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An Integrated Analysis of School Students' Aspirations for STEM Careers: Which Student and School Factors Are Most Predictive?

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Title: An Integrated Analysis of School Students' Aspirations for STEM Careers: Which Student and School Factors Are Most Predictive?
Language: English
Authors: Holmes, Kathryn, Gore, Jennifer, Smith, Max, Lloyd, Adam
Source: International Journal of Science and Mathematics Education. Apr 2018 16(4):655-675.
Availability: Springer. 233 Spring Street, New York, NY 10013. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-348-4505; e-mail: service-ny@springer.com; Web site: http://www.springerlink.com
Peer Reviewed: Y
Page Count: 21
Publication Date: 2018
Document Type: Journal Articles
Reports - Research
Descriptors: Meta Analysis, STEM Education, Science Careers, Predictor Variables, Science Education, Student Surveys, Career Choice, Regression (Statistics), Academic Achievement, Science Interests, Student Attitudes, Cultural Capital, Parent Background, Gender Differences, Occupational Aspiration
DOI: 10.1007/s10763-016-9793-z
ISSN: 1571-0068
Abstract: Declining enrolments in science, technology, engineering and mathematics (STEM) disciplines and a lack of interest in STEM careers are concerning at a time when society is becoming more reliant on complex technologies. We examine student aspirations for STEM careers by drawing on surveys conducted annually from 2012 to 2015. School students in years 3 to 12 (n = 6492) were asked to indicate their occupational choices. A logistic regression analysis showed that being in the older cohorts, possessing high cultural capital, being male, having a parent in a STEM occupation and high prior achievement in reading and numeracy, were significant. This analysis provides a strong empirical basis for school-based initiatives to improve STEM participation. In particular, strategies should target the following: the persistent lack of interest by females in some careers, improving student academic achievement in both literacy and numeracy and expanding knowledge of STEM careers, especially for students without familial STEM connections.
Abstractor: As Provided
Number of References: 54
Entry Date: 2018
Accession Number: EJ1172611
Database: ERIC
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  Value: <anid>AN0128462140;[3d0g]01apr.18;2018Mar15.12:07;v2.2.500</anid> <title id="AN0128462140-1">An Integrated Analysis of School Students’ Aspirations for STEM Careers: Which Student and School Factors Are Most Predictive? </title> <p>Declining enrolments in science, technology, engineering and mathematics (STEM) disciplines and a lack of interest in STEM careers are concerning at a time when society is becoming more reliant on complex technologies. We examine student aspirations for STEM careers by drawing on surveys conducted annually from 2012 to 2015. School students in years 3 to 12 (<italic>n</italic> = 6492) were asked to indicate their occupational choices. A logistic regression analysis showed that being in the older cohorts, possessing high cultural capital, being male, having a parent in a STEM occupation and high prior achievement in reading and numeracy, were significant. This analysis provides a strong empirical basis for school-based initiatives to improve STEM participation. In particular, strategies should target the following: the persistent lack of interest by females in some careers, improving student academic achievement in both literacy and numeracy and expanding knowledge of STEM careers, especially for students without familial STEM connections.</p> <p>Career aspiration; Gender; STEM education; Student achievement</p> <hd id="AN0128462140-2">Introduction</hd> <p>Internationally, there is concern over the low number of people studying and working in the science, technology, engineering and mathematics (STEM) areas (Office of the Chief Scientist, [<reflink idref="bib34" id="ref1">34</reflink>] ; Wang & Degol, [<reflink idref="bib50" id="ref2">50</reflink>] ; Wilson & Mack, [<reflink idref="bib53" id="ref3">53</reflink>] ). Declining enrolments and participation in STEM disciplines is a significant issue because building capacity in the STEM fields is pivotal to maintaining/increasing productivity and international competiveness (Marginson, Tytler, Freeman & Roberts, [<reflink idref="bib31" id="ref4">31</reflink>] ; Office of the Chief Scientist, [<reflink idref="bib34" id="ref5">34</reflink>] ). In Australia, student participation in year 12 mathematics and science subjects is declining and for science is at its lowest point in 20 years (Kennedy, Lyons & Quinn, [<reflink idref="bib25" id="ref6">25</reflink>] ). Furthermore, comparative international tests of mathematical and scientific literacy reveal that Australian students’ performance has consistently dropped since 2003 (Office of the Chief Scientist, [<reflink idref="bib35" id="ref7">35</reflink>] ; Thomson, Wennert, O’Grady & Rodrigues, [<reflink idref="bib48" id="ref8">48</reflink>] ). At the same time, over the coming decades, demand in some STEM fields is predicted to grow substantially with an 11.7% increase in engineers, a 9% increase in business analysts and programmers and an 8% increase in natural and physical scientists (PwC, [<reflink idref="bib37" id="ref9">37</reflink>] ).</p> <p>Focus on the STEM disciplines has arisen not only out of a perceived lack of skilled workers in new highly technological fields of employment but also in relation to concerns about STEM being taught as discrete subjects in schools rather than as part of an integrated curriculum (Office of the Chief Scientist, [<reflink idref="bib35" id="ref10">35</reflink>] ). Within the Australian context, this decline in STEM participation in both the education sector and the workforce has prompted the Chief Scientist to call for school curricula to reflect the evolving nature of STEM, provide better STEM teaching, address the under-representation of females and disadvantaged students in STEM and, at all levels of education, highlight the future needs of a well-qualified STEM workforce to help guide students’ study decisions (Office of the Chief Scientist, [<reflink idref="bib34" id="ref11">34</reflink>] , [<reflink idref="bib35" id="ref12">35</reflink>] ).</p> <p>In this paper, we analyse the aspirations for STEM careers of a sample of Australian school students in relation to student background and school-related factors in order to shed light on the relative influence of these factors. At a time when shortfalls in STEM participation are becoming critical, a better understanding of who is and is not interested in STEM will provide timely insights for teachers, school careers advisors and tertiary educators, concerning fertile avenues for redressing the apparent decline in student interest.</p> <hd id="AN0128462140-3">Aspirations for STEM Careers</hd> <p>The relationship between the career choices of adults and their earlier aspirations have been studied extensively over many decades (Hartung, Porfeli & Vondracek, [<reflink idref="bib20" id="ref13">20</reflink>] ; Howard, Carlstrom, Katz, Chew, Ray, Laine & Calum, [<reflink idref="bib23" id="ref14">23</reflink>] ). While there is evidence to suggest that young people with high aspirations are more likely to enter high-status occupations (Schoon & Parsons, [<reflink idref="bib43" id="ref15">43</reflink>] ), there is also evidence that other factors such as gender, prior achievement, educational aspiration and SES can be highly influential in the formation and actualisation of youth aspirations (Gutman & Akerman, [<reflink idref="bib18" id="ref16">18</reflink>] ; Gutman & Schoon, [<reflink idref="bib19" id="ref17">19</reflink>] ). In relation to the investigation of aspirations for STEM careers, many different research perspectives have been employed, including, for example, sociological explorations of career choice (Archer, DeWitt & Wong, [<reflink idref="bib2" id="ref18">2</reflink>] ; Beal & Crockett, [<reflink idref="bib5" id="ref19">5</reflink>] ), psychological examinations of motivation and interest (Robnett & Leaper, [<reflink idref="bib39" id="ref20">39</reflink>] ; Watt, Shapka, Morris, Durik, Keating & Eccles, [<reflink idref="bib52" id="ref21">52</reflink>] ), and educational studies focussed on curriculum, teachers and career guidance (Schmitt-Wilson & Welsh, [<reflink idref="bib42" id="ref22">42</reflink>] ). While most of these studies examine the aspirations of students in the upper years of secondary school, our study is unique in examining the aspirations of students from years 3 to 12, thus providing new insights into the development of STEM aspirations across the school years.</p> <p>A comprehensive synthesis of research focussed on aspirations for STEM careers, conducted by van Tuijl and van der Molen ([<reflink idref="bib49" id="ref23">49</reflink>] ), points to three important interrelated factors: students’ knowledge of STEM fields, the affective value placed on STEM careers and students’ ability beliefs. The authors suggest that it is important to acknowledge the role of students’, parents’ and teachers’ knowledge (or lack thereof) of STEM fields, children’s knowledge of the self in STEM activities, and parents’ and teachers’ knowledge of the early circumscription processes that can occur between the ages of 8 and 16. They suggest that more awareness of the range of STEM careers from a young age is crucial for STEM career development. Also, their overview of the literature reveals the persistent negative affective view that STEM fields “are predominately ‘things- and male-oriented’ and form a threat to more feminine lifestyles or to a good family-work balance” (p.16) leading them to reinforce the importance of male and female STEM role models for children (van Tuijl & van der Molen, [<reflink idref="bib49" id="ref24">49</reflink>] ). Lastly, their research synthesis reveals the central role of students’ ability beliefs and the prevalent view that STEM subjects are difficult and complex, requiring a great deal of effort. They suggest that students with an entity view of intelligence, rather than those with a growth mindset (Dweck, [<reflink idref="bib12" id="ref25">12</reflink>] ) may be unwilling to take on the perceived challenge of STEM subjects at school. Each of these factors (knowledge, affective value and ability beliefs) identified by van Tuijl and van der Molen ([<reflink idref="bib49" id="ref26">49</reflink>] ) are themselves multi-faceted and intersect with other influential factors identified in the literature such as gender, prior achievement, socioeconomic status and notions of social and cultural capital.</p> <hd id="AN0128462140-4">Gender and STEM</hd> <p>Gender has been a key feature of many studies examining aspirations for STEM careers (Archer et al., [<reflink idref="bib2" id="ref27">2</reflink>] ; Eccles, [<reflink idref="bib13" id="ref28">13</reflink>] ; Hernandez-Martinez, Black, Williams, Davis, Pampaka & Wake, [<reflink idref="bib21" id="ref29">21</reflink>] ; Novakovic & Fouad, [<reflink idref="bib33" id="ref30">33</reflink>] ; Packard & Nguyen, [<reflink idref="bib36" id="ref31">36</reflink>] ; Sadler, Sonnert, Hazari & Tai, [<reflink idref="bib40" id="ref32">40</reflink>] ; Shapka, Domene & Keating, [<reflink idref="bib44" id="ref33">44</reflink>] ; Watt et al., [<reflink idref="bib52" id="ref34">52</reflink>] ). In general, this body of research points to a persistent gap in the proportions of boys and girls aspiring to STEM careers, with boys more likely to do so from as early as the middle school years (Archer et al., [<reflink idref="bib2" id="ref35">2</reflink>] ; Kanny, Sax & Riggers-Piehl, [<reflink idref="bib24" id="ref36">24</reflink>] ). Indeed, there is evidence of a subsequent gender gap in those taking up STEM careers (Beede, Julian, Langdon, McKittrick, Khan & Doms, [<reflink idref="bib6" id="ref37">6</reflink>] ; Broadley, [<reflink idref="bib10" id="ref38">10</reflink>] ). The reasons for this disparity have been identified as complex and interrelated, ranging from issues related to a lack of female role models, irrelevant science curricula, lack of mathematics preparation, cultural stereotypes, field-specific ability beliefs and teacher predispositions (Blickenstaff, [<reflink idref="bib8" id="ref39">8</reflink>] ; Cheryan, Master & Meltzoff, [<reflink idref="bib11" id="ref40">11</reflink>] ; Leslie, Cimpian, Meyer & Freeland, [<reflink idref="bib28" id="ref41">28</reflink>] ).</p> <p>It may be the case that girls are not completely free to pursue STEM as they are constrained by gender stereotypes within academic cultures about who is suited to, and likely to succeed in, these disciplines (Stout, Dasgupta, Hunsinger & McManus, [<reflink idref="bib47" id="ref42">47</reflink>] ). These stereotypes can affect girls’ beliefs about their STEM competence and the value of STEM careers (Blickenstaff, [<reflink idref="bib8" id="ref43">8</reflink>] ; Broadley, [<reflink idref="bib10" id="ref44">10</reflink>] ). There is also a widespread belief that the study of STEM subjects requires in-born brilliance, a trait more often believed to be held by males (Cheryan et al., [<reflink idref="bib11" id="ref45">11</reflink>] ; Leslie et al., [<reflink idref="bib28" id="ref46">28</reflink>] ). Furthermore, females of equal ability to males often report that they perceive themselves as being less capable (MacPhee, Farro & Canetto, [<reflink idref="bib29" id="ref47">29</reflink>] ; Mann, Legewie & DiPrete, [<reflink idref="bib30" id="ref48">30</reflink>] ; Sax, Kanny, Riggers-Piehl, Whang & Paulson, [<reflink idref="bib41" id="ref49">41</reflink>] ; Shumow & Schmidt, [<reflink idref="bib45" id="ref50">45</reflink>] ). In this study, we test the notion that female students are not as interested in STEM careers as their male counterparts and examine the intersection of gender with other factors such as prior achievement.</p> <hd id="AN0128462140-5">The Importance of Prior Achievement</hd> <p>Alongside gender issues, researchers have also focussed on the critical role of prior achievement in the formation and maintenance of STEM career aspirations over time, finding that secondary school achievement levels, particularly in mathematics, can act as a critical filter for future career choices (Gemici, Bednarz, Karmel & Lim, [<reflink idref="bib15" id="ref51">15</reflink>] ; Gore, Holmes, Smith, Southgate & Albright, [<reflink idref="bib17" id="ref52">17</reflink>] ) and for STEM careers in particular (Shapka et al., [<reflink idref="bib44" id="ref53">44</reflink>] ). Drawing on data from the Longitudinal Study of Australian Youth (LSAY), Anlezark, Lim, Semo and Nguyen ([<reflink idref="bib1" id="ref54">1</reflink>] ) found that half of those from the highest mathematics achievement quartile ended up working in a STEM career, and that the students in this quartile were twice as likely to choose a STEM career as those in the lowest achievement quartile.</p> <p>Differences in prior achievement, however, do not explain the persistent gender gaps in STEM career aspirations (Riegle-Crumb, King, Grodsky & Muller, [<reflink idref="bib38" id="ref55">38</reflink>] ) with some pointing to individual persistence and motivation as being more important explanatory factors for the observed lack of female participation (Shumow & Schmidt, [<reflink idref="bib45" id="ref56">45</reflink>] ). Others have suggested that female lack of interest in STEM subjects at school is not solely related to prior achievement but rather is dependent on the value that they place on STEM tasks (Shumow & Schmidt, [<reflink idref="bib45" id="ref57">45</reflink>] ). The implication here is that changes in instructional approaches to mathematics and science, in ways that make the social benefits explicit, have some potential to increase female interest and participation in STEM.</p> <p>The performance of one’s peers has also been linked to aspirations for STEM careers with highly competitive school environments having a negative impact on these aspirations, particularly for girls (Mann et al., [<reflink idref="bib30" id="ref58">30</reflink>] ). However, this aspiration gender gap lessens for high-performing students in strong performance environments (Mann et al., [<reflink idref="bib30" id="ref59">30</reflink>] ).</p> <hd id="AN0128462140-6">Socioeconomic Status and Aspirations for STEM Careers</hd> <p>While students from low SES backgrounds are under-represented in higher education, scholars have questioned the view that this phenomenon stems from a lack of individual aspiration, recognising the complex nature of social disadvantage (Gale, Parker, Rodd, Stratton & Sealey, [<reflink idref="bib14" id="ref60">14</reflink>] ; MacPhee et al., [<reflink idref="bib29" id="ref61">29</reflink>] ; St Clair & Benjamin, [<reflink idref="bib46" id="ref62">46</reflink>] ; Zipin, Sellar, Brennan & Gale, [<reflink idref="bib54" id="ref63">54</reflink>] ). With reference to STEM aspirations, Archer, Osborne, DeWitt, Dillon, Wong and Willis ([<reflink idref="bib3" id="ref64">3</reflink>] ) found in the ASPIRES study that less than 15% of middle school students aspire to become scientists and that these preferences remain stable over time. The ASPIRES study determined that familial ‘science capital’, having family members with science qualifications or science careers, was a key factor in increasing the likelihood of students’ science aspirations. However, it should also be noted that these higher levels of ‘science capital’ tend to be associated with middle to high SES families.</p> <p>The connection between SES and prior achievement has also been researched, with acknowledgement that students’ achievement in school can mediate the impact of SES on aspirations (Gemici et al., [<reflink idref="bib15" id="ref65">15</reflink>] ). Similarly, SES has been found to intersect with gender and race, indicating that caution should be taken when considering students from low SES backgrounds as a distinct group (Kanny et al., [<reflink idref="bib24" id="ref66">24</reflink>] ). All of these factors are examined concurrently in our analysis, resulting in new understandings of their relative weight in explaining students’ aspirations for STEM careers.</p> <hd id="AN0128462140-7">Cultural Capital and Family Characteristics</hd> <p>Family cultural capital plays a role in supporting student aspirations and achievement through exposure to abstract ideas via art or culture, through provision of resources in the home to support learning and through acquaintance with knowledge about further education and careers (Khattab, [<reflink idref="bib26" id="ref67">26</reflink>] ). Family characteristics are also linked to students’ educational aspirations which, in turn, have a major influence on career trajectories. The children of parents with higher education qualifications are more likely to aspire to achieving at least the same level of education as their parents, in comparison to those students whose parents do not have post-secondary qualifications (Gil-Flores, Padilla-Carmona & Suárez-Ortega, [<reflink idref="bib16" id="ref68">16</reflink>] ). Also, there is longitudinal evidence that those who do aspire to complete university are more likely to actually do so, reinforcing the importance of aspiration as a first step toward achievement (Homel & Ryan, [<reflink idref="bib22" id="ref69">22</reflink>] ).</p> <p>Factors related to student background and the school setting, for example, gender, SES, cultural capital, indigenous status, language background, location, year level, prior achievement and students’ self-perceptions of their ability, all interact with student aspirations and with each other to some extent. This ‘fuzzy’ complexity complicates the interpretation of related research but some common ground is emerging: gender gaps in STEM education and its associated careers are consistently prevalent along with stereotypical gendered views of many STEM careers, students’ ability beliefs and prior achievement impact on their choices to study STEM subjects in school, the key influence of family factors including parents’ knowledge of or experience with STEM careers is prevalent and the pivotal role of teachers and schools as potential enablers for STEM aspirations remains a key site for intervention. While the vast majority of this empirical research into career development and occupational aspirations have been conducted with adolescents (Watson, Nota & McMahon, [<reflink idref="bib51" id="ref70">51</reflink>] ), few studies have focussed on children. Our study responds to calls to ‘focus more systematic research attention on the childhood antecedents and dimensions of occupational choice and vocational development across the life span’ (Hartung et al., [<reflink idref="bib20" id="ref71">20</reflink>] , p.412). We employ an accelerated longitudinal design to examine the development of aspirations for STEM careers from years 3 to 12, providing key insights into changes in aspirations from childhood through to adolescence, in conjunction with other key variables such as gender, prior achievement and SES.</p> <p>This study addresses the following research questions:</p> <p>Which school students express interest in STEM careers?</p> <p>What types of STEM careers are they aspiring to?</p> <p>Which student background and school factors are most predictive of student interest in a STEM career?</p> <p>Using a unique dataset, involving students from 8 to 18 years of age, we examine the formation of aspirations for STEM careers and make recommendations for STEM career education across the school years.</p> <hd id="AN0128462140-8">Method</hd> <hd id="AN0128462140-9">Data</hd> <p>The sample for this paper is drawn from data collected in a 4-year (2012-2015) longitudinal study, Educational and Career Aspirations in the Middle Years of Schooling: Understanding Complexity for Increased Equity (Gore et al., [<reflink idref="bib17" id="ref72">17</reflink>] ). The data were collected from 6492 students in 64 public high schools in New South Wales, Australia. The students in the study were in four cohorts (years 3, 5, 7, 9) depending on their school year in the first year of the study. In the final year of the study the students were in years 6, 8, 10 and 12.</p> <hd id="AN0128462140-10">Independent variables: Student background and school measures.</hd> <p>The independent variables for this study were grouped into two categories;</p> <p>Student background. This category consisted of gender, language background, Indigenous status, socioeconomic status, location, cultural capital, parent’s occupation (as described by students) and student cohort.</p> <p>School-related. This category consisted of prior achievement (reading), prior achievement (numeracy), school disadvantage, self-rated academic ability and use of out-of school tutoring.</p> <p>Students’ cohort, language background, indigenous status and gender were accessible from school enrolment records provided by the NSW Department of Education. Location was classified as metropolitan or provincial, using the Schools Geographic Location Classification Scheme reported in the MySchool website. Each school’s level of social disadvantage was measured using the Index of Community Socio-Educational Advantage (ICSEA), also available from the MySchool website, categorised into quartiles. The cross-sectional and longitudinal effects of age were measured by cohort and survey-year variables, respectively.</p> <p>An individual student measure of SES was calculated by combining information related to parents’ education and occupation. Parental level of education was represented by an ordinal variable coded 1 (year 9 or equivalent or below), 2 (year 10 or equivalent), 3 (year 11 or equivalent), 4 (year 12 or equivalent), 5 (certificate I-IV including trade certificate), 6 (advanced diploma/diploma), or 7 (bachelor degree or above). Parental occupation was represented by an ordinal variable coded 1 (not in paid work in last 12 months), 2 (machine operators, hospitality staff, assistants, labourers and related workers), 3 (tradesmen/women, clerks and skilled office, sales and service staff), 4 (other business managers, arts/media/sportspersons and associated professionals) or 5 (senior management in large business organisations, government administration and defence and qualified professionals). For each student, the highest parental education and occupation levels were combined into an equally weighted proxy representing student SES using an approach consistent with that taken by Marjoribanks ([<reflink idref="bib32" id="ref73">32</reflink>] ) and Khattab ([<reflink idref="bib26" id="ref74">26</reflink>] ). Full data for the NSW government school sector was used as a normative backdrop to separate SES scores into quartiles for our analysis. A separate dichotomous variable was created to indicate whether students reported that either parent worked in a STEM occupation or not.</p> <p>Students’ cultural capital was categorised into quartiles through the creation of a variable which included responses to a number of survey items such as: How often do you listen to classical music; talk about music; go to the theatre to see a play, dance or opera performance; go to art galleries or museums; go to the cinema to watch a movie; go to a library; talk about books; play a musical instrument or sing; participate in dancing, gymnastics or yoga; talk about art. This scale had a Cronbach’s alpha of 0.8 indicating an acceptable level of reliability. Students’ prior achievement was measured using their most recent results on the reading and numeracy components of the National Assessment Program Literacy and Numeracy (NAPLAN) divided into quartiles based on statewide data. Also, a measure of students’ comparative self-rated ability was taken from one item on the survey (How are your marks this year compared with other students?).</p> <hd id="AN0128462140-11">Dependent variable.</hd> <p>Within the surveys in the Aspirations Longitudinal Study, students were asked open-ended questions about their career aspirations for the future. Specifically they were asked ‘What would you like to do when you grow up?’ (primary students) and ‘Do you know what kind of work you would like to be doing at 25 years of age?’ (secondary students). These aspirations were coded using the Australian and New Zealand Standard Classification of Occupations (ANZSCO). A dichotomous variable was produced to indicate whether students aspired to a STEM career or not. Each survey response was counted only once in the regression models. The outcome variables is defined as ‘Interested in a STEM occupation’ determined by whether or not the survey response mentioned the following: 1—one or more STEM job(s) or 0—no STEM job. Breiner, Johnson, Harkness and Koehler ([<reflink idref="bib9" id="ref75">9</reflink>] ) noted that there is no universally accepted definition for STEM, with the National Science Foundation in the USA using a broad definition that includes discipline areas such as chemistry, computer science, engineering, life sciences, mathematical sciences, geosciences, astronomy and some social sciences. In Australia, the Chief Scientist has defined STEM more narrowly, limited to the enabling disciplines in the natural and physical sciences, which rely on ‘causal relationships, characterised by systematic observation, critical experimentation, hypothesis formation and falsification’ (Office of the Chief Scientist, [<reflink idref="bib34" id="ref76">34</reflink>] , p. 24). For this paper, aligned with this narrower view, we have categorised a STEM career as those generally requiring a university qualification in the disciplines of science, technology, engineering and/or mathematics. Table 1 displays the ANZSCO categories into which student responses were coded across the four STEM disciplines.</p> <hd id="AN0128462140-12">Logistic Regression</hd> <p>A logistic regression model was used to determine if there were differences in student background or school-related characteristics between the students who were interested in a STEM occupation and those who expressed a preference for non-STEM careers. The logistic regression model was fitted within a generalized estimating equation (GEE) framework, a method robust against violations of normality and missing data assumptions, to adjust for the correlation of outcomes within students due to repeated measures. The GEE model was compared to an equivalent random effects Generalized Linear Model employing the same data and variables, both of which produced similar estimates and p values.</p> <p>A series of univariate logistic regressions was undertaken and reported as odds ratios and associated p values. All student background variables were then included as potential predictors in a regression model for the STEM occupation outcome, reported as adjusted odds ratios and adjusted p values in model 1. A second regression model included school-related variables for educational advantage, out of school tutoring and prior achievement—both standardised and self-assessed—in addition to the individual and home-based variables, reported as adjusted odds ratios and adjusted p values in model 2.</p> <p>A variable with a significant p value for the odds ratio in model 1, which loses significance after school-level factors are introduced in model 2, suggests possible mediation of the effect of that characteristic on STEM occupation choice. In such cases the direct and indirect effects on the choice of a STEM occupation were investigated using the procedure of Baron and Kenny ([<reflink idref="bib4" id="ref77">4</reflink>] ). Data were analysed using SAS software version 9.4. Statistical significance was set at 0.05. The results from these models are presented in Table 4.</p> <hd id="AN0128462140-13">Results</hd> <p>The survey responses were collected over a 4-year period from 2012 to 2015. The sample distribution is detailed in Table 2 in relation to the student background and school-related factors under examination in this study. Of the 6492 students who completed surveys over the period of the study, 3639 completed one survey, 1893 completed two surveys, 722 completed three surveys and 238 students completed four consecutive surveys. For most variables the distribution is consistent across the four years of the study, for example, females constituted 47.8% of the sample in 2012, 49.9% in 2013, 50.9% in 2014 and 49.5% in 2015. However, for school location the sample in the first year of the study is heavily weighted to metropolitan schools (91.3%) as most provincial schools were recruited into the study in 2013. This delayed entry also impacts on the distribution of other variables in 2012 in comparison to other years. For example, the proportion of Indigenous students increased as the study progressed, along with the relative proportion of low SES students. The proportion of students with a language background other than English decreased as the study progressed.</p> <p>Survey responses by year and key variables</p> <p> <ephtml> <table frame="hsides" rules="groups"><tr><th rowspan="3">Variable</th><th>2012</th><th>2013</th><th>2014</th><th>2015</th><th>Total</th></tr><tr><th>(n = 2572)</th><th>(n = 3996)</th><th>(n = 1908)</th><th>(n = 2067)</th><th>(n = 10543)</th></tr><tr><th>n, %</th><th>n, %</th><th>n, %</th><th>n, %</th><th>n, %</th></tr><tr><td colspan="6">Gender</td></tr><tr><td> Male</td><td>1,315 (52.2)</td><td>1,845 (50.1)</td><td>932 (49.1)</td><td>995 (50.1)</td><td>5,087 (50.5)</td></tr><tr><td> Female</td><td>1,203 (47.8)</td><td>1,834 (49.9)</td><td>967 (50.9)</td><td>990 (49.9)</td><td>4,994 (49.5)</td></tr><tr><td colspan="6">School Location</td></tr><tr><td> Metro</td><td>2,347 (91.3)</td><td>1,925 (48.2)</td><td>782 (41.0)</td><td>1,016 (49.2)</td><td>6,070 (57.6)</td></tr><tr><td> Non-metro</td><td>224 (8.7)</td><td>2,071 (51.8)</td><td>1,126 (59.0)</td><td>1,051 (50.8)</td><td>4,472 (42.4)</td></tr><tr><td colspan="6">Indigenous status</td></tr><tr><td> Indigenous</td><td>2,395 (96.1)</td><td>3,373 (92.7)</td><td>1,753 (92.6)</td><td>1,809 (92.0)</td><td>9,330 (93.4)</td></tr><tr><td> Non-indigenous</td><td>98 (3.9)</td><td>265 (7.3)</td><td>141 (7.4)</td><td>157 (8.0)</td><td>661 (6.6)</td></tr><tr><td colspan="6">SES</td></tr><tr><td> Quartile 1</td><td>451 (18.6)</td><td>819 (22.7)</td><td>440 (24.0)</td><td>531 (29.7)</td><td>2,241 (23.2)</td></tr><tr><td> Quartile 2</td><td>571 (23.6)</td><td>1,007 (27.9)</td><td>576 (31.4)</td><td>507 (28.4)</td><td>2,661 (27.6)</td></tr><tr><td> Quartile 3</td><td>569 (23.5)</td><td>829 (22.9)</td><td>419 (22.8)</td><td>436 (24.4)</td><td>2,253 (23.3)</td></tr><tr><td> Quartile 4</td><td>829 (34.3)</td><td>960 (26.6)</td><td>401 (21.8)</td><td>311 (17.4)</td><td>2,501 (25.9)</td></tr><tr><td colspan="6">NAPLAN Numeracy</td></tr><tr><td> Numeracy</td><td /><td /><td /><td /><td /></tr><tr><td>  Quartile 1</td><td>408 (16.7)</td><td>705 (20.0)</td><td>363 (20.0)</td><td>378 (19.9)</td><td>1,854 (19.1)</td></tr><tr><td>  Quartile 2</td><td>562 (22.9)</td><td>890 (25.2)</td><td>540 (29.8)</td><td>530 (28.0)</td><td>2,522 (26.0)</td></tr><tr><td>  Quartile 3</td><td>638 (26.1)</td><td>943 (26.7)</td><td>574 (31.6)</td><td>575 (30.3)</td><td>2,730 (28.2)</td></tr><tr><td>  Quartile 4</td><td>841 (34.3)</td><td>995 (28.2)</td><td>337 (18.6)</td><td>412 (21.7)</td><td>2,585 (26.7)</td></tr><tr><td colspan="6">NAPLAN Literacy</td></tr><tr><td colspan="6"> Literacy</td></tr><tr><td>  Quartile 1</td><td>406 (16.5)</td><td>670 (18.8)</td><td>375 (20.4)</td><td>418 (21.8)</td><td>1,869 (19.1)</td></tr><tr><td>  Quartile 2</td><td>518 (21.1)</td><td>802 (22.6)</td><td>481 (26.2)</td><td>460 (24.0)</td><td>2,261 (23.1)</td></tr><tr><td>  Quartile 3</td><td>675 (27.4)</td><td>933 (26.2)</td><td>503 (27.4)</td><td>503 (26.2)</td><td>2,614 (26.8)</td></tr><tr><td>  Quartile 4</td><td>861 (35.0)</td><td>1,151 (32.4)</td><td>478 (26.0)</td><td>537 (28.0)</td><td>3,027 (31.0)</td></tr><tr><td colspan="6"> ICSEA National</td></tr><tr><td>  Quartile 1</td><td>502 (19.5)</td><td>1,191 (29.8)</td><td>310 (16.2)</td><td>575 (27.8)</td><td>2,578 (24.5)</td></tr><tr><td>  Quartile 2</td><td>728 (28.3)</td><td>1,297 (32.5)</td><td>1,215 (63.7)</td><td>1,039 (50.3)</td><td>4,279 (40.6)</td></tr><tr><td>  Quartile 3</td><td>187 (7.3)</td><td>494 (12.4)</td><td>203 (10.6)</td><td>199 (9.6)</td><td>1,083 (10.3)</td></tr><tr><td>  Quartile 4</td><td>1,155 (44.9)</td><td>1,014 (25.4)</td><td>180 (9.4)</td><td>254 (12.3)</td><td>2,603 (24.7)</td></tr><tr><td colspan="6"> Language</td></tr><tr><td>  English</td><td>2,050 (81.4)</td><td>3,270 (88.9)</td><td>1,806 (95.1)</td><td>1,862 (93.8)</td><td>8,988 (89.2)</td></tr><tr><td>  Other</td><td>468 (18.6)</td><td>409 (11.1)</td><td>93 (4.9)</td><td>123 (6.2)</td><td>1,093 (10.8)</td></tr><tr><td colspan="6">Cultural capital</td></tr><tr><td> Quartile 1</td><td>661 (26.7)</td><td>899 (23.5)</td><td>462 (26.3)</td><td>473 (24.5)</td><td>2,495 (25.0)</td></tr><tr><td> Quartile 2</td><td>639 (25.8)</td><td>942 (24.7)</td><td>415 (23.6)</td><td>458 (23.7)</td><td>2,454 (24.6)</td></tr><tr><td> Quartile 3</td><td>583 (23.5)</td><td>1,088 (28.5)</td><td>430 (24.5)</td><td>462 (23.9)</td><td>2,563 (25.7)</td></tr><tr><td> Quartile 4</td><td>595 (24.0)</td><td>891 (23.3)</td><td>451 (25.7)</td><td>538 (27.9)</td><td>2,475 (24.8)</td></tr><tr><td colspan="6">Self rated ability</td></tr><tr><td> Well below</td><td>369 (16.9)</td><td>475 (14.1)</td><td>209 (12.1)</td><td>204 (11.3)</td><td>1,257 (13.8)</td></tr><tr><td> Below</td><td>763 (35.0)</td><td>1,159 (34.4)</td><td>562 (32.5)</td><td>612 (33.8)</td><td>3,096 (34.1)</td></tr><tr><td> Average</td><td>867 (39.8)</td><td>1,478 (43.9)</td><td>771 (44.6)</td><td>818 (45.2)</td><td>3,934 (43.3)</td></tr><tr><td> Above</td><td>138 (6.3)</td><td>194 (5.8)</td><td>135 (7.8)</td><td>137 (7.6)</td><td>604 (6.6)</td></tr><tr><td> Well above</td><td>44 (2.0)</td><td>59 (1.8)</td><td>53 (3.1)</td><td>37 (2.0)</td><td>193 (2.1)</td></tr><tr><td colspan="6">Tutoring</td></tr><tr><td> No</td><td>564 (22.2)</td><td>602 (15.4)</td><td>266 (14.2)</td><td>284 (14.2)</td><td>1,716 (16.6)</td></tr><tr><td> Yes</td><td>1,976 (77.8)</td><td>3,301 (84.6)</td><td>1,602 (85.8)</td><td>1,717 (85.8)</td><td>8,596 (83.4)</td></tr><tr><td colspan="6">Student year at baseline</td></tr><tr><td> Year 3</td><td>657 (25.5)</td><td>960 (24.4)</td><td>552 (29.0)</td><td>757 (36.7)</td><td>2,926 (28.0)</td></tr><tr><td> Year 5</td><td>707 (27.5)</td><td>1,075 (27.4)</td><td>572 (30.0)</td><td>554 (26.9)</td><td>2,908 (27.8)</td></tr><tr><td> Year 7</td><td>694 (27.0)</td><td>1,119 (28.5)</td><td>514 (27.0)</td><td>545 (26.4)</td><td>2,872 (27.4)</td></tr><tr><td> Year 9</td><td>514 (20.0)</td><td>775 (19.7)</td><td>266 (14.0)</td><td>207 (10.0)</td><td>1,762 (16.8)</td></tr></table> </ephtml> </p> <p>The data in Table 3 demonstrate the variation in students’ aspirations for careers in each of the STEM disciplines. Of the 894 students (13.8% of student participants) who expressed interest in at least one STEM career in at least one survey year, a greater number expressed interest in science-related professions (n = 463), than in engineering (n = 322), technology (n = 180) or mathematics (n = 8), respectively. Interest in mathematics as a distinct career path is significantly lower than the other STEM categories with only eight students expressing an interest over the 4 years of the study. Engineering was more popular with older cohorts and males and comparatively more likely to be chosen by students from a non-English speaking background and/or from a metropolitan location. Students who have a parent in any STEM career were found to be more likely to be interested in a science, technology or engineering career.</p> <p>Student interest in science, technology, engineering and mathematics occupations by key variables (total number of students = 894; 13.8% of total sample)</p> <p> <ephtml> <table frame="hsides" rules="groups"><tr><th colspan="5">Surveys naming one or more job in:</th></tr><tr><th rowspan="2">Variable</th><th>Science</th><th>Technology</th><th>Engineering</th><th>Mathematics</th></tr><tr><th>(n = 553) n, %</th><th>(n = 207) n, %</th><th>(n = 396) n, %</th><th>(n = 8) n, %</th></tr><tr><td colspan="5">Indigenous status</td></tr><tr><td> Non-indigenous</td><td>498 (95.4)</td><td>190 (99.0)</td><td>375 (98.9)</td><td>6 (100.0)</td></tr><tr><td> Indigenous</td><td>24 (4.6)</td><td>2 (1.0)</td><td>4 (1.1)</td><td /></tr><tr><td colspan="5">Student year at baseline</td></tr><tr><td> Year 3 cohort</td><td>151 (27.6)</td><td>37 (17.9)</td><td>90 (22.8)</td><td>2 (25.0)</td></tr><tr><td> Year 5 cohort</td><td>173 (31.6)</td><td>49 (23.7)</td><td>110 (27.8)</td><td /></tr><tr><td> Year 7 cohort</td><td>139 (25.4)</td><td>83 (40.1)</td><td>108 (27.3)</td><td>3 (37.5)</td></tr><tr><td> Year 9 cohort</td><td>84 (15.4)</td><td>38 (18.4)</td><td>87 (22.0)</td><td>3 (37.5)</td></tr><tr><td colspan="5">Cultural capital</td></tr><tr><td> Quartile 1</td><td>78 (14.6)</td><td>54 (27.1)</td><td>113 (29.8)</td><td /></tr><tr><td> Quartile 2</td><td>111 (20.8)</td><td>58 (29.1)</td><td>88 (23.2)</td><td>3 (42.9)</td></tr><tr><td> Quartile 3</td><td>166 (31.1)</td><td>45 (22.6)</td><td>109 (28.8)</td><td>3 (42.9)</td></tr><tr><td> Quartile 4</td><td>178 (33.4)</td><td>42 (21.1)</td><td>69 (18.2)</td><td>1 (14.3)</td></tr><tr><td colspan="5">ICSEA national quartile</td></tr><tr><td> Quartile 1</td><td>119 (21.5)</td><td>43 (20.8)</td><td>80 (20.2)</td><td /></tr><tr><td> Quartile 2</td><td>198 (35.8)</td><td>69 (33.3)</td><td>144 (36.4)</td><td>5 (62.5)</td></tr><tr><td> Quartile 3</td><td>60 (10.8)</td><td>18 (8.7)</td><td>29 (7.3)</td><td>1 (12.5)</td></tr><tr><td> Quartile 4</td><td>176 (31.8)</td><td>77 (37.2)</td><td>143 (36.1)</td><td>2 (25.0)</td></tr><tr><td colspan="5">School location</td></tr><tr><td> Metro</td><td>339 (61.3)</td><td>149 (72.0)</td><td>257 (64.9)</td><td>5 (62.5)</td></tr><tr><td> Non-metro</td><td>214 (38.7)</td><td>58 (28.0)</td><td>139 (35.1)</td><td>3 (37.5)</td></tr><tr><td colspan="5">NAPLAN literacy</td></tr><tr><td> Quartile 1</td><td>39 (7.5)</td><td>14 (7.5)</td><td>25 (6.8)</td><td>1 (16.7)</td></tr><tr><td> Quartile 2</td><td>87 (16.7)</td><td>30 (16.0)</td><td>62 (16.8)</td><td /></tr><tr><td> Quartile 3</td><td>135 (26.0)</td><td>59 (31.6)</td><td>94 (25.5)</td><td>1 (16.7)</td></tr><tr><td> Quartile 4</td><td>259 (49.8)</td><td>84 (44.9)</td><td>188 (50.9)</td><td>4 (66.7)</td></tr><tr><td colspan="5">NAPLAN numeracy</td></tr><tr><td> Quartile 1</td><td>38 (7.4)</td><td>9 (4.9)</td><td>11 (3.0)</td><td /></tr><tr><td> Quartile 2</td><td>86 (16.7)</td><td>37 (20.0)</td><td>52 (14.1)</td><td>1 (16.7)</td></tr><tr><td> Quartile 3</td><td>175 (34.0)</td><td>53 (28.6)</td><td>108 (29.3)</td><td /></tr><tr><td> Quartile 4</td><td>215 (41.8)</td><td>86 (46.5)</td><td>198 (53.7)</td><td>5 (83.3)</td></tr><tr><td colspan="5">Self-rated ability</td></tr><tr><td> Well below</td><td>3 (0.6)</td><td>2 (1.1)</td><td>6 (1.6)</td><td /></tr><tr><td> Below</td><td>7 (1.4)</td><td>14 (7.5)</td><td>12 (3.2)</td><td /></tr><tr><td> Average</td><td>144 (28.6)</td><td>70 (37.4)</td><td>92 (24.6)</td><td>2 (25.0)</td></tr><tr><td> Above</td><td>238 (47.2)</td><td>68 (36.4)</td><td>180 (48.1)</td><td>2 (25.0)</td></tr><tr><td> Well above</td><td>112 (22.2)</td><td>33 (17.6)</td><td>84 (22.5)</td><td>4 (50.0)</td></tr><tr><td colspan="5">SES</td></tr><tr><td> Quartile 1</td><td>84 (16.1)</td><td>33 (17.7)</td><td>65 (17.3)</td><td>1 (16.7)</td></tr><tr><td> Quartile 2</td><td>125 (23.9)</td><td>47 (25.3)</td><td>79 (21.1)</td><td>2 (33.3)</td></tr><tr><td> Quartile 3</td><td>115 (22.0)</td><td>41 (22.0)</td><td>105 (28.0)</td><td>2 (33.3)</td></tr><tr><td> Quartile 4</td><td>198 (37.9)</td><td>65 (34.9)</td><td>126 (33.6)</td><td>1 (16.7)</td></tr><tr><td colspan="5">Gender</td></tr><tr><td> Female</td><td>277 (52.9)</td><td>182 (94.3)</td><td>347 (91.3)</td><td>4 (66.7)</td></tr><tr><td> Male</td><td>247 (47.1)</td><td>11 (5.7)</td><td>33 (8.7)</td><td>2 (33.3)</td></tr><tr><td colspan="5">Parent in STEM</td></tr><tr><td> No</td><td>523 (94.6)</td><td>202 (97.6)</td><td>382 (96.5)</td><td>8 (100.0)</td></tr><tr><td> Yes</td><td>30 (5.4)</td><td>5 (2.4)</td><td>14 (3.5)</td><td /></tr><tr><td colspan="5">Tutoring</td></tr><tr><td> No</td><td>459 (83.6)</td><td>173 (84.0)</td><td>322 (81.7)</td><td>6 (75.0)</td></tr><tr><td> Yes</td><td>90 (16.4)</td><td>33 (16.0)</td><td>72 (18.3)</td><td>2 (25.0)</td></tr><tr><td colspan="5">Survey year</td></tr><tr><td> 2012</td><td>145 (26.2)</td><td>44 (21.3)</td><td>109 (27.5)</td><td>1 (12.5)</td></tr><tr><td> 2013</td><td>201 (36.3)</td><td>100 (48.3)</td><td>167 (42.2)</td><td>2 (25.0)</td></tr><tr><td> 2014</td><td>113 (20.4)</td><td>25 (12.1)</td><td>75 (18.9)</td><td>2 (25.0)</td></tr><tr><td> 2015</td><td>94 (17.0)</td><td>38 (18.4)</td><td>45 (11.4)</td><td>3 (37.5)</td></tr></table> </ephtml> </p> <p>Table 4 displays the results of the logistic regression analysis investigating the influence of student background and school-related variables on students’ aspiration for a STEM career. Model 1 includes only the student background variables and model 2 includes both student background and school-related variables. For the student background variables, the univariate analysis indicates that most are significantly related to the choice of a STEM career, specifically Indigenous status, student cohort, cultural capital, language background, location, parent in a STEM occupation, SES, gender and survey year. When considered in a logistic regression model (model 1), school location is no longer a significant predictor of the choice of a STEM career. When variables related to schooling (school ICSEA, reading achievement, numeracy achievement, comparative self-rated ability, access to tutoring) are included (model 2), the following variables are no longer predictive of the choice of a STEM career: Indigenous status, language background, SES, survey year. In model 2, considering both student background and school-related variables, the variables that are significant in predicting whether students express aspiration for a STEM career are student cohort, cultural capital, gender, having a parent in a STEM occupation and prior achievement in reading and numeracy. It appears that achievement at school in both reading and numeracy, while related to other non-significant variables such as SES and indigenous status, is a better predictor of interest in a STEM career than these demographic variables.</p> <p>Logistic modelling results for choice of a STEM career</p> <p> <ephtml> <table frame="hsides" rules="groups"><tr><th rowspan="2">Variable</th><th colspan="2">STEM career choice</th><th colspan="2">Univariate models</th><th colspan="2">Model 1</th><th colspan="2">Model 2</th></tr><tr><th>No</th><th>Yes</th><th>p value</th><th>Unadjusted odds ratio [95% CI]</th><th>Adjusted p value</th><th>Adjusted odds ratio [95% CI]</th><th>Adjusted p value</th><th>Adjusted odds ratio [95 CI]</th></tr><tr><td colspan="9">Indigenous status</td></tr><tr><td> Indigenous</td><td>631 (95)</td><td>30 (5)</td><td /><td /><td /><td /><td /><td /></tr><tr><td> Non-indigenous</td><td>8,318 (89)</td><td>1,012 (11)</td><td><.001</td><td>2.62 [1.7, 4.02]</td><td>.000</td><td>2.21 [1.37, 3.57]</td><td>.190</td><td>1.41 [0.84, 2.35]</td></tr><tr><td colspan="9">Student year</td></tr><tr><td> Year 3</td><td>2,658 (91)</td><td>268 (9)</td><td /><td /><td /><td /><td /><td /></tr><tr><td> Year 5</td><td>2,596 (89)</td><td>312 (11)</td><td>.029</td><td>1.25 [1.02, 1.52]</td><td><.001</td><td>1.45 [1.17, 1.79]</td><td>.013</td><td>1.35 [1.07, 1.71]</td></tr><tr><td> Year 7</td><td>2,554 (89)</td><td>318 (11)</td><td>.010</td><td>1.30 [1.06, 1.59]</td><td><.001</td><td>1.65 [1.33, 2.06]</td><td><.001</td><td>1.58 [1.22, 2.04]</td></tr><tr><td> Year 9</td><td>1,561 (89)</td><td>201 (11)</td><td>.038</td><td>1.27 [1.01, 1.59]</td><td><.001</td><td>1.63 [1.26, 2.10]</td><td>.004</td><td>1.54 [1.15, 2.06]</td></tr><tr><td colspan="9">Cultural capital</td></tr><tr><td> Quartile 1</td><td>2,256 (90)</td><td>239 (10)</td><td /><td /><td /><td /><td /><td /></tr><tr><td> Quartile 2</td><td>2,208 (90)</td><td>246 (10)</td><td>.650</td><td>1.05 [0.86, 1.27]</td><td>.311</td><td>1.11 [0.91, 1.36]</td><td>.964</td><td>1.01 [0.81, 1.25]</td></tr><tr><td> Quartile 3</td><td>2,264 (88)</td><td>299 (12)</td><td>.024</td><td>1.23 [1.03, 1.48]</td><td><.001</td><td>1.47 [1.20, 1.79]</td><td>.035</td><td>1.27 [1.02, 1.58]</td></tr><tr><td> Quartile 4</td><td>2,198 (89)</td><td>277 (11)</td><td>.040</td><td>1.23 [1.01, 1.48]</td><td><.001</td><td>1.69 [1.37, 2.09]</td><td>.001</td><td>1.47 [1.16, 1.86]</td></tr><tr><td colspan="9">Language</td></tr><tr><td> English</td><td>8,095 (90)</td><td>893 (10)</td><td /><td /><td /><td /><td /><td /></tr><tr><td> Other</td><td>940 (86)</td><td>153 (14)</td><td><.001</td><td>1.59 [1.29, 1.97]</td><td>.037</td><td>1.28 [1.01, 1.61]</td><td>.148</td><td>1.22 [0.93, 1.59]</td></tr><tr><td colspan="9">School</td></tr><tr><td> Metro</td><td>5,369 (88)</td><td>701 (12)</td><td /><td /><td /><td /><td /><td /></tr><tr><td> Non-metro</td><td>4,067 (91)</td><td>405 (9)</td><td><.001</td><td>0.76 [0.66, 0.88]</td><td>.355</td><td>0.92 [0.76, 1.10]</td><td>.891</td><td>1.01 [0.82, 1.25]</td></tr><tr><td colspan="9">STEM parent</td></tr><tr><td> No</td><td>9,139 (90)</td><td>1,026 (10)</td><td /><td /><td /><td /><td /><td /></tr><tr><td> Yes</td><td>298 (79)</td><td>80 (21)</td><td><.001</td><td>1.93 [1.43, 2.60]</td><td>.000</td><td>1.65 [1.21, 2.25]</td><td>.040</td><td>1.42 [1.02, 1.98]</td></tr><tr><td colspan="9">SES</td></tr><tr><td> Quartile 1</td><td>2,063 (92)</td><td>178 (8)</td><td /><td /><td /><td /><td /><td /></tr><tr><td> Quartile 2</td><td>2,417 (91)</td><td>244 (9)</td><td>.050</td><td>1.24 [1.00, 1.55]</td><td>.370</td><td>1.12 [0.88, 1.42]</td><td>.530</td><td>0.92 [0.71, 1.20]</td></tr><tr><td> Quartile 3</td><td>1,997 (89)</td><td>256 (11)</td><td><.001</td><td>1.57 [1.25, 1.96]</td><td>.000</td><td>1.43 [1.13, 1.83]</td><td>0.716</td><td>1.05 [0.80, 1.37]</td></tr><tr><td> Quartile 4</td><td>2,143 (86)</td><td>358 (14)</td><td><.001</td><td>2.07 [1.67, 2.56]</td><td><.001</td><td>1.82 [1.44, 2.31]</td><td>.280</td><td>1.17 [0.88, 1.55]</td></tr><tr><td colspan="9">Gender</td></tr><tr><td> Female</td><td>4,707 (94)</td><td>287 (6)</td><td /><td /><td /><td /><td /><td /></tr><tr><td> Male</td><td>4,328 (85)</td><td>759 (15)</td><td><.001</td><td>2.77 [2.36, 3.26]</td><td><.001</td><td>3.29 [2.77, 3.91]</td><td><.001</td><td>3.18 [2.63, 3.83]</td></tr><tr><td colspan="9">Survey year</td></tr><tr><td> 2012</td><td>2,292 (89)</td><td>280 (11)</td><td /><td /><td /><td /><td /><td /></tr><tr><td> 2013</td><td>3,546 (89)</td><td>450 (11)</td><td>.330</td><td>1.08 [0.93, 1.25]</td><td>.090</td><td>1.15 [0.98, 1.36]</td><td>.293</td><td>1.10 [0.92, 1.32]</td></tr><tr><td> 2014</td><td>1,701 (89)</td><td>207 (11)</td><td>.590</td><td>1.05 [0.88, 1.26]</td><td>.040</td><td>1.24 [1.01, 1.53]</td><td>.077</td><td>1.24 [0.98, 1.57]</td></tr><tr><td> 2015</td><td>1,898 (92)</td><td>169 (8)</td><td>.010</td><td>0.78 [0.64, 0.95]</td><td>.680</td><td>0.95 [0.76, 1.19]</td><td>.394</td><td>0.90 [0.70, 1.15]</td></tr><tr><td colspan="9">ICSEA National</td></tr><tr><td> Quartile 1</td><td>2,345 (91)</td><td>233 (9)</td><td /><td /><td /><td /><td /><td /></tr><tr><td> Quartile 2</td><td>3,879 (91)</td><td>400 (9)</td><td>.360</td><td>1.10 [0.90,10.33]</td><td /><td /><td>.178</td><td>0.85 [0.67, 1.08]</td></tr><tr><td> Quartile 3</td><td>978 (90)</td><td>105 (10)</td><td>.190</td><td>10.20 [0.91,1.57]</td><td /><td /><td>.132</td><td>0.76 [0.53, 1.09]</td></tr><tr><td> Quartile 4</td><td>2,235 (86)</td><td>368 (14)</td><td><.001</td><td>1.81 [1.49, 2.21]</td><td /><td /><td>.069</td><td>0.75 [0.54, 1.02]</td></tr><tr><td colspan="9">NAPLAN literacy</td></tr><tr><td> Quartile 1</td><td>1,794 (96)</td><td>75 (4)</td><td /><td /><td /><td /><td /><td /></tr><tr><td> Quartile 2</td><td>2,088 (92)</td><td>173 (8)</td><td><.001</td><td>2.00 [1.45, 2.76]</td><td /><td /><td>.012</td><td>1.66 [1.12, 2.46]</td></tr><tr><td> Quartile 3</td><td>2,332 (89)</td><td>282 (11)</td><td><.001</td><td>2.80 [2.08, 3.75]</td><td /><td /><td>.005</td><td>1.74 [1.18, 2.57]</td></tr><tr><td> Quartile 4</td><td>2,531 (84)</td><td>496 (16)</td><td><.001</td><td>4.49 [3.38, 5.96]</td><td /><td /><td><.001</td><td>2.13 [1.42, 3.19]</td></tr><tr><td colspan="9">NAPLAN numeracy</td></tr><tr><td> Quartile 1</td><td>1,796 (97)</td><td>58 (3)</td><td /><td /><td /><td /><td /><td /></tr><tr><td> Quartile 2</td><td>2,351 (94)</td><td>171 (7)</td><td><.001</td><td>2.10 [1.56, 2.83]</td><td /><td /><td>.034</td><td>1.52 [1.03, 2.25]</td></tr><tr><td> Quartile 3</td><td>2,404 (88)</td><td>326 (12)</td><td><.001</td><td>3.69 [2.77, 4.91]</td><td /><td /><td><.001</td><td>2.02 [1.37, 3.00]</td></tr><tr><td> Quartile 4</td><td>2,122 (82)</td><td>463 (18)</td><td><.001</td><td>6.10 [4.61, 8.06]</td><td /><td /><td><.001</td><td>2.71 [1.78, 4.13]</td></tr><tr><td colspan="9">Self-rated ability</td></tr><tr><td> Well below</td><td>182 (94)</td><td>11 (6)</td><td /><td /><td /><td /><td /><td /></tr><tr><td> Below</td><td>572 (95)</td><td>32 (5)</td><td>.740</td><td>0.90 [0.47, 1.70]</td><td /><td /><td>.856</td><td>0.93 [0.41, 2.08]</td></tr><tr><td> Average</td><td>3,634 (92)</td><td>300 (8)</td><td>.280</td><td>1.36 [0.78, 2.37]</td><td /><td /><td>.567</td><td>1.23 [0.61, 2.47]</td></tr><tr><td> Above</td><td>2,629 (85)</td><td>467 (15)</td><td><.001</td><td>2.66 [1.53, 4.62]</td><td /><td /><td>.094</td><td>1.82 [0.90, 3.65]</td></tr><tr><td> Well above</td><td>1,048 (83)</td><td>209 (17)</td><td><.001</td><td>2.73 [1.56, 4.78]</td><td /><td /><td>.144</td><td>1.69 [0.08, 3.41]</td></tr><tr><td colspan="9">Tutoring</td></tr><tr><td> No</td><td>7,682 (89)</td><td>914 (11)</td><td /><td /><td /><td /><td /><td /></tr><tr><td> Yes</td><td>1,530 (89)</td><td>186 (11)</td><td>.980</td><td>1.00 [0.84, 1.18]</td><td /><td /><td>.864</td><td>1.02 [0.08, 1.26]</td></tr></table> </ephtml> </p> <p>Male students were more likely to aspire to a STEM career than females (OR = 3.18) and students in the older cohorts were also more likely (OR = 1.35 for the year 5-8 cohort; OR = 1.58 for the year 7-10 cohort; OR = 1.54 for the year 9-12 cohort) in comparison to students in the year 3-6 cohort. With respect to cultural capital, students in the highest two quartiles were more likely to aspire to a STEM career than those in the lowest quartile (OR = 1.27 Q3; OR = 1.47 Q4). Students’ prior achievement in reading and numeracy, as measured by NAPLAN, was also a significant variable. In relation to reading achievement students in quartiles other than the lowest quartile were increasingly likely to aspire to a STEM career (OR = 2.13 Q4; OR = 1.74 Q3; OR = 1.66 Q2). Achievement in numeracy was also related to the choice of a STEM career with those in the top numeracy quartile more likely to express interest in these careers than those in the lowest quartile (OR = 2.71). Those in the second quartile for numeracy were also more likely to aspire to a STEM career (OR = 2.02), as were those in the third quartile (OR = 1.52) than those in the bottom quartile for numeracy.</p> <p>The addition of school-related variables in model 2 mediates the effects of some of the student background factors which were found to be significant in model 1. While we found that students with an Indigenous background were significantly less likely to express interest in a STEM career than their non-indigenous peers (5% Indigenous; 11% non-Indigenous), and that the proportion of students choosing STEM significantly increased across the SES quartiles (quartile 1, 8%; quartile 2, 9%; quartile 3, 11%; quartile 4, 14%), both of these factors were not significant in the presence of school-related factors measuring students’ prior achievement in reading and numeracy. In other words, Indigenous and low SES students with relatively high levels of prior achievement are just as likely to aspire to a STEM career as their non-Indigenous or high SES peers. In contrast, prior achievement does not mediate the effect of gender, with males significantly more likely to aspire to a STEM career than females even when these other factors are taken into account.</p> <p>Two other factors related to student background were found to be significant predictors of the aspiration for a STEM career: exposure to a parent in a STEM career (OR = 1.42) and being in the higher two quartiles for cultural capital (OR = 1.47 Q4; OR = 1.27 Q3). Other factors related to student background were found not to be significant in explaining aspiration for a STEM career in the presence of the variables in model 2 representing school-related factors.</p> <hd id="AN0128462140-14">Discussion</hd> <p>This paper has provided a detailed examination of primary and secondary school student aspirations for STEM careers. In general, more students are interested in science careers, followed by engineering, technology and mathematics careers, respectively. We have examined a combination of student background and school-related factors to determine those which are significant predictors of school students’ likelihood of expressing interest in a post-school STEM career. In terms of student background, the analysis revealed that students were more likely to aspire to a STEM career as they get older, more so for males than females. Cultural capital was found to be a salient factor related to students’ home life, as was their exposure to a parent working in a STEM career. In relation to the school-related factors tested, students’ prior achievement in both reading and numeracy were found to be significant predictors of aspiration for a STEM career. Other factors, seemingly related to the choice of STEM when considered independently, for example, SES, indigenous status, language background and students’ self-rated ability, were found not to be significant in explaining aspirations for STEM in the presence of the other variables.</p> <p>At the individual level, consideration of a STEM career remains a highly gendered concern but the reasons remain unclear. Students’ choices in this study aligned with the stereotypical notion that STEM careers are largely ‘male oriented’ and are therefore, not desirable for females, particularly in the fields of technology and engineering. Previous research has concluded that females’ lack of aspiration in STEM may be linked to poor academic self-concept even when their achievement levels are comparable to males (Sax et al., [<reflink idref="bib41" id="ref78">41</reflink>] ). This study finds that actual academic achievement rather than students’ perception of their achievement is more predictive of aspirations for a STEM career. However, it could also be that academic self-concept is a precursor to academic achievement (MacPhee et al., [<reflink idref="bib29" id="ref79">29</reflink>] ) and so its importance as a factor for indirectly enabling aspirations for STEM careers remains. The findings highlight the need for teachers to be aware of the importance of maintaining student interest and achievement levels in mathematics in the earlier years of schooling so that students are able to sustain their belief that a STEM career is achievable.</p> <p>Another gender difference in STEM disciplines is the relationship between student interest and skill. It has previously been found to be bi-directional for males but uni-directional for females (Lee, Lawson & McHale, [<reflink idref="bib27" id="ref80">27</reflink>] ). In other words, male interest in STEM can lead to increased achievement and conversely high achievement can increase males’ interest in STEM careers. However, for females, while achievement in STEM disciplines can lead to increased interest, the reverse does not generally occur. Given that interest in STEM in early high school is a key predictor of interest at the end of formal schooling (Sadler et al., [<reflink idref="bib40" id="ref81">40</reflink>] ), this finding reinforces the importance for females of supporting the development of sound STEM skills from an early age. Whereas males seem to maintain interest in STEM without necessarily having strong underlying skills, females are less likely to do so (Lee et al., [<reflink idref="bib27" id="ref82">27</reflink>] ). Maintaining and supporting females’ academic achievement in STEM disciplines is vital for sustaining their interest as they progress through school. However, there is evidence that female students’ achievement in STEM can be undermined through teachers’ unconscious bias towards males in the classroom (Blickenstaff, [<reflink idref="bib8" id="ref83">8</reflink>] ) leading them to spend more time interacting with male students in comparison to females (Shumow & Schmidt, [<reflink idref="bib45" id="ref84">45</reflink>] ). It is important that teachers are aware of their potential unconscious bias in STEM classrooms which could impact negatively on female students’ achievement and aspirations.</p> <p>Students cannot aspire to particular careers, including those in the STEM fields, if they lack knowledge of those careers (van Tuijl & van der Molen, [<reflink idref="bib49" id="ref85">49</reflink>] ). This knowledge can be gained through careers education, generally in secondary school, or through exposure to adult family members or acquaintances outside of school. Our study finds that students are more likely to aspire to a STEM career if one or more of their parents/carers work in a STEM occupation, thereby giving students detailed knowledge of careers that they might otherwise not gain. Students without access to parents working in STEM fields are less likely to aspire to a STEM career, even if they have comparable levels of prior achievement. This finding reveals a significant gap in students’ knowledge about STEM careers, possibly leading some students to compromise their aspirations due to a lack of knowledge of the breadth of possibilities. The implication for careers education in STEM is that it is currently not filling this gap, leaving some students who may potentially have the ‘skill and will’ to pursue STEM without the necessary familiarity with these careers.</p> <p>The influence that a parent’s STEM occupation can have on students’ aspirations indicates the importance of role models for career development. Several studies point to the lack of female STEM role models as a reason for the persistent lack of female interest in STEM (Beede et al., [<reflink idref="bib6" id="ref86">6</reflink>] ; Broadley, [<reflink idref="bib10" id="ref87">10</reflink>] ; Wang & Degol, [<reflink idref="bib50" id="ref88">50</reflink>] ). However, the impact of role models, particularly for female students, is contentious. While the stereotypical view of women in STEM is that they are unfeminine, which may be a disincentive for some females considering a STEM career, the use of overtly feminine STEM role models has also been found to have a negative impact on the STEM aspirations of school-aged girls (Betz & Sekaquaptewa, [<reflink idref="bib7" id="ref89">7</reflink>] ). This counterintuitive finding could indicate that the use of role models to entice females into STEM is a somewhat shallow approach, particularly if stereotypical views of the STEM disciplines remain ‘male’ and ‘thing’ oriented (van Tuijl & van der Molen, [<reflink idref="bib49" id="ref90">49</reflink>] ). Approaches that reveal non-gendered but multi-faceted views of STEM careers, challenging the dominant stereotypes, might be more successful in attracting females.</p> <hd id="AN0128462140-15">Conclusion</hd> <p>The need to increase numbers of suitably qualified STEM professionals has prompted interest in the school sector and its role in developing the future STEM workforce. Our findings reveal a dire lack of interest in, or possible lack of knowledge of, careers that focussed on mathematics, with only eight out of 6492 students expressing such interest during the four years of the study. While this is concerning, the role of mathematics as a foundational component necessary for any STEM career is important. The way students perceive themselves as students learning mathematics can have a vital impact on aspirations for related STEM careers, particularly for females. To increase aspirations for STEM careers, schools need to plan instruction that increases students’ self-concept and achievement in mathematics in particular.</p> <p>This study has revealed a series of interrelated key factors associated with school students’ aspirations for STEM careers. These are gender, cultural capital, age, parent STEM occupation and prior achievement in reading and numeracy. Schools clearly have a role to play in improving the achievement levels of all students in STEM disciplines. However, for females the importance of school achievement appears to be even more crucial than it is for males because it can drive interest in STEM. However, females with achievement comparable to males are considerably less likely to aspire to STEM careers, indicating that prior achievement is only one part of the puzzle in determining the relative lack of female interest in STEM.</p> <p>The likelihood of student interest in STEM increases for those students who have a parent working in a STEM occupation, emphasising that insider knowledge of STEM careers can lead to increased awareness and interest. Providing more students with this kind of knowledge is a potentially fruitful approach for careers education in schools. However, in doing so, care must be taken to avoid the reinforcement of STEM gender stereotypes which are proving very resistant to change. Future research using the qualitative data from the Aspirations Longitudinal Study will provide further insights into the ways in which students think about STEM careers and their motivations for aspiring to them, shedding light on the seemingly intransigent patterns of participation reported in this paper.</p> <p>This paper is derived from a 2012-2015 Australian Research Council Linkage grant (LP120100013) in partnership with the New South Wales Department of Education. Further funding was obtained from the Australian Government Higher Education Participation and Partnerships Programme.</p> <hd id="AN0128462140-16">References</hd> <hd id="AN0128462140-17">Citations</hd> <p>1 Anlezark, A., Lim, P., Semo, R., & Nguyen, N. (2008)., From STEM to leaf: Where are Australia’s science, mathematics, engineering and technology (STEM) students heading. (NCVER Final Report). Retrieved from, https://docs.education.gov.au/system/files/doc/other/stemtoleafaustscitechenginmathheading.pdf.</p> <ulist> <item>2 Archer L, DeWitt J, Wong B, Spheres of influence: What shapes young people’s aspirations at age 12/13 and what are the implications for education policy?, Journal of Education Policy, 2014, 29, 1, 58, 85, 10.1080/02680939.2013.790079</item> <item>3 Archer L, Osborne J, DeWitt J, Dillon J, Wong B, Willis B, ASPIRES: Young people’s science and career aspirations, age 10-14, 2013, London, England, King’s College</item> <item>4 Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations., Journal of personality and social psychology, 51(<reflink idref="bib6" id="ref91">6</reflink>), 1173-1182.</item> <item>5 Beal SJ, Crockett LJ, Adolescents’ occupational and educational goals: A test of reciprocal relations, Journal of Applied Developmental Psychology, 2013, 34, 5, 219, 229, 10.1016/j.appdev.2013.04.005</item> <item>6 Beede DN, Julian TA, Langdon D, McKittrick G, Khan B, Doms ME, Women in STEM: A gender gap to innovation, 2011</item> <item>7 Betz DE, Sekaquaptewa D, My fair physicist? Feminine math and science role models demotivate young girls, Social Psychological and Personality Science, 2012, 3, 6, 738, 746, 10.1177/1948550612440735</item> <item>8 Blickenstaff JC, Women and science careers: Leaky pipeline or gender filter?, Gender and Education, 2005, 17, 4, 369, 386, 10.1080/09540250500145072</item> <item>9 Breiner JM, Johnson CC, Harkness SS, Koehler CM, What is STEM? A discussion about conceptions of STEM in education and partnerships, School Science and Mathematics, 2012, 112, 1, 3, 11, 10.1111/j.1949-8594.2011.00109.x</item> <item>10 Broadley K, Entrenched gendered pathways in science, technology, engineering and mathematics: Engaging girls through collaborative career development, Australian Journal of Career Development, 2015, 24, 1, 27, 38, 10.1177/1038416214559548</item> <item>11 Cheryan S, Master A, Meltzoff AN, Cultural stereotypes as gatekeepers: increasing girls’ interest in computer science and engineering by diversifying stereotypes, Frontiers in Psychology, 2015, 6, 49, 10.3389/fpsyg.2015.00049</item> <item>12 Dweck C, Mindset: The new psychology of success, 2006, New York, NY, Ballantine Books</item> <item>13 Eccles JS, Understanding women’s educational and occupational choices, Psychology of Women Quarterly, 1994, 18, 4, 585, 609, 10.1111/j.1471-6402.1994.tb01049.x</item> <item>14 Gale, T., Parker, S., Rodd, P., Stratton, G., & Sealey, T. (2013)., Student aspirations for higher education in Central Queensland. Centre for Research in Educational Futures and Innovation (CREFI). Retrieved from, https://www.deakin.edu.au/__data/assets/pdf_file/0016/365200/student-aspirations-qld.pdf.</item> <item>15 Gemici, S., Bednarz, A., Karmel, T. & Lim, P. (2014)., The factors affecting the educational and occupational aspirations of young Australians (Research Report). Adelaide, Australia: NCVER. Retrieved from, http://www.ncver.edu.au/.</item> <item>16 Gil-Flores J, Padilla-Carmona MT, Suárez-Ortega M, Influence of gender, educational attainment and family environment on the educational aspirations of secondary school students, Educational Review, 2011, 63, 3, 345, 363, 10.1080/00131911.2011.571763</item> <item>17 Gore, J., Holmes, K., Smith, M., Southgate, E., Albright, J. (2015). Socioeconomic status and the career aspirations of Australian school students: Testing enduring assumptions., The Australian Educational Researcher, 42(<reflink idref="bib2" id="ref92">2</reflink>), 155-177.</item> <item>18 Gutman, L. & Akerman, R. (2008)., Determinants of aspirations [wider benefits of learning research report no. 27]. London, United Kingdom: Centre for Research on the Wider Benefits of Learning.</item> <item>19 Gutman L, Schoon I, Correlates and consequences of uncertainty in career aspirations: Gender differences among adolescents in England, Journal of Vocational Behavior, 2012, 80, 3, 608, 618, 10.1016/j.jvb.2012.02.002</item> <item>20 Hartung PJ, Porfeli EJ, Vondracek FW, Child vocational development: A review and reconsideration, Journal of Vocational Behavior, 2005, 66, 385, 419, 10.1016/j.jvb.2004.05.006</item> <item>21 Hernandez Martinez, P., Black, L., Williams, J., Davis, P., Pampaka, M., & Wake, G. (2008). Mathematics students’ aspirations for higher education: class, ethnicity, gender and interpretative repertoire styles., Research Papers in Education, 23(<reflink idref="bib2" id="ref93">2</reflink>), 153-165.</item> <item>22 Homel, J. & Ryan, C. (2014)., Educational outcomes: the impact of aspirations and the role of student background characteristics (Report). Adelaide, Australia: NCVER. Retrieved from, http://www.ncver.edu.au/.</item> <item>23 Howard KAS, Carlstrom AH, Katz AD, Chew AY, Ray GC, Laine L, Calum D, Career aspirations of youth: Untangling race/ethnicity, SES and gender, Journal of Vocational Behavior, 2011, 79, 98, 109, 10.1016/j.jvb.2010.12.002</item> <item>24 Kanny MA, Sax LJ, Riggers-Piehl TA, Investigating forty years of STEM research: How explanations for the gender gap have evolved over time, Journal of Women and Minorities in Science and Engineering, 2014, 20, 2, 127, 148, 10.1615/JWomenMinorScienEng.2014007246</item> <item>25 Kennedy J, Lyons T, Quinn F, The continuing decline of science and mathematics enrolments in Australian high schools, Teaching Science, 2014, 60, 2, 34, 46</item> <item>26 Khattab N, Students’ aspirations, expectations and school achievement: What really matters?, British Educational Research Journal, 2015, 41, 5, 731, 748, 10.1002/berj.3171</item> <item>27 Lee B, Lawson KM, McHale SM, Longitudinal associations between gender-typed skills and interests and their links to occupational outcomes, Journal of Vocational Behavior, 2015, 88, 121, 130, 10.1016/j.jvb.2015.02.011</item> <item>28 Leslie S-J, Cimpian A, Meyer M, Freeland E, Expectations of brilliance underlie gender distributions across academic disciplines, Science, 2015, 347, 6219, 262, 265, 10.1126/science.1261375</item> <item>29 MacPhee D, Farro S, Canetto SS, Academic self‐efficacy and performance of underrepresented STEM majors: Gender, ethnic, and social class patterns, Analyses of Social Issues and Public Policy, 2013, 13, 1, 347, 369, 10.1111/asap.12033</item> <item>30 Mann A, Legewie J, DiPrete TA, The role of school performance in narrowing gender gaps in the formation of STEM aspirations: a cross-national study, Frontiers in Psychology, 2015, 6, 171, 10.3389/fpsyg.2015.00171</item> <item>31 Marginson S, Tytler R, Freeman B, Roberts K, STEM: Country comparisons, 2013, Melbourne, Australia, Report for the Australian Council of Learned Academies ACOLA</item> <item>32 Marjoribanks K, Learning environments, family contexts, educational aspirations and attainment: A moderation-mediation model extended, Learning Environments Research, 2003, 6, 3, 247, 265, 10.1023/A:1027327707647</item> <item>33 Novakovic A, Fouad NA, Background, personal, and environmental influences on the career planning of adolescent girls, Journal of Career Development, 2013, 40, 3, 223, 244, 10.1177/0894845312449380</item> <item>34 Science, technology, engineering and mathematics in the national interest: A strategic approach, 2013, Canberra, Australia, Australian Government</item> <item>35 Science, technology, engineering, mathematics: Australia’s future, 2014, Canberra, Australia, Australian Government</item> <item>36 Packard BW-L, Nguyen D, Science career-related possible selves of adolescent girls: A longitudinal study, Journal of Career Development, 2003, 29, 4, 251, 263</item> <item>37 PricewaterhouseCoopers (PwC). (2015)., Future-proofing Australia’s workforce by growing skills in science, technology, engineering and maths (STEM): A smart move. Retrieved from, https://www.pwc.com.au/pdf/a-smart-move-pwc-stem-report-april-2015.pdf.</item> <item>38 Riegle-Crumb C, King B, Grodsky E, Muller C, The more things change, the more they stay the same? Prior achievement fails to explain gender inequality in entry into STEM college majors over time, American Educational Research Journal, 2012, 49, 6, 1048, 1073, 10.3102/0002831211435229</item> <item>39 Robnett RD, Leaper C, Friendship groups, personal motivation, and gender in relation to high school students’ STEM career interest, Journal of Research on Adolescence, 2013, 23, 4, 652, 664, 10.1111/jora.12013</item> <item>40 Sadler PM, Sonnert G, Hazari Z, Tai R, Stability and volatility of STEM career interest in high school: A gender study, Science Education, 2012, 96, 3, 411, 427, 10.1002/sce.21007</item> <item>41 Sax LJ, Kanny MA, Riggers-Piehl TA, Whang H, Paulson LN, “But I’m not good at math”: The changing salience of mathematical self-concept in shaping women’s and men’s STEM aspirations, Research in Higher Education, 2015, 56, 8, 813, 884, 10.1007/s11162-015-9375-x</item> <item>42 Schmitt-Wilson S, Welsh MC, Vocational knowledge in rural children: A study of individual differences and predictors of occupational aspirations and expectations, Learning and Individual Differences, 2012, 22, 6, 862, 867, 10.1016/j.lindif.2012.06.003</item> <item>43 Schoon L, Parsons S, Teenage aspirations for future careers and occupational outcomes, Journal of Vocational Behavior, 2002, 60, 2, 262, 288, 10.1006/jvbe.2001.1867</item> <item>44 Shapka JD, Domene JF, Keating DP, Trajectories of career aspirations through adolescence and young adulthood: Early math achievement as a critical filter, Educational Research and Evaluation, 2006, 12, 4, 347, 358, 10.1080/13803610600765752</item> <item>45 Shumow L, Schmidt JA, Haumann R, Zimmer G, Academic grades and motivation in high school science classrooms among male and female students: Associations with teachers’ characteristics, beliefs, and practices, Handbook of academic performance predictors, learning strategies, and influence of gender, 2013, Hauppauge, NJ, Nova, 53, 72</item> <item>46 St Clair R, Benjamin A, Performing desires: The dilemma of aspirations and educational attainment, British Educational Research Journal, 2011, 37, 3, 501, 517, 10.1080/01411926.2010.481358</item> <item>47 Stout JG, Dasgupta N, Hunsinger M, McManus MA, STEMing the tide: Using ingroup experts to inoculate women’s self-concept in science, technology, engineering, and mathematics (STEM), Journal of Personality and Social Psychology, 2011, 100, 2, 255, 270, 10.1037/a0021385</item> <item>48 Thomson, S., Wennert, N., O’Grady, E., & Rodrigues, S. (2016)., TIMSS 2015: a first look at Australia’s results. Retrieved from Victoria:, http://research.acer.edu.au/cgi/viewcontent.cgi?article=1000&context=timss_2015.</item> <item>49 Tuijl C, Molen JH, Study choice and career development in STEM fields: an overview and integration of the research, International Journal of Technology and Design Education, 2015, 26, 2, 159, 183, 10.1007/s10798-015-9308-1</item> <item>50 Wang M-T, Degol J, Motivational pathways to STEM career choices: Using expectancy-value perspective to understand individual and gender differences in STEM fields, Developmental Review, 2013, 33, 4, 304, 340, 10.1016/j.dr.2013.08.001</item> <item>51 Watson M, Nota L, McMahon M, Child career development: Present and future trends, International Journal for Educational and Vocational Guidance, 2015, 15, 2, 95, 97, 10.1007/s10775-015-9308-4</item> <item>52 Watt HM, Shapka JD, Morris ZA, Durik AM, Keating DP, Eccles JS, Gendered motivational processes affecting high school mathematics participation, educational aspirations, and career plans: A comparison of samples from Australia, Canada, and the United States, Developmental Psychology, 2012, 48, 6, 1594, 1612, 10.1037/a0027838</item> <item>53 Wilson R, Mack J, Declines in high school mathematics and science participation: Evidence of students’ and future teachers’ disengagement with maths, International Journal of Innovation in Science and Mathematics Education (formerly CAL-laborate International), 2014, 22, 7, 35, 48</item> <item>54 Zipin L, Sellar S, Brennan M, Gale T, Educating for futures in marginalized regions: A sociological framework for rethinking and researching aspirations, Educational Philosophy and Theory, 2015, 47, 3, 227, 246, 10.1080/00131857.2013.839376</item> </ulist> <aug> <p>By Kathryn Holmes; Jennifer Gore; Max Smith and Adam Lloyd</p> </aug> <nolink nlid="nl1" bibid="bib34" firstref="ref1"></nolink> <nolink nlid="nl2" bibid="bib50" firstref="ref2"></nolink> <nolink nlid="nl3" bibid="bib53" firstref="ref3"></nolink> <nolink nlid="nl4" bibid="bib31" firstref="ref4"></nolink> <nolink nlid="nl5" bibid="bib25" firstref="ref6"></nolink> <nolink nlid="nl6" bibid="bib35" firstref="ref7"></nolink> <nolink nlid="nl7" bibid="bib48" firstref="ref8"></nolink> <nolink nlid="nl8" bibid="bib37" firstref="ref9"></nolink> <nolink nlid="nl9" bibid="bib20" firstref="ref13"></nolink> <nolink nlid="nl10" bibid="bib23" firstref="ref14"></nolink> <nolink nlid="nl11" bibid="bib43" firstref="ref15"></nolink> <nolink nlid="nl12" bibid="bib18" firstref="ref16"></nolink> <nolink nlid="nl13" bibid="bib19" firstref="ref17"></nolink> <nolink nlid="nl14" bibid="bib2" firstref="ref18"></nolink> <nolink nlid="nl15" bibid="bib5" firstref="ref19"></nolink> <nolink nlid="nl16" bibid="bib39" firstref="ref20"></nolink> <nolink nlid="nl17" bibid="bib52" firstref="ref21"></nolink> <nolink nlid="nl18" bibid="bib42" firstref="ref22"></nolink> <nolink nlid="nl19" bibid="bib49" firstref="ref23"></nolink> <nolink nlid="nl20" bibid="bib12" firstref="ref25"></nolink> <nolink nlid="nl21" bibid="bib13" firstref="ref28"></nolink> <nolink nlid="nl22" bibid="bib21" firstref="ref29"></nolink> <nolink nlid="nl23" bibid="bib33" firstref="ref30"></nolink> <nolink nlid="nl24" bibid="bib36" firstref="ref31"></nolink> <nolink nlid="nl25" bibid="bib40" firstref="ref32"></nolink> <nolink nlid="nl26" bibid="bib44" firstref="ref33"></nolink> <nolink nlid="nl27" bibid="bib24" firstref="ref36"></nolink> <nolink nlid="nl28" bibid="bib6" firstref="ref37"></nolink> <nolink nlid="nl29" bibid="bib10" firstref="ref38"></nolink> <nolink nlid="nl30" bibid="bib8" firstref="ref39"></nolink> <nolink nlid="nl31" bibid="bib11" firstref="ref40"></nolink> <nolink nlid="nl32" bibid="bib28" firstref="ref41"></nolink> <nolink nlid="nl33" bibid="bib47" firstref="ref42"></nolink> <nolink nlid="nl34" bibid="bib29" firstref="ref47"></nolink> <nolink nlid="nl35" bibid="bib30" firstref="ref48"></nolink> <nolink nlid="nl36" bibid="bib41" firstref="ref49"></nolink> <nolink nlid="nl37" bibid="bib45" firstref="ref50"></nolink> <nolink nlid="nl38" bibid="bib15" firstref="ref51"></nolink> <nolink nlid="nl39" bibid="bib17" firstref="ref52"></nolink> <nolink nlid="nl40" bibid="bib1" firstref="ref54"></nolink> <nolink nlid="nl41" bibid="bib38" firstref="ref55"></nolink> <nolink nlid="nl42" bibid="bib14" firstref="ref60"></nolink> <nolink nlid="nl43" bibid="bib46" firstref="ref62"></nolink> <nolink nlid="nl44" bibid="bib54" firstref="ref63"></nolink> <nolink nlid="nl45" bibid="bib3" firstref="ref64"></nolink> <nolink nlid="nl46" bibid="bib26" firstref="ref67"></nolink> <nolink nlid="nl47" bibid="bib16" firstref="ref68"></nolink> <nolink nlid="nl48" bibid="bib22" firstref="ref69"></nolink> <nolink nlid="nl49" bibid="bib51" firstref="ref70"></nolink> <nolink nlid="nl50" bibid="bib32" firstref="ref73"></nolink> <nolink nlid="nl51" bibid="bib9" firstref="ref75"></nolink> <nolink nlid="nl52" bibid="bib4" firstref="ref77"></nolink> <nolink nlid="nl53" bibid="bib27" firstref="ref80"></nolink> <nolink nlid="nl54" bibid="bib7" firstref="ref89"></nolink>
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  Label: Title
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  Data: An Integrated Analysis of School Students' Aspirations for STEM Careers: Which Student and School Factors Are Most Predictive?
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  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Holmes%2C+Kathryn%22">Holmes, Kathryn</searchLink><br /><searchLink fieldCode="AR" term="%22Gore%2C+Jennifer%22">Gore, Jennifer</searchLink><br /><searchLink fieldCode="AR" term="%22Smith%2C+Max%22">Smith, Max</searchLink><br /><searchLink fieldCode="AR" term="%22Lloyd%2C+Adam%22">Lloyd, Adam</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22International+Journal+of+Science+and+Mathematics+Education%22"><i>International Journal of Science and Mathematics Education</i></searchLink>. Apr 2018 16(4):655-675.
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  Label: Availability
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  Data: Springer. 233 Spring Street, New York, NY 10013. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-348-4505; e-mail: service-ny@springer.com; Web site: http://www.springerlink.com
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  Label: Peer Reviewed
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  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 21
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2018
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Meta+Analysis%22">Meta Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22STEM+Education%22">STEM Education</searchLink><br /><searchLink fieldCode="DE" term="%22Science+Careers%22">Science Careers</searchLink><br /><searchLink fieldCode="DE" term="%22Predictor+Variables%22">Predictor Variables</searchLink><br /><searchLink fieldCode="DE" term="%22Science+Education%22">Science Education</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Surveys%22">Student Surveys</searchLink><br /><searchLink fieldCode="DE" term="%22Career+Choice%22">Career Choice</searchLink><br /><searchLink fieldCode="DE" term="%22Regression+%28Statistics%29%22">Regression (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Academic+Achievement%22">Academic Achievement</searchLink><br /><searchLink fieldCode="DE" term="%22Science+Interests%22">Science Interests</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Attitudes%22">Student Attitudes</searchLink><br /><searchLink fieldCode="DE" term="%22Cultural+Capital%22">Cultural Capital</searchLink><br /><searchLink fieldCode="DE" term="%22Parent+Background%22">Parent Background</searchLink><br /><searchLink fieldCode="DE" term="%22Gender+Differences%22">Gender Differences</searchLink><br /><searchLink fieldCode="DE" term="%22Occupational+Aspiration%22">Occupational Aspiration</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1007/s10763-016-9793-z
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 1571-0068
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Declining enrolments in science, technology, engineering and mathematics (STEM) disciplines and a lack of interest in STEM careers are concerning at a time when society is becoming more reliant on complex technologies. We examine student aspirations for STEM careers by drawing on surveys conducted annually from 2012 to 2015. School students in years 3 to 12 (n = 6492) were asked to indicate their occupational choices. A logistic regression analysis showed that being in the older cohorts, possessing high cultural capital, being male, having a parent in a STEM occupation and high prior achievement in reading and numeracy, were significant. This analysis provides a strong empirical basis for school-based initiatives to improve STEM participation. In particular, strategies should target the following: the persistent lack of interest by females in some careers, improving student academic achievement in both literacy and numeracy and expanding knowledge of STEM careers, especially for students without familial STEM connections.
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  Data: 54
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  Data: 2018
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  Data: EJ1172611
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        Value: 10.1007/s10763-016-9793-z
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      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 21
        StartPage: 655
    Subjects:
      – SubjectFull: Meta Analysis
        Type: general
      – SubjectFull: STEM Education
        Type: general
      – SubjectFull: Science Careers
        Type: general
      – SubjectFull: Predictor Variables
        Type: general
      – SubjectFull: Science Education
        Type: general
      – SubjectFull: Student Surveys
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      – SubjectFull: Career Choice
        Type: general
      – SubjectFull: Regression (Statistics)
        Type: general
      – SubjectFull: Academic Achievement
        Type: general
      – SubjectFull: Science Interests
        Type: general
      – SubjectFull: Student Attitudes
        Type: general
      – SubjectFull: Cultural Capital
        Type: general
      – SubjectFull: Parent Background
        Type: general
      – SubjectFull: Gender Differences
        Type: general
      – SubjectFull: Occupational Aspiration
        Type: general
    Titles:
      – TitleFull: An Integrated Analysis of School Students' Aspirations for STEM Careers: Which Student and School Factors Are Most Predictive?
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            NameFull: Gore, Jennifer
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            NameFull: Smith, Max
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            NameFull: Lloyd, Adam
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              Value: 1571-0068
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            – TitleFull: International Journal of Science and Mathematics Education
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