Skip to main content
Log in

Establishing a STEM Pipeline: Trends in Male and Female Enrollment and Performance in Higher Level Secondary STEM Courses

  • Published:
International Journal of Science and Mathematics Education Aims and scope Submit manuscript

Abstract

The purpose of this study was to understand enrollment and performance differences between male and females in higher level secondary STEM courses. This study analyzes performance and enrollment of 355,688 secondary students in higher level STEM courses. This research also enabled an exploration of country level differences. The enrollment research questions are evaluated using chi-square tests, frequency tables, and histograms. Performance research questions are analyzed with hierarchical linear regression and ANOVA with post hocs and Cohen’s d effect size measures. Results suggest that females enroll much less frequently in higher level secondary STEM courses. Females and males perform equally well.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Notes

  1. Students may register for individual IB courses and take exams and are referred to as Diploma Course students.

  2. The core content of each SL/HL course overlaps, with more teaching hours dedicated to each component in the HL option, allowing for additional topics and/or more depth. Mathematics HL requires students to complete 5 h of external exams and an internal exploration project. The SL course assesses students on two external exam papers instead of three and also includes short response items and extended response items (IBO, 2012).

  3. Legal status (at level 2) was most significant in the Design Technology model. A reduction in variance of 26.7 % is meaningful, and this indicates that the legal status of a school may play an important role in exam performance. It is worth noting that Design Technology had the smallest sample size, and that a three-level model may not have been the most appropriate method of analysis here due to the uneven distribution of public/private schools in this subject. It is likely that the model is picking up on the proportion of private schools in the sample (Hox, 2010).

References

  • Bieri-Buschor, C., Berweger, S., Keck-Frei, A. & Kappler, C. (2014). Majoring in STEM—What accounts for women’s career decision making? A mixed methods study. The Journal of Educational Research, 107(3), 167–176. doi:10.1080/00220671.2013.788989.

    Article  Google Scholar 

  • Bollen, K. A. (1989). Structural equations with latent variables. New York, NY: Wiley.

    Book  Google Scholar 

  • Brincks, A. (April 14, 2012). Centering decisions in three-level, cross-sectional, contextual models. Annual meeting. Lecture conducted from AERA, Vancouver.

  • Chinn, S. (2000). A simple method for converting an odds ratio to effect size for use in meta-analysis. Statistics in Medicine, 19(22), 3127–3131.

    Article  Google Scholar 

  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Enders, C. K. & Tofighi, D. (2007). Centering predictor variables in a cross-sectional multilevel models: A new look at an old issue. Psychological Methods, 12(2), 121–138.

    Article  Google Scholar 

  • Greenwood, P. & Nukulin, M. (1996). A guide to chi-squared testing. New York, NY: Wiley.

    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. doi:10.1016/j.econedurev.2010.06.010.

    Article  Google Scholar 

  • Halpern, D., Aronson, J., Reimer, N., Simpkins, S., Star, J., & Wentzel, K. (2007). Encouraging girls in math and science: IES practice guide (NCER 2007–2003). Washington, DC: Institute of Educational Sciences, U.S. Department of Education. Available: http://ies.ed.gov/ncee/wwc/pdf/practiceguides/20072003.pdf

  • Hill, C., Corbet, C. & Rose, A. (2010). Why so few? Women in science, technology, engineering, and mathematics. Washington, DC: American Association of University Women (AAUW).

    Google Scholar 

  • Howie, S. & Plomp, T. (2008). Narrowing the gap? Studies in Educational Evaluation, 34(2), 53–130.

    Article  Google Scholar 

  • Hox, J. (2010). Multilevel analysis: Techniques and applications (2nd ed.). New York, NY: Routledge.

    Google Scholar 

  • Hyde, J. & Mertz, J. (2009). Gender, culture, and mathematics performance. Proceedings of the National Academy of Sciences, 106, 8,801–8,807.

    Article  Google Scholar 

  • Ing, M. (2014). Gender differences in the influence of early perceived parental support on student mathematics and science achievement and stem career attainment. International Journal of Science & Mathematics Education, 12(5), 1221–1239.

    Article  Google Scholar 

  • International Baccalaureate Organization (IBO). (2012). Handbook of procedures for the diploma programme. Geneva, Switzerland: IBO.

  • Lomax, R. (2007). Multiple comparison procedures. In Statistical concepts: A second course (3rd ed., pp. 27–51). Mahwah, NJ: Erlbaum.

  • Ma, D. X., Ma, L. & Bradley, K. (2008). Using multilevel modeling to investigate school effects. In A. Connell & D. B. McCoach (Eds.), Multilevel modeling of educational data (pp. 59–110). Charlotte, NC: IAP.

    Google Scholar 

  • Nagy, G., Garrett, J., Trautwein, U., Cortina, K. S., Baumert, J. & Eccles, J. S. (2008). Gendered high school course selection as a precursor of gendered careers: The mediating role of self-concept and intrinsic value. In H. M. G. Watt & J. S. Eccles (Eds.), Gender and occupational outcomes. Longitudinal assessments of individual, social, and cultural influences (pp. 115–143). Washington, DC: American Psychological Association.

    Chapter  Google Scholar 

  • Nagy, G., Trautwein, U., Baumert, J., Koller, O. & Garret, J. (2006). Gender and course selection in upper secondary education: Effects of academic self-concept and intrinsic value. Educational Research and Evaluation, 12, 323–345.

    Article  Google Scholar 

  • National Science Foundation (NSF) (2006). Science and engineering indicators, (Volume 1). Arlington, VA: National Science Foundation. NSB 06-01.

  • National Science Foundation (NSF) (2010). Preparing the next generation of STEM innovators: Identifying and developing our nation’s human capital. Arlington, VA: National Science Foundation. NSB 10-33.

  • Niederle, M. & Vesterlund, L. (2007). Do women shy away from competition? Do men compete too much? Quarterly Journal of Economics, 122(3), 1067–1101.

    Article  Google Scholar 

  • O’Shea, M., Heilbronner, N. N. & Reis, S. M. (2010). Characteristics of academically talented women who achieve at high levels on the scholastic achievement test-mathematics. Journal of Advanced Academics, 21(2), 234–271.

    Article  Google Scholar 

  • Raudenbush, S. W. & Bryk, A. S. (2002). Hierarchical linear models applications and data analysis methods (2nd ed.). Thousand Oaks, CA: Sage.

    Google Scholar 

  • Sarkisian, N. (2013). Hierarchical linear modeling [PDF document]. Retrieved from http://www.sarkisian.net/sc708/learning_HLM6.pdf.

  • Smith, E. & Gorard, S. (2011). Is there a shortage of scientists? A re-analysis of supply for the UK. British Journal of Educational Studies, 59(2), 159–177.

    Article  Google Scholar 

  • Snijders, T. & Bosker, R. (2012). An introduction to basic and advanced multilevel modeling (2nd ed.). Thousand Oaks, CA: Sage.

    Google Scholar 

  • Sun, S., Pan, W. & Wang, L. (2010). A comprehensive review of effect size reporting and interpreting practices in academic journals in education and psychology. Journal of Educational Psychology, 102(4), 989–1004.

    Article  Google Scholar 

  • Thompson, R. & Bolin, G. (2011). Indicators of success in STEM majors: A cohort study. Journal of College Admission, 212, 18–24.

    Google Scholar 

  • Tyson, W., Lee, R., Borman, K. M. & Hanson, M. (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.

  • U.S. Department of Education (2009). Student who study science, technology, engineering and mathematics (STEM) in post secondary education. Washington, DC: U.S. Department of Education.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Liz Bergeron.

Electronic supplementary material

Below is the link to the electronic supplementary material.

ESM 1

(PDF 59 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bergeron, L., Gordon, M. Establishing a STEM Pipeline: Trends in Male and Female Enrollment and Performance in Higher Level Secondary STEM Courses. Int J of Sci and Math Educ 15, 433–450 (2017). https://doi.org/10.1007/s10763-015-9693-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10763-015-9693-7

Keywords

Navigation