Abstract
Excellent Science, Technology, Engineering, and Mathematics (STEM) employees are in constant demand worldwide. The current study explores Israeli students’ choices to study and pursue careers in STEM fields, while students’ level of excellence in mathematics in secondary school is explored as a predictor for choosing a STEM track in future study and employment, as well as a predictor for the level of success in completing a STEM degree. The theoretical framework for the study is based on the task performance model of the social cognitive career theory which focuses on human behavior in the context of career choice. The study presents a big data analysis based on about 650,000 records retrieved from the Central Bureau of Statistics in Israel for several points in time over the last one and a half decades, to demonstrate choice patterns over the years. We analyzed a representative sample using systematic sampling over the last decade and a half, from secondary school, higher education, and postgraduate STEM-related populations who are employed in STEM professions. Our study presents a scale for mathematics excellence, which reflects a combination of study level and level of success. Our findings reveal that choosing a more advanced level of mathematics in secondary school, as opposed to the level of success in these studies, is the best predictor for choosing STEM as a major in secondary school, completing a STEM bachelor’s degree, succeeding in a STEM bachelor’s degree, and making a STEM career choice.
Similar content being viewed by others
Notes
Excellence in mathematics was defined by level of study in mathematics in secondary school: basic, standard, and advanced levels, as explained in the method section.
Since the planning stage for STEM study occurs in parallel to studying mathematics in secondary school, we could not refer to choosing STEM as a major in secondary school as a predictable variable. Accordingly, since success at work cannot be quantified with objective measures, predicting success will only refer to the acting stage.
References
Adkins, M., & Noyes, A. (2018). Do advanced mathematics skills predict success in biology and chemistry degrees? International Journal of Science and Mathematics Education, 16(3), 487–502. https://doi.org/10.1007/s10763-016-9794-y.
Archer, L., DeWitt, J., & Osborne, J. (2015). Is science for us? Black students’ and parents’ views of science and science careers. Science Education, 99(2), 199–237.
Ashford, S. N., Lanehart, R. E., Kersaint, G. K., Lee, R. S., & Kromrey, J. D. (2016). STEM pathways: Examining persistence in rigorous math and science course taking. Journal of Science Education and Technology, 25(6), 961–975. https://doi.org/10.1007/s10956-016-9654-0.
Avargil, S., Kohen, Z., & Dori, Y. J. (2020). Trends and perceptions of choosing chemistry as a major and a career. Chemistry Education Research and Practice, 21, 668–684.
Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice- Hall, Inc.
Bottia, M. C., Stearns, E., Mickelson, R. A., Moller, S., & Parker, A. D. (2015). The relationships among high school STEM learning experiences and students’ intent to declare and declaration of a STEM major in college. Teachers College Record, 17(3), 1–46.
Brown, S. D., & Lent, R. W. (2019). A social cognitive view of career development and guidance. In J. A. Athanasou & H. N. Perera (Eds.), International handbook of career guidance (pp. 147–166). Berlin, Germany: Springer International Publishing.
Cannady, M. A., Greenwald, E., & Harris, K. N. (2014). Problematizing the STEM pipeline metaphor: is the STEM pipeline metaphor serving our students and the STEM workforce?. Science Education, 98(3), 443–460.
Chachashvili-Bolotin, S., Milner-Bolotin, M., & Lissitsa, S. (2016). Examination of factors predicting secondary students’ interest in tertiary STEM education. International Journal of Science Education, 38(3), 366–390. https://doi.org/10.1080/09500693.2016.1143137.
European Centre for the Development of Vocational Training (CEDEFOP). (2016). European sectoral trends: The next decade. Retrieved Nov., 2020 from: https://www.cedefop.europa.eu/en/publications-and-resources/publications/8093.
Fares, M. (2018). How did a crisis in mathematics education lead to a positive reform? In N. Movshovitz-Hadar (Ed.), K-12 mathematics education in Israel: Issues and Innovations (pp. 21–28). Singapore: World scientific publishing co. Pte. Ltd.. https://doi.org/10.1142/9789813231191_0002.
Funk, C., & Parker, K. (2018). Women and men in STEM often at odds over workplace equity. Washington, DC: Pew Research Center.
Hackett, G., & Lent, R. W. (1992). Theoretical advances and current inquiry in career psychology. In S. D. Brown & R. W. Lent (Eds.), Handbook of counseling psychology (pp. 419–452). Hoboken, NJ: JohnWiley & Sons.
Hazari, Z., Sonnert, G., Sadler, P. M., & Shanahan, M. C. (2010). Connecting high school physics experiences, outcome expectations, physics identity, and physics career choice: A gender study. Journal of Research in Science Teaching, 47(8), 978–1003.
Heckhausen, H., & Gollwitzer, P. M. (1987). Thought contents and cognitive functioning in motivational versus volitional states of mind. Motivation and Emotion, 11(2), 101–120. https://doi.org/10.1007/BF00992338.
Holmes, K., Gore, J., Smith, M., & Lloyd, A. (2018). An integrated analysis of school students’ aspirations for STEM careers: Which student and school factors are most predictive? International Journal of Science and Mathematics Education, 16(4), 655–675. https://doi.org/10.1007/s10763-016-9793-z.
Honey, M., Pearson, G., & Schweingruber, H. A. (2014). STEM integration in K-12 education: Status, prospects, and an agenda for research (Vol. 500). Washington, DC: National Academies Press.
Jeffries, D., Curtis, D. D., & Conner, L. N. (2019). Student factors influencing STEM subject choice in year 12: A structural equation model using PISA/LSAY data. International Journal of Science and Mathematics Education, 18, 441–461. https://doi.org/10.1007/s10763-019-09972-5.
Lee, S. W., Min, S., & Mamerow, G. P. (2015). Pygmalion in the classroom and the home: Expectation’s role in the pipeline to STEMM. Teachers College Record, 117(9), 1–40.
Lent, R. W., Brown, S. D., & Hackett, G. (1994). Toward a unifying social cognitive theory of career and academic interest, choice, and performance. Journal of Vocational Behavior, 45(1), 79–122. https://doi.org/10.1006/jvbe.1994.1027.
Lent, R. W., Hackett, G., & Brown, S. D. (1999). A social cognitive view of school-to-work transition. The Career Development Quarterly, 47(4), 297–311.
Lysaght, M. J., Jaklenec, A., & Deweerd, E. (2008). Great expectations: Private sector activity in tissue engineering, regenerative medicine, and STEM cell therapeutics. Tissue Engineering Part A, 14(2), 305–315. https://doi.org/10.1089/tea.2007.0267.
Maltese, A. V., & Tai, R. H. (2011). Pipeline persistence: Examining the association of educational experiences with earned degrees in STEM among US students. Science Education, 95(5), 877–907. https://doi.org/10.1002/sce.20441.
National Academies of Sciences‚ Engineering‚ and Medicine (2016). Promising practices for strengthening the regional STEM workforce development ecosystem. Washington, DC: National Academies Press.
National Research Council (2010). Rising above the gathering storm, revisited: Rapidly approaching category 5. Washington, DC: National Academies Press.
Nicholas, J., Poladian, L., Mack, J., & Wilson, R. (2015). Mathematics preparation for university: Entry, pathways and impact on performance in first year science and mathematics subjects. International Journal of Innovation in Science and Mathematics Education, 23(1), 37–51.
Nugent, G., Barker, B., Welch, G., Grandgenett, N., Wu, C., & Nelson, C. (2015). A model of factors contributing to STEM learning and career orientation. International Journal of Science Education, 37(7), 1067–1088. https://doi.org/10.1080/09500693.2015.1017863.
Organization for Economic Co-operation and Development (2019). Education at a glance 2019. Paris, France: Author.
Prieto, E., & Dugar, N. (2017). An enquiry into the influence of mathematics on students’ choice of STEM careers. International Journal of Science and Mathematics Education, 15(8), 1501–1520. https://doi.org/10.1007/s10763-016-9753-7.
Productivity Commission (2016). Digital disruption: What do governments need to do? (commission research paper). Canberra, Australia: Australian Government.
Reinhold, S., Holzberger, D., & Seidel, T. (2018). Encouraging a career in science: A research review of secondary schools’ effects on students’ STEM orientation. Studies in Science Education, 54(1), 69–103. https://doi.org/10.1080/03057267.2018.1442900.
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.
Sadler, P. M., & Tai, R. H. (2007). The two high-school pillars supporting college science. Science, 317(5837), 457–458. https://doi.org/10.1126/science.1144214.
Sahin, A., Ekmekci, A., & Waxman, H. C. (2018). Collective effects of individual, behavioral, and contextual factors on high school students’ future STEM career plans. International Journal of Science and Mathematics Education, 16(1), 69–89.
Shulruf, B., Hattie, J., & Tumen, S. (2008). The predictability of enrolment and first year university results from secondary school performance: The New Zealand National Certificate of Educational Achievement. Studies in Higher Education, 33(6), 685–698. https://doi.org/10.1080/03075070802457025.
Staus, N. L., Lesseig, K., Lamb, R., Falk, J., & Dierking, L. (2019). Validation of a measure of STEM interest for adolescents. International Journal of Science and Mathematics Education, 18, 279–293. https://doi.org/10.1007/s10763-019-09970-7.
Tai, R. H., Liu, C. Q., Maltese, A. V., & Fan, X. (2006). Planning early for careers in science. Science, 312(5777), 1143–1144. https://doi.org/10.1126/science.1128690.
Tyson, W., Lee, R., Borman, K. M., & Hanson, M. A. (2007). Science, technology, engineering, and mathematics (STEM) pathways: High school science and math coursework and postsecondary degree attainment. Journal of Education for Students Placed at Risk, 12(3), 243–270.
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.
Funding
This study was partially funded by the National Institute for Testing and Evaluation (NITE) and by Samuel Neaman Institute, Technion, Israel Institute of Technology.
Author information
Authors and Affiliations
Corresponding author
Supplementary Information
ESM 1
(DOCX 13 kb)
Rights and permissions
About this article
Cite this article
Kohen, Z., Nitzan, O. Excellence in Mathematics in Secondary School and Choosing and Excelling in STEM Professions over Significant Periods in Life. Int J of Sci and Math Educ 20, 169–191 (2022). https://doi.org/10.1007/s10763-020-10138-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10763-020-10138-x