Single-sex schools

4Research questions

LSAY09 offers a unique opportunity to re-evaluate this debate with recent data and in the context of the major changes that have affected the science participation of Australian students in the last decade (Ainley, Kos & Nicholas 2008). The goal of this paper is, thus, to establish whether single-sex schooling continues to make little difference to the gendered patterns of science participation of the recent cohorts of students and whether the gender gap in science participation is as it was a decade ago.

Although this paper briefly considers the differences in science performance between adolescents attending single-sex and coeducational schools, it aims to focus attention on two other aspects of science participation. The first is science subject choices in Year 11, since the upper secondary stage of schooling is the first opportunity for most Australian high school students to specialise, by selecting themselves out of certain fields of study. The second form of science engagement examined here is a student’s career plan, reported between their fifteenth and sixteenth birthdays.

With respect to these two forms of science engagement, the research questions posed in this paper are as follows:

Across all schools, do boys and girls continue to select different science subjects and formulate different science-related career plans?

Are gendered patterns of science engagement systematically different between students in single‐sex and coeducational settings?

It must be noted that, while the literature on gendered patterns of science participation pays attention primarily to the disadvantage of girls, segregation is not necessarily disadvantageous for one sex only. Gender segregation is a phenomenon with the potential to adversely affect both young men and women. Given that comparable numbers of young women and men engage in science (Sikora & Pokropek 2011 and table 3), if girls are underrepresented in certain fields, boys must be underrepresented in others.

5How are life and physical sciences defined in this paper?

The concentration of males and females in different fields of science has been well documented (Hill, Corbett & Rose 2010; OECD 2006; Sikora & Pokropek 2012a). In Australia, Fullarton and Ainley (2000, p.v1) noted in their analyses of subject choice among Australian students:

Gender was found to be one of the student characteristics accounting for the greatest proportion of variation in student enrolments. As found in previous subject choice reports, males predominate in the areas of mathematics, particularly in higher level mathematics, physical sciences, technical studies, computer studies and physical education.

There is no established and widely accepted terminology to denote the distinction between ‘feminine’ and ‘masculine’ fields of science, although its existence is well known to science educators. Some authors refer to it as the contrast between ‘soft’ and ‘hard’ sciences (Kjrnsli & Lie 2011), or between ‘life’ and ‘quantitative’ sciences (Kessel & Nelson 2011), or between ‘physical’ and ‘life’ sciences (Ainley & Daly 2002). This paper uses Ainley and Daly’s labels of life and physical sciences, but any choice of labels is to a degree arbitrary and thus it is important to peruse the list of science fields included in each category (provided in appendix B). In principle, fields and courses with significant biology, health-related or environment-focused content are treated in this analysis as ‘life science’, while fields with explicit physics, chemistry or geology content are treated as ‘physical science’. Occupational plans related to biology and health services are assumed to relate to life science, while engineering, mathematical and computing occupations are assumed to relate to physical science. This latter distinction is adopted from the OECD framework previously used for international comparisons (Sikora & Pokropek 2011). Analysis at the level of particular subject titles or occupational titles is impossible because of the large numbers of science subjects offered across the states and territories and the equally large numbers of occupational titles that group relatively few students. Therefore, some categorisation of science fields along the dimensions of care versus technology (Barone 2011) is necessary to highlight the gendered concentration of students within particular areas of science, technology, engineering and mathematics. In contrast, treating science as one homogeneous field of study conceals systematic gendered differences in science engagement (Anlezark et al. 2008).

Data and measurement

This paper utilises data from the upper secondary school students who participated in LSAY and who were between 15 and 16 years of age in 2009 — LSAY09. The 2009 Program for International Student Assessment (PISA) constitutes the first wave of LSAY09. It was conducted in Australia on a two-stage stratified representative sample of students, generated by sampling first schools and then students within schools. Schools were stratified by sector and state or territory. In 2010 and 2011 respondents of the initial PISA 2009 survey were contacted for an annual follow-up interview. Of 14 251 students who participated in PISA, 8759 participated in LSAY in 2010 and 7626 participated in 2011 (NCVER 2012, p.12).

6What is science engagement in this paper?

This study first considers science subject uptake and then science-oriented vocational plans, since recent studies suggest that a high level of academic achievement in science does not necessarily lead students to pursue science at tertiary level (Anlezark et al. 2008).

Although it has been pointed out that ‘the combination of subjects studied by students in the senior secondary years says more about a student’s educational orientation than does enrolment in any given subject’ (Ainley & Daly 2002, p.250), enrolment in a life science course or a physical science course is a good indicator of two different types of patterns of science course taking. For this reason this analysis relies on modelling enrolment in at least one life science subject or one physical science subject in Year 11. Science in this instance excludes mathematics courses (as per listing in appendix B), as they are not only outside the scope of this paper, but they also require a different coding scheme, one which distinguishes advanced and applied courses. A small number of science subjects could not be classified into either physical or life science categories because of the broad scope of their content (see appendix B) and were omitted from the analysis. However, this omission does not bias results, because the numbers of students enrolled in these subjects were negligible. Students who took a life science subject were coded 1 on the relevant dummy variable and all other students were coded zero. A similar procedure was applied to create a dummy variable that denotes taking a physical science subject, so all students with information on subject taking were included in the analyses. It is important to note that the patterns of science engagement for Year 12 in this dataset strongly resemble those of Year 11. As Year 11 data are less affected by attrition,¹ they are the focus of this analysis.

A high level of academic achievement in science does not necessarily lead students to pursue science as a profession; therefore, a student’s plan to work in science-related occupations is another focus of this paper. Students in LSAY09 were asked what occupation they expected to work in when they reached 30 years of age. This is the indicator of a science-related career plan, converted into two dichotomous variables, named a ‘plan to work in a physical science occupation’ and a ‘plan to work in a life science occupation’, which were created using the list of occupations at the end of appendix B. Students who named one of them were coded 1 on the relevant variable, while others were coded 0. Missing data on these variables, which amount to 32% in LSAY09, have been imputed using multiple chain imputations (Royston 2004).