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Student Factors Influencing STEM Subject Choice in Year 12: a Structural Equation Model Using PISA/LSAY Data

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Abstract

This study investigates factors that influenced the science, technology, engineering and mathematics (STEM) subject enrolment decisions of Year 12 students in Australia. Structural equation modelling (SEM) is used to develop a model using Programme for International Student Assessment (PISA) and Longitudinal Surveys of Australian Youth (LSAY) data with participating students (N  =  7442) from 356 schools. An adapted version of the theory of planned behaviour (TPB), a behavioural prediction model, is used as the guiding conceptual framework. Students’ demographic background, attitudes towards science and achievement in science and mathematics at age 15 are used as predictors for subsequent enrolment in STEM subjects in Year 12. Gender, socio-economic status (SES) and immigrant status (native vs. non-native) are shown to be contributing factors. The personal value of science, enjoyment of science, self-concept in science and achievement (mathematics and science) are mediating factors in the model. These findings provide schools, policymakers and educational advisors with a greater understanding of the factors that influence Australian students’ decisions of whether to enrol in a STEM subject at Year 12. Evidence provided allows key stakeholders to take a more targeted approach to enhance STEM participation for students from varying demographic backgrounds.

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Notes

  1. The lists of included and excluded subjects that were used to create the outcome variable are available on request from the corresponding author.

References

  • Agresti, A. (2003). Categorical data analysis (2nd ed.). New York, NY: Wiley.

    Google Scholar 

  • Ainley, J., Kos, J., & Nicholas, M. (2008). Participation in science, mathematics and technology in australian education. Camberwell, Victoria: ACER.

    Google Scholar 

  • Ainley, M., & Ainley, J. (2011). A cultural perspective on the structure of student interest in science. International Journal of Science Education, 33(1), 51–71. https://doi.org/10.1080/09500693.2010.518640.

    Article  Google Scholar 

  • Archer, L., DeWitt, J., Osborne, J., Dillon, J., Willis, B., & Wong, B. (2012). “Balancing acts”: Elementary school girls’ negotiations of femininity, achievement, and science. Science Education, 96(6), 967–989. https://doi.org/10.1002/sce.21031.

    Article  Google Scholar 

  • Archer, L., DeWitt, J., Osborne, J., Dillon, J., Willis, B., & Wong, B. (2013). ‘Not girly, not sexy, not glamorous’: Primary school girls’ and parents’ constructions of science aspirations. Pedagogy, Culture & Society, 21(1), 171–194. https://doi.org/10.1080/14681366.2012.748676.

    Article  Google Scholar 

  • Atweh, B., Taylor, S., & Singh, P. (2005). School curriculum as cultural commodity in the construction of young people’s post-school aspirations. Paper presented at the Australian Association for Research in Education Annual Conference, University of Western Sydney, Parramatta.

  • Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice Hall.

    Google Scholar 

  • Barrington, F., & Brown, P. (2005). Comparison of year 12 pre-tertiary mathematics subjects in Australia 2004–2005. Melbourne, Australia: International Centre of Excellence for Education in Mathematics, Australian Mathematical Sciences Institute.

  • Bennett, J., & Hogarth, S. (2009). Would you want to talk to a scientist at a party? High school students’ attitudes to school science and to science. International Journal of Science Education, 31(14), 1975–1998. https://doi.org/10.1080/09500690802425581.

    Article  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.

    Article  Google Scholar 

  • Broadley, K. (2015). Entrenched gendered pathways in science, technology, engineering and mathematics: Engaging girls through collaborative career development. Australian Journal of Career Development, 24(1), 27–38. https://doi.org/10.1177/1038416214559548.

    Article  Google Scholar 

  • Browne, M. W., & Cudeck, R. (1992). Alternative ways of assessing model fit. Sociological Methods & Research, 21(2), 230–258. https://doi.org/10.1177/0049124192021002005.

    Article  Google Scholar 

  • Byrne, B. M. (2012). Structural equation modeling with Mplus: Basic concepts, applications, and programming. New York, NY: Routledge.

    Google Scholar 

  • Ceci, S. J., & Williams, W. M. (2010). The mathematics of sex: How biology and society conspire to limit talented women. Oxford, England: Oxford University Press.

    Google Scholar 

  • Clyne, R. J. (2014). The factors influencing secondary school girls' mathematics subject selections. (Master's thesis). The University of Melbourne, Australia. Retrieved from http://hdl.handle.net/11343/42253

  • Cole, M. (2013). Literature review update: Student identity in relation to science, technology, engineering and mathematics subject choices and career aspirations. Melbourne, Australia: Australian Council of Learned Academies.

    Google Scholar 

  • Coleman, L. J., & Cross, T. L. (2005). Being gifted in school: An introduction to development, guidance, and teaching (2nd ed.). Waco, TX: Prufrock Press.

    Google Scholar 

  • Cundiff, J. L., Vescio, T. K., Loken, E., & Lo, L. (2013). Do gender–science stereotypes predict science identification and science career aspirations among undergraduate science majors? Social Psychology of Education, 16(4), 541–554. https://doi.org/10.1007/s11218-013-9232-8.

    Article  Google Scholar 

  • Daly, P., & Ainley, J. (1999). Student participation in mathematics courses in Australian secondary schools. The Irish Journal of Education, 30, 77–95.

    Google Scholar 

  • De Loof, H., Struyf, A., Boeve-de Pauw, J., & Van Petegem, P. (2017). Teachers' motivating style and students' engagement and motivation in STEM. Paper presented at the European Science Education Research Association Conference, Dublin, Ireland.

  • Deloitte. (2012). Measuring the economic benefits of mathematical science research in the UK. Retrieved from https://epsrc.ukri.org/newsevents/pubs/deloitte-measuring-the-economic-benefits-of-mathematical-science-research-in-the-uk/.

  • Department of Further Education, Employment, Science and Technology. (2011). A science, technology, engineering and mathematics (STEM) skills strategy for South Australia. Adelaide, Australia: Government of South Australia.

    Google Scholar 

  • Department of Further Education, Employment, Science and Technology. (2014). Investing in science: An action plan for prosperity through science, research and innovation. Adelaide, Australia: Government of South Australia.

    Google Scholar 

  • Eccles, J. (2009). Who am I and what am I going to do ith my life?: Personal and collective identities as motivators of action. Educational Psychologist, 44(2), 78–89. https://doi.org/10.1080/00461520902832368.

    Article  Google Scholar 

  • Eccles, 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). San Francisco, CA: W. H. Freeman.

    Google Scholar 

  • Else-Quest, N. M., 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.

    Article  Google Scholar 

  • Field, A. (2013). Discovering statistics using IBM SPSS statistics (4th ed.). Los Angeles, CA: SAGE Publications.

  • Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley Publishing Company.

  • Fishbein, M., & Ajzen, I. (2010). Predicting and changing behavior: The reasoned action approach. New York, NY: Psychology press (Taylor and Francis).

  • Francis, B., Archer, L., Moote, J., de Witt, J., & Yeomans, L. (2017). Femininity, science, and the denigration of the girly girl. British Journal of Sociology of Education, 38(8), 1097–1110. https://doi.org/10.1080/01425692.2016.1253455.

    Article  Google Scholar 

  • Fullarton, S., & Ainley, J. (2000). Subject choice by students in Year 12 in Australian secondary schools (LSAY Research Report No. 15). Melbourne, Australia: ACER.

    Google Scholar 

  • Fullarton, S., Walker, M., Ainley, J. & Hillman, K. (2003). Patterns of participation in Year 12 (LSAY Research Report No. 33). Melbourne, Australia: ACER.

  • George, D., & Mallery, P. (2016). IBM SPSS Statistics 23 step by step: A simple guide and reference. New York, NY: Routledge.

  • Gill, T., & Bell, J. F. (2013). What factors determine the uptake of a-level physics? International Journal of Science Education, 35(5), 753–772. https://doi.org/10.1080/09500693.2011.577843.

    Article  Google Scholar 

  • Gore, J., Holmes, K., Smith, M., Fray, L., McElduff, P., Weaver, N., & Wallington, C. (2017). Unpacking the career aspirations of Australian school students: Towards an evidence base for university equity initiatives in schools. Higher Education Research & Development, 36(7), 1383–1400. https://doi.org/10.1080/07294360.2017.1325847.

    Article  Google Scholar 

  • Graham, J. W., Olchowski, A. E., & Gilreath, T. D. (2007). How many imputations are really needed? Some practical clarifications of multiple imputation theory. Prevention Science, 8(3), 206–213. https://doi.org/10.1007/s11121-007-0070-9.

    Article  Google Scholar 

  • Hassan, G. (2008). Attitudes toward science among Australian tertiary and secondary school students. Research in Science & Technological Education, 26(2), 129–147. https://doi.org/10.1080/02635140802034762.

    Article  Google Scholar 

  • Henriksen, E. K., Dillon, J., & Ryder, J. (2015). Understanding student participation and choice in science and technology education. Dordrecht, Netherlands: Springer.

    Book  Google Scholar 

  • Hobbs, L., Jakab, C., Millar, V., Prain, V., Redman, C., Speldewinde, C., . . . van Driel, J.  (2017). Girls' future - our future: The Invergowrie Foundation STEM report. Melbourne, Australia: Invergowrie Foundation.

  • Holmes, K., Gore, J., Smith, M., & Lloyd, A. (2017). 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.

    Article  Google Scholar 

  • Hu, L. t., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118.

    Article  Google Scholar 

  • IBM Corp. (Released 2015). IBM SPSS Statistics for Windows (version 23.0). Armonk, NY: IBM Corp.

  • Kaplan, R. M., Chambers, D. A., & Glasgow, R. E. (2014). Big data and large sample size: A cautionary note on the potential for bias. Clinical and Translational Science, 7(4), 342–346. https://doi.org/10.1111/cts.12178.

    Article  Google Scholar 

  • Kennedy, J., Quinn, F., & Lyons, T. (2018). The keys to STEM: Australian Year 7 students’ attitudes and intentions towards science, mathematics and technology courses. Research in Science Education. https://doi.org/10.1007/s11165-018-9754-3.

  • Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). New York, NY: Guilford Press.

    Google Scholar 

  • Lee, A. (2011). Mathematical learning instruction and teacher motivation factors affecting science technology engineering and math (STEM) major choices in 4-year colleges and universities: Multilevel structural equation modeling (Doctoral dissertation). Retrieved from ProQuest Dissertations and Theses. (Order No. AAT 3471457).

  • 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.

    Article  Google Scholar 

  • Little, R. J. A., & Rubin, D. B. (1987). Statistical analysis with missing data. New York, NY: Wiley.

    Google Scholar 

  • Lyons, T., & Quinn, F. (2010). Choosing science: Understanding the declines in senior high school science enrolments (SiMERR Australia, Trans.). Armidale, Australia: University of New England.

  • Ma, X. (1997). Reciprocal relationships between attitude toward mathematics and achievement in mathematics. The Journal of Educational Research, 90(4), 221–229. https://doi.org/10.1080/00220671.1997.10544576.

    Article  Google Scholar 

  • Ma, X. (2001). Participation in advanced mathematics: Do expectation and influence of students, peers, teachers, and parents matter? Contemporary Educational Psychology, 26(1), 132–146. https://doi.org/10.1006/ceps.2000.1050.

    Article  Google Scholar 

  • MacPhee, D., Farro, S., & Canetto, S. S. (2013). Academic self-efficacy and performance of underrepresented STEM majors: Gender, ethnic, and social class patterns. Analyses of Social Issues and Public Policy, 13(1), 347–369. https://doi.org/10.1111/asap.12033.

    Article  Google Scholar 

  • Marjoribanks, K. (2005). Family background, adolescents' educational aspirations, and young Australian adults' educational attainment. International Education Journal, 6(1), 104–112.

    Google Scholar 

  • McGaw, B. (2006). Achieving quality and equity education. Bob Hawke Prime Ministerial Centre, Australia: University of South Australia.

    Google Scholar 

  • Muthén, B. O., du Toit, S. H. C., & Spisic, D. (1997). Robust inference using weighted least squares and quadratic estimating equations in latent variable modeling with categorical and continuous outcomes (Unpublished Technical Paper). Retrived from https://www.statmodel.com/download/Article_075.pdf.

  • Muthén, L. K., & Muthén, B. O. (1998-2012). Mplus user’s guide (7th ed.). Los Angeles, CA: Muthén & Muthén.

    Google Scholar 

  • National Academies of Sciences‚ Engineering‚ and Medicine. (2016). Promising practices for strengthening the regional STEM workforce development ecosystem. Washington, DC: National Academies Press.

    Google Scholar 

  • National Centre for Vocational Education Research. (2006-2009). Longitudinal surveys of Australian youth (wave 1 - wave 4), 2006–2009 [dataset]. Retrieved from https://www.lsay.edu.au/data/access

  • National Centre for Vocational Education Research. (2016). Longitudinal surveys of Australian youth (LSAY) 2006 cohort user guide. Adelaide, Australia: NCVER.

  • National Science Board. (2004). An emerging and critical problem of the science and engineering labor force: A companion to science and engineering indicators 2004. Arlington, VA: National Science Foundation.

    Google Scholar 

  • National Science Board. (2007). A national action plan for addressing the critical needs of the U.S. science, technology, engineering, and mathematics education system. Arlington, VA: National Science Foundation.

    Google Scholar 

  • Office of the Chief Scientist. (2012). Mathematics, engineering & science in the national interest. Canberra, Australia: Australian Government.

    Google Scholar 

  • Organisation for Economic Co-operation and Development. (2006). Programme for International Student Assessment, 2006 [Dataset]. Retrieved from http://www.oecd.org/pisa/pisaproducts/database-pisa2006.htm.

  • Organisation for Economic Co-operation and Development. (2014). PISA 2012 technical report. Paris, France: Author.

  • Porche, M., Grossman, J. M., & Dupaya, K. C. (2016). New American scientists: first generation immigrant status and college STEM aspirations. Journal of Women and Minorities in Science and Engineering, 22(1), 1–21. https://doi.org/10.1615/JWomenMinorScienEng.2016015227.

    Article  Google Scholar 

  • PricewaterhouseCoopers Australia. (2015). Future-proofing Australia’s workforce by growing skills in science, technology, engineering and maths (STEM) (P. S. R. Centre, Trans.). Retrieved from https://www.pwc.com.au/pdf/a-smart-move-pwc-stem-report-april-2015.pdf.

  • 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.

    Article  Google Scholar 

  • Productivity Commission. (2016). Digital disruption: What do governments need to do? (Commission Research Paper). Canberra, Australia: Australian Government.

    Google Scholar 

  • Royal Academy of Engineering. (2012). Jobs and growth: The importance of engineering skills to the UK economy. London, England: The Royal Academy of Engineering.

    Google Scholar 

  • Rubin, D. B. (1987). Multiple imputation for nonresponse in surveys. New York, NY: Wiley.

  • Sahin, A., Ekmekci, A., & Waxman, H. C. (2017). The relationships among high school STEM learning experiences, expectations, and mathematics and science efficacy and the likelihood of majoring in STEM in college. International Journal of Science Education, 39(11), 1549–1572. https://doi.org/10.1080/09500693.2017.1341067.

    Article  Google Scholar 

  • Schwab, K. (2017). The fourth industrial revolution. London, England: Penguin.

    Google Scholar 

  • Sikora, J. (2014). Gendered pathways into post-secondary study of science. Adelaide, Australia: NCVER.

    Google Scholar 

  • Sikora, J., & Pokropek, A. (2012). Gender segregation of adolescent science career plans in 50 countries. Science Education, 96(2), 234–264. https://doi.org/10.1002/sce.20479.

    Article  Google Scholar 

  • Smyth, E., & Hannan, C. (2006). School effects and subject choice: The uptake of scientific subjects in Ireland. School Effectiveness and School Improvement, 17(3), 303–327. https://doi.org/10.1080/09243450600616168.

    Article  Google Scholar 

  • Taylor, R. C. (2015). Using the theory of planned behaviour to understand students’ subject choices in post-compulsory education. Research Papers in Education, 30(2), 214–231. https://doi.org/10.1080/02671522.2014.880732.

    Article  Google Scholar 

  • The Australian Industry Group. (2015). Progressing STEM skills in Australia. Sydney, Australia: Ai Group.

  • Tripney, J., Newman, M., Bangpan, M., Niza, C., MacKintosh, M., & Sinclair, J. (2010). Subject choice in STEM: Factors influencing young people (aged 14–19) in education: A systematic review of the UK literature. London, England: Wellcome Trust.

    Google Scholar 

  • Vartanian, T. P. (2011). Secondary data analysis. New York, NY: Oxford University Press.

    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.

    Article  Google Scholar 

  • Wang, X. (2012). Modeling student choice of STEM fields of study: Testing a conceptual framework of motivation, high school learning, and postsecondary context of support. Retrieved from ERIC database. (ED529700).

  • Wigfield, A. (1994). Expectancy-value theory of achievement motivation: a developmental perspective. Educational Psychology Review, 6(1), 49–78. https://doi.org/10.1007/bf02209024.

    Article  Google Scholar 

  • Wigfield, A., & Eccles, J. S. (2000). Expectancy–value theory of achievement motivation. Contemporary Educational Psychology, 25(1), 68–81. https://doi.org/10.1006/ceps.1999.1015.

    Article  Google Scholar 

  • Wu, M. (2005). The role of plausible values in large-scale surveys. Studies in Educational Evaluation, 31(2), 114–128. https://doi.org/10.1016/j.stueduc.2005.05.005.

    Article  Google Scholar 

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Funding

The first author of this study was funded by the Australian Government Research Training Program Scholarship during his PhD candidacy.

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  1. College of Education, Psychology and Social Work, Flinders University, Sturt Road, Bedford Park, Australia

    David Jeffries, David D. Curtis & Lindsey N. Conner

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  1. David Jeffries
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Correspondence to David Jeffries.

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Data from PISA is publicly and freely available. Consent for participants in PISA was obtained via parental consent (implicit or explicit). Data from LSAY was accessed after permission from NCVER and ADA was received. All participants in LSAY gave informed consent via telephone, online or in person.

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Jeffries, D., Curtis, D.D. & Conner, L.N. Student Factors Influencing STEM Subject Choice in Year 12: a Structural Equation Model Using PISA/LSAY Data. Int J of Sci and Math Educ 18, 441–461 (2020). https://doi.org/10.1007/s10763-019-09972-5

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