school girls’ envisioned future in computing. Computer Science Education, 25(2), 152–173. https://doi.org/10.1080/08993408.2015.1033128
   Web of Science ®Google Scholar
• Gainor, K. A., & Lent, R. W. (1998). Social cognitive expectations and racial identity attitudes in predicting the math choice intentions of black college students. Journal of Counseling Psychology, 45(4), 403–413. https://doi.org/10.1037/0022-0167.45.4.403
   Web of Science ®Google Scholar
• Geary, D. C., Hoard, M. K., Nugent, L., Chu, F., Scofield, J. E., & Ferguson Hibbard, D. (2019). Sex differences in mathematics anxiety and attitudes: concurrent and longitudinal relations to mathematical competence. Journal of Educational Psychology, 111(8), 1447–1461. https://doi.org/10.1037/edu0000355
   PubMed Web of Science ®Google Scholar
• Gladstone, J. R., Häfner, I., Turci, L., Kneißler, H., & Muenks, K. (2018). Associations between parents and students’ motivational beliefs in mathematics and mathematical performance: The role of gender. Contemporary Educational Psychology, 54, 221–234. https://doi.org/10.1016/j.cedpsych.2018.06.009
   Web of Science ®Google Scholar
• Gottlieb, J. J. (2018). STEM career aspirations in Black, Hispanic, and White ninth‐grade students. Journal of Research in Science Teaching, 55(10), 1365–1392. https://doi.org/10.1002/tea.21456
   Web of Science ®Google Scholar
• Griffith, A. L. (2010). Persistence of women and minorities in STEM field majors: Is it the school that matters? Economics of Education Review, 29(6), 911–922. https://doi.org/10.1016/j.econedurev.2010.06.010
   Web of Science ®Google Scholar
• Grover, S., Fisler, K., Lee, I., & Yadav, A. (2020, February). Integrating computing and computational thinking into K-12 STEM learning. In Proceedings of the 51st ACM Technical Symposium on Computer Science Education, Portland, OR, USA (pp. 481–482). https://doi.org/10.1145/3328778.3366970
   Google Scholar
• Huang, X., Zhang, J., & Hudson, L. (2019). Impact of math self-efficacy, math anxiety, and growth mindset on math and science career interest for middle school students: The gender moderating effect. European Journal of Psychology of Education, 34(3), 621–640. https://doi.org/10.1007/s10212-018-0403-z
   Web of Science ®Google Scholar
• Huber, P. J. (1967, June). The behavior of maximum likelihood estimates under nonstandard conditions. In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, Berkeley, CA, USA (Vol. 1, No. 1, pp. 221–233).
   Google Scholar
• Hughes, R. A., Heron, J., Sterne, J. A., & Tilling, K. (2019). Accounting for missing data in statistical analyses: Multiple imputation is not always the answer. International Journal of Epidemiology, 48(4), 1294–1304. https://doi.org/10.1093/ije/dyz032
   PubMed Web of Science ®Google Scholar
• Hur, J. W., Andrzejewski, C. E., & Marghitu, D. (2017). Girls and computer science: Experiences, perceptions, and career aspirations. Computer Science Education, 27(2), 100–120. https://doi.org/10.1080/08993408.2017.1376385
   Web of Science ®Google Scholar
• 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
   PubMed Web of Science ®Google Scholar
• Jo Campbell, N., & Williams, J. E. (1990). Relation of Computer Attitudes and Computer Attributions to Enrollment in High School Computer Courses and Self-Perceived Computer Proficiency. Journal of Research on Computing in Education, 22(3), 276–289. https://doi.org/10.1080/08886504.1990.10781921
   Google Scholar
• Kim, A. Y., Sinatra, G. M., & Seyranian, V. (2018). Developing a STEM identity among young women: A social identity perspective. Review of Educational Research, 88(4), 589–625. https://doi.org/10.3102/0034654318779957
   Web of Science ®Google Scholar
• Kong, S. C., Chiu, M. M., & Lai, M. (2018). A study of primary school students’ interest, collaboration attitude, and programming empowerment in computational thinking education. Computers & Education, 127, 178–189. https://doi.org/10.1016/j.compedu.2018.08.026
   Web of Science ®Google Scholar
• Lent, R. W., Brown, S. D., & Hackett, G. (1994). Toward a unifying social cognitive theory of career and academic interest, choice, and performance [Monograph]. Journal of Vocational Behavior, 45, 79–122. https://doi.org/10.1006/jvbe.1994.1027
   Web of Science ®Google Scholar
• Lent, R. W., Brown, S. D., & Hackett, G. (2002). Social cognitive career theory. D. Brown Ed. In Career Choice and Development (4th ed., pp. 255–311). Jossey-Bass.
   Google Scholar
• Lent, R. W., Morris, T. R., Penn, L. T., & Ireland, G. W. (2019). Social–cognitive predictors of career exploration and decision-making: Longitudinal test of the career self-management model. Journal of Counseling Psychology, 66(2), 184–194. https://doi.org/10.1037/cou0000307
   PubMed Web of Science ®Google Scholar
• Leyva, E., Walkington, C., Perera, H., & Bernacki, M. (2022). Making mathematics relevant: An examination of student interest in mathematics, interest in STEM careers, and perceived relevance. International Journal of Research in Undergraduate Mathematics Education, 1–30. https://doi.org/10.1007/s40753-021-00159-4
   Google Scholar
• Lichtenberger, E., & George-Jackson, C. (2013). Predicting high school students’ interest in majoring in a STEM field: insight into high school students’ postsecondary plans. Journal of Career and Technical Education, 28(1), 19–38. https://eric.ed.gov/?id=EJ1043177
   Google Scholar
• Lin, L., Lee, T., & Snyder, L. A. (2018). Math self-efficacy and STEM intentions: A person-centered approach. Frontiers in Psychology, 9, 2033. https://doi.org/10.3389/fpsyg.2018.02033
   PubMed Web of Science ®Google Scholar
• Linnel, N., Dayal, A., Gonsalves, P., Kakodkar, M., Ribiero, B., Starr, A., & Zdankus, J. (2020). Curated pathways to innovation: Personalized CS education to promote diversity. Journal of Computing Sciences in Colleges, 35(10), 39–45. https://www.ccsc.org/publications/journals/SW2020.pdf#page=39
   Google Scholar
• Liu, G., & Liang, K. Y. (1997). Sample size calculations for studies with correlated observations. Biometrics, 53(3), 937–947. https://doi.org/10.2307/2533554
   PubMed Web of Science ®Google Scholar
• London, J. S., Lee, W. C., & Hawkins Ash, C. D. (2021). Potential engineers: A systematic literature review exploring black children’s access to and experiences withSTEM. Journal of Engineering Education, 110(4), 1003–1026. https://doi.org/10.1002/jee.20426
   Web of Science ®Google Scholar
• Low, K. S. D., Yoon, M., Roberts, B. W., & Rounds, J. (2005). The stability of vocational interests from early adolescence to middle adulthood: A quantitative review of longitudinal studies. Psychological Bulletin, 131(5), 713–737. https://doi.org/10.1037/0033-2909.131.5.713
   PubMed Web of Science ®Google Scholar
• Madley-Dowd, P., Hughes, R., Tilling, K., & Heron, J. (2019). The proportion of missing data should not be used to guide decisions on multiple imputation. Journal of Clinical Epidemiology, 110, 63–73. https://doi.org/10.1016/j.jclinepi.2019.02.016
   PubMed Web of Science ®Google Scholar
• Maltese, A. V., & Tai, R. H. (2011). Pipeline persistence: Examining the association of educational experiences with earned degrees in STEM among U.S. students. Science Education, 95(5), 877–907. https://doi.org/10.1002/sce.20441
   Web of Science ®Google Scholar
• McNeish, D. (2018). Thanks coefficient alpha, we’ll take it from here. Psychological Methods, 23(3), 412–433. https://doi.org/10.1037/met0000144
   PubMed Web of Science ®Google Scholar
• Meade, A. W., & Craig, S. B. (2012). Identifying careless responses in survey data. Psychological Methods, 17(3), 437–455. https://doi.org/10.1037/a0028085
   PubMed Web of Science ®Google Scholar
• Meade, A. W., Lautenschlager, G. J., & Hecht, J. E. (2005). Establishing measurement equivalence and invariance in longitudinal data with item response theory. International Journal of Testing, 5(3), 279–300. https://doi.org/10.1207/s15327574ijt0503_6
   Google Scholar
• 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. https://doi.org/10.3389/fpsyg.2015.00037
   PubMed Web of Science ®Google Scholar
• Moote, J., Archer, L., DeWitt, J., & MacLeod, E. (2020). Science capital or STEM capital? Exploring relationships between science capital and technology, engineering, and maths aspirations and attitudes among young people aged 17/18. Journal of Research in Science Teaching, 57(8), 1228–1249. https://doi.org/10.1002/tea.21628
   Web of Science ®Google Scholar
• Niepel, C., Burrus, J., Greiff, S., Lipnevich, A. A., Brenneman, M. W., & Roberts, R. D. (2018). Students’ beliefs and attitudes toward mathematics across time: A longitudinal examination of the theory of planned behavior. Learning and Individual Differences, 63, 24–33. https://doi.org/10.1016/j.lindif.2018.02.010
   Web of Science ®Google Scholar
• O’Brien, L. T., Blodorn, A., Adams, G., Garcia, D. M., & Hammer, E. (2015). Ethnic variation in gender-STEM stereotypes and STEM participation: An intersectional approach. Cultural Diversity and Ethnic Minority Psychology, 21(2), 169–180. https://doi.org/10.1037/a0037944
   PubMed Web of Science ®Google Scholar
• Olivera-Aguilar, M. (2013). Impact of violations of longitudinal measurement invariance in latent growth models and autoregressive quasi-simplex models. [Dissertation].Arizona State University. https://core.ac.uk/download/pdf/79567262.pdf
   Google Scholar
• Pajares, F., & Graham, L. (1999). Self-efficacy, motivation constructs, and mathematics performance of entering middle school students. Contemporary Educational Psychology, 24(2), 124–139. https://doi.org/10.1006/ceps.1998.0991
   PubMed Web of Science ®Google Scholar
• Pantic, K., Clarke-Midura, J., Poole, F., Roller, J., & Allan, V. (2018). Drawing a computer scientist: Stereotypical representations or lack of awareness? Computer Science Education, 28(3), 232–254. https://doi.org/10.1080/08993408.2018.1533780
   Web of Science ®Google Scholar
• Plant, E. A., Baylor, A. L., Doerr, C. E., & Rosenberg-Kima, R. B. (2009). Changing middle-school students’ attitudes and performance regarding engineering with computer-based social models. Computers & Education, 53(2), 209–215. https://doi.org/10.1016/j.compedu.2009.01.013
   Web of Science ®Google Scholar
• Pollock, L., McCoy, K., Carberry, S., Hundigopal, N., & You, X. (2004). Increasing high school girls’ self-confidence and awareness of CS through a positive summer experience. ACM SIGCSE Bulletin, 36(1), 185–189. https://doi.org/10.1145/1028174.971369
   Google Scholar
• Ram, N., & Grimm, K. (2007). Using simple and complex growth models to articulate developmental change: Matching theory to method. International Journal of Behavioral Development, 31(4), 303–316. https://doi.org/10.1177/0165025407077751
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Rogers, M. E., & Creed, P. A. (2011). A longitudinal examination of adolescent career planning and exploration using a social cognitive career theory framework. Journal of Adolescence, 34(1), 163–172. https://doi.org/10.1016/j.adolescence.2009.12.010
   PubMed Web of Science ®Google Scholar
• Ryoo, J. J., Tanksley, T., Estrada, C., & Margolis, J. (2020). Take space, make space: How students use computer science to disrupt and resist marginalization in schools. Computer Science Education, 30(3), 337–361. https://doi.org/10.1080/08993408.2020.1805284
   Web of Science ®Google Scholar
• Sadler, P. M., Sonnert, G., Hazari, Z., & Tai, R. (2012). Stability and volatility of STEM career interest in high school: A gender study. Science Education, 96(3), 411–427. https://doi.org/10.1002/sce.21007
   Web of Science ®Google Scholar
• Saw, G., Chang, C., & Chan, H. (2018). Cross-sectional and longitudinal disparities in STEM career aspirations at the intersection of gender, race/ethnicity, and socioeconomic status. Educational Researcher, 47(8), 525–531. https://doi.org/10.3102/0013189X18787818
   Web of Science ®Google Scholar
• Seo, E., Shen, Y., & Alfaro, E. C. (2019). Adolescents’ beliefs about math ability and their relations to STEM career attainment: Joint consideration of race/ethnicity and gender. Journal of Youth and Adolescence, 48(2), 306–325. https://doi.org/10.1007/s10964-018-0911-9
   PubMed Web of Science ®Google Scholar
• Štuikys, V., & Burbaitė, R. (2018). Smart STEM-driven computer science education: Theory, methodology and robot-based practices. Springer. https://doi.org/10.1007/978-3-319-78485-4
   Google Scholar
• Su, R. (2020). The three faces of interests: An integrative review of interest research in vocational, organizational, and educational psychology. Journal of Vocational Behavior, 116, 103240. https://doi.org/10.1016/j.jvb.2018.10.016
   Web of Science ®Google Scholar
• Taub, R., Armoni, M., & Ben-Ari, M. (2012). CS unplugged and middle-school students’ views, attitudes, and intentions regarding CS. ACM Transactions on Computing Education (TOCE), 12(2), 1–29. https://doi.org/10.1145/2160547.2160551
   Google Scholar
• Thomas, J. A., & Strunk, K. K. (2017). Expectancy‐value and children’s science achievement: Parents matter. Journal of Research in Science Teaching, 54(6), 693–712. https://doi.org/10.1002/tea.21382
   Web of Science ®Google Scholar
• Tsybulsky, D. (2020). Digital curation for promoting personalized learning: A study of secondary-school science students’ learning experiences. Journal of Research on Technology in Education, 52(3), 429–440. https://doi.org/10.1080/15391523.2020.1728447
   Web of Science ®Google Scholar
• Unfried, A., Faber, M., Stanhope, D. S., & Wiebe, E. (2015). The development and validation of a measure of student attitudes toward science, technology, engineering, and math (S-STEM). Journal of Psychoeducational Assessment, 33(7), 622–639. https://doi.org/10.1177/0734282915571160
   Web of Science ®Google Scholar
• Urdan, T. (2019). Curated Pathways to Innovation Survey. Unpublished survey instrument.
   Google Scholar
• U.S. Bureau of Labor Statistics [BLS]. (2021). Occupational Outlook Handbook: Computer and Information Research Scientists. Retrieved 11 Feb 2021. From https://www.bls.gov/ooh/computer-and-information-technology/computer-and-information-research-scientists.htm#tab-6
   Google Scholar
• U.S. National Science Foundation [NSF]. (2019, Sept). Science and Engineering Labor Force: Demographic Trends of the S&E Workforce. https://ncses.nsf.gov/pubs/nsb20198/demographic-trends-of-the-s-e-workforce
   Google Scholar
• U.S. National Science Foundation [NSF] & National Center for Science and Engineering Statistics [NCSES]. (2019, March). Women, minorities, and persons with disabilities in science and engineering: glossary and key to acronyms. Report created with surveys by the Surveys conducted by the National Center for Science and Engineering Statistics (NCSES). Retrieved 12 Feb 2021 from https://ncses.nsf.gov/pubs/nsf19304/digest/glossary-and-key-to-acronyms
   Google Scholar
• van Buuren, S., & Groothuis-Oudshoorn, K. (2011). mice: Multivariate imputation by chained equations in R. Journal of Statistical Software, 45(3), 1–68. https://doi.org/10.18637/jss.v045.i03
   Web of Science ®Google Scholar
• Vandenberg, J., Tsan, J., Boulden, D., Zakaria, Z., Lynch, C., Boyer, K. E., & Wiebe, E. (2020). Elementary students’ understanding of CS terms. ACM Transactions on Computing Education (TOCE), 20(3), 1–19. https://doi.org/10.1145/3386364
   Web of Science ®Google Scholar
• Varma, R. (2018). US science and engineering workforce: Underrepresentation of women and minorities. American Behavioral Scientist, 62(5), 692–697. https://doi.org/10.1177/000276421876884
   Web of Science ®Google Scholar
• Vegas, E., & Fowler, B. (2020). What do we know about the expansion of K-12 computer science education. Brookings Institute. Retrieved February, 2. 2021https://www.brookings.edu/research/what-do-we-know-about-the-expansion-of-k-12-computer-science-education/.
   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
• Wang, M. T., Binning, K. R., Del Toro, J., Qin, X., & Zepeda, C. D. (2021). Skill, thrill, and will: The role of metacognition, interest, and self‐control in predicting student engagement in mathematics learning over time. Child Development, 92(4), 1369–1387. https://doi.org/10.1111/cdev.13531
   PubMed 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, M. T., & Degol, J. L. (2017). Gender gap in science, technology, engineering, and mathematics (STEM): Current knowledge, implications for practice, policy, and future directions. Educational Psychology Review, 29(1), 119–140. https://doi.org/10.1007/s10648-015-9355-x
   PubMed Web of Science ®Google Scholar
• Wang, Z., Rimfeld, K., Shakeshaft, N., Schofield, K., & Malanchini, M. (2020). The longitudinal role of mathematics anxiety in mathematics development: Issues of gender differences and domain-specificity. Journal of Adolescence, 80, 220–232. https://doi.org/10.1016/j.adolescence.2020.03.003
   PubMed Web of Science ®Google Scholar
• Wiebe, E., Unfried, A., & Faber, M. (2018). The relationship of STEM attitudes and career interest. EURASIA Journal of Mathematics, Science and Technology Education, 14(10). https://doi.org/10.29333/ejmste/92286
   Google Scholar
• You, S. (2013). Gender and ethnic differences in precollege mathematics coursework related to science, technology, engineering, and mathematics (STEM) pathways. School Effectiveness and School Improvement, 24(1), 64–86. https://doi.org/10.1080/09243453.2012.681384
   Web of Science ®Google Scholar