33 SES 08 B, Science, Engineers and Education - A Gender Perspective
Adolescents’ career plans are indicative of their future educational and also labour market decisions. These associations also hold for employment in Science, Technology, Engineering and Mathematics (STEM) which is an area of particular interest not only for its high importance for countries’ economic growth, but also for the massive and non-decreasing gender-segregation in the field. To better understand why men and women end up studying certain subjects and doing certain jobs, we need to look at how they develop their career plans at early ages.
On the individual level, science and maths performance are strong predictors of STEM career choices. However, it is not only the actual performance that matters: students’ attitudes toward science and maths are also important. In particular, subject self-efficacy (the extent to which students believe that they can handle given subject-related tasks effectively) and enjoyment of science (intrinsic interest or interest value) have successfully been linked to STEM career plans and also to STEM higher education choices.
Concerning enjoyment, single-country studies and also studies that combine several countries have demonstrated that students, who enjoy doing science and have more interest in the subject are also more likely to opt for STEM also when their abilities and self-efficacy are held constant. Moreover, there is some evidence that gender-differences in enjoyment (more broadly: subjective task values) related to math and physical sciences explain some of the gender gap in the corresponding occupational choices as girls tend to attach less intrinsic value to STEM-related subjects.
Our preliminary analyses of PISA 2015 data however demonstrate that the underlying processes that link science enjoyment to related career choices show complex patterns not only by gender but also by country. In particular, we find that in some European countries, the association between science enjoyment and STEM career choices is either consistently positive across genders (e.g. Austria, Belgium, Denmark, Germany, Italy, Sweden, the UK…), or it is more pronounced for males (France, Ireland and the Netherlands). In a second group of countries however, enjoyment of science is either unrelated to STEM choices (e.g. Bulgaria, the Czech Republic, Latvia, Romania…), or – in a smaller number of cases – shows some positive effect for females only (Croatia, Estonia, Slovenia).
The lack of relevance of science enjoyment either for both genders or for males in the second group of countries is an interesting phenomenon. At this stage we can only speculate about its roots. As it is mainly countries with lower income that belong to this group, we propose that in less affluent societies, students might be less concerned with intrinsic than with extrinsic rewards of their future jobs as their major concern is to achieve labour market security and also a high income. As STEM occupations offer high returns across Europe, it is possible that in poorer countries, students – with a given level of achievement and self-efficacy – are more inclined to choose such careers irrespective of their interest for the topic. An alternative hypothesis however suggests that it is part of the socio-economic heritage of the post-socialist past that disconnects enjoyment and career choices in a high number of European countries.
The paper will explore the complex associations between enjoyment of science and STEM career choices of the students, and reveal the role of gender and some country-characteristics in moderating these associations. We aim to better understand whether making science learning more enjoyable for students can boost students’ interest in related careers and whether such attempts can also help to reduce the gender segregation in STEM. We expect that the answers to these questions to show remarkable cross-country differences.
PISA data includes comparable information on occupational expectations of adolescents as well as test-scores on students’ science performance and variables related to their attitudes for science. From these, students’ science self-efficacy (their self-beliefs whether they could perform a series of science-related tasks either easily or not) and the enjoyment of science scale will be used. The latter was constructed from five items describing students’ enjoyment in activities related to science. Gender will be depicted by a dummy variable. On the country level, two alternative measures will be used to identify the underlying cross-country differences: Human Development Index (HDI) will account for the different levels of affluence between the countries and a dummy for a socialist past will depict potential cultural and historical factors. The measure of student career expectations was constructed from the following question: What kind of job do you expect to have when you are about 30 years old? Our definition of STEM occupation is based on ISCO-08 codes and includes the following job-categories: Science and engineering professionals; Information and communications technology professionals; Science and engineering associate professionals; Information and communication technicians. A dummy variable was constructed with STEM occupations coded as 1, and 0 otherwise. In the analyses, PISA 2015 data for the European countries will be used. To deal with missing values, multiple imputation by chained equations (ICE) was applied. The analysis will employ the General Multilevel Structural Equations Modelling Framework for assessing multilevel mediation and moderation. With this framework we are able to separate between effects (country level associations) from within effects (individual level associations). In the analyses we will focus on relations between STEM career choices of individuals, science score, self-efficacy and enjoyment and gender. In moderation analysis we will explain the cross country variation of relations between predictors and dependent variables using country level variables, which will be the Human Development and distinction between post-socialist and non-post-socialist countries. Mediation analysis will investigate how the relation between gender and STEM choices is mediated by enjoyment.
We expect that between-country differences in HDI and the historical past of the European countries explain between country differences in the relationships between science-enjoyment and STEM career choices also when students’ science performance, their self-efficacy and other control variables are held constant. Further, these relationships will also vary by gender. In European countries with a post-socialist heritage (or with lower levels of HDI) students’ enjoyment is a weaker predictor of their interest in STEM occupations than it is other countries. If at all, a positive association between science enjoyment and STEM career choices exists among females, but not among males. On the other hand, in countries without a socialist past (or in the more affluent countries of Europe), science enjoyment remains a strong predictor of students; related career choices and this association is particularly strong among males. We expect the post-socialist factor to be a stronger predictor than HDI and we will present possible explanations related to the different socio-historical paths of the two groups of European countries. The presentation will also discuss policy implications.
Bandura, A. (1977). Self-efficacy: Toward a Unifying Theory of Behavioral Change. Psychological Review, 84(2), 191–215. Bandura, A. (1999). Social cognitive theory: An agentic perspective. Asian Journal of Social Psychology, (2), 21–41. Blasko, Zs., Pokropek, A. & Sikora, J. (2018): Science career plans of adolescents: patterns, trends and gender divides. JRC Science for Policy Report. European Commission, JRC 2018 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), 78–89. https://doi.org/10.1080/00461520902832368 Eccles, J. (2011). Gendered Educational and Occupational Choices: Applying the Eccles et al. Model of Achievement-Related Choices. International Journal of Behavioral Development, 35(3), 195–201. Eccles, J. S. (1994). Understanding women’s educational and occupational choices. Psychology of Women Quarterly, 18(4), 585–609. Goos, M., Hathaway, I., Konings, J., & Vandeweyer, M. (2013). High-technology employment in the European Union. Retrieved from https://lirias.kuleuven.be/handle/123456789/445755 Han, S. W. (2017). What motivates high-school students to pursue STEM careers? The influence of public attitudes towards science and technology in comparative perspective. Journal of Education and Work, 30(6), 632–652. https://doi.org/10.1080/13639080.2017.1329584 Koller, O., Baumert, J., & Schnabel, K. (2001). Does Interest Matter? The Relationship between Academic Interest and Achievement in Mathematics. Journal for Research in Mathematics Education, 32(5), 448. https://doi.org/10.2307/749801 Mcmaster, N. C. (2017). What role do enjoyment and students’ perception of ability play in social disparities in subject choices at university? UCL Working Paper. Riegle-Crumb, C., Moore, C., & Ramos-Wada, A. (2011). Who wants to have a career in science or math? exploring adolescents’ future aspirations by gender and race/ethnicity. Science Education, 95(3), 458–476. https://doi.org/10.1002/sce.20431 Royston, P., & others. (2004). Multiple imputation of missing values. Stata Journal, 4(3), 227–41. Sikora, J. (2014). Gendered pathways into the post-secondary study of science (Occasional Paper). NCVER, Australian Government. Tai, R. H., Liu, C. Q., Maltese, A. V., & Fan, X. (2006). Planning early for careers in science. Science, 312(5777), 1143–1144.
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