1. Helen M. G. Watt Monash University ACER 2016 Conference: “Improving STEM Learning: What will it take?” 7—9 August: Brisbane Convention & Exhibition Centre Promoting Girls', and Boys', Engagement and Participation in Senior Secondary STEM Fields and Occupational Aspirations [ “STEM”: Science, Technology, Engineering & Mathematics ] Funding Acknowledgment: Australian Research Fellowship, 2011-2015 Australian Research Council DP 110100472, 2011-2015

2. Who is studying STEM and do we care? Economic drivers Maths as the “critical filter”

3. “Maths and science …drive the data analysis, forecasting, modelling, decision- making, management design and technological principles that underpin every sector of enterprise.” (p.4) “For Australia to succeed in a highly competitive global economy, students need to have a strong grasp of basic maths and science and encouragement to pursue careers in this area… 0.4% of Australian university students graduate with maths and statistics qualifications compared with the OECD average of around 1%.” (p.2) New Directions for Maths and Science (2007). Published by ALP. “The STEM fields …are critical engines of innovation and growth: …while only about 5% of the US workforce is employed in STEM fields, the STEM workforce accounts for more than 50% of the nation's sustained economic growth.” Mathematics, Engineering & Science in the National Interest (May 2012). Published by the Office of the Chief Scientist. STEM participation is an Issue in Australia, as in U.S. and many countries of the OECD

4. Approx. ¾ of young Australians remain at school to Year 12. Group differences occur for: ACER: Melbourne. Online: http://research.acer.edu.au/lsay research/37http://research.acer.edu.au/lsay research/37 Year 12 Participation Characteristics Percentage Gap for who undertakes Year 12 Gender10% more for girls Socioeconomic status15% more for highest vs. lowest of 6 SES groups Cultural background 8% more for non-English speaking background Earlier achievement31% more for highest vs. lowest of 4 achievement groups School sector14% more for independent vs. government schools Home location 8% more for metropolitan vs. non-metropolitan homes

5. National Snapshot: Yr 12 Maths Most Yr 12 students study at least some maths (72% in 2010), but the proportion choosing advanced (and intermediate) levels has been declining. % Yr 12 students Advanced–prerequisites or assumed knowledge for university courses in engineering & physical, mathematical, computer sciences Intermediate–while not assumed knowledge for university courses, are useful for students who wish to pursue study in areas such as the social sciences or psychology, where techniques such as those found in statistics are applied; Fundamental/Basic–basic mathematics skills and are terminal in their nature (do not usually feed into further studies).

6. National Snapshot: Yr 12 Sciences % Yr 12 students Between 1992—2009, the % Year 12 students taking sciences fell by: 32% biology, 23% chemistry, 31% physics. Year 12 science participation (% of Year 12 cohort): 1976—2007 Biology Chemistry Geology Other Physics Psych. Participation in Science, Mathematics and Technology in Australian Education (Aug. 2008). ACER: Melbourne. Online : http://research.acer.edu.au/acer_monographs/4 http://research.acer.edu.au/acer_monographs/4

7. Certain groups are overrepresented in subject selections: 1 even after allowing for the effect of other related influences. ACER: Melbourne. Online: http://research.acer.edu.au/lsay research/37http://research.acer.edu.au/lsay research/37 Year 12 Subject Selections Advanced Maths, Physics, Chemistry Arts & Home sciences Technical & Computer studies BoysGirlsBoys Students aspiring to higher ed., & Higher prior achievers 1 Lower prior achievers 1 Higher SESLower SES Asian students (more than any other cultural group)

8. University Commencement Fields 2006, shows non-Science proportion Participation in Science, Mathematics and Technology in Australian Education (Aug. 2008). ACER: Melbourne. Online : http://research.acer.edu.au/acer_monographs/4 http://research.acer.edu.au/acer_monographs/4

9. University Commencement Fields 2002—2010 Completion rates are higher for Health & Natural/Physical sciences (73/69%), lower for Engineering (58%) & Agriculture/Environment (48-60%), lowest for I.T. (50%). Natural/Physical sciences Health Agriculture/Env.’t Engineering I.T. N enrolments Office of the Chief Scientist 2012a, Health of Australian Science, Australian Government, p. 71.

10. Secondary Teacher Graduate Specialisms 2006, N = 1,875 DEST (2006). Survey of final year teacher education students. Adapted from Table 2, p.5.

11. Secondary Teachers teaching in STEM: % with training in teaching method 2007 Staff in Australia’s Schools (SiAS) Survey 2007 (2008). DEEWR: Canberra.

12. Secondary Teachers teaching in STEM: % with training in teaching method 2007  2010 Staff in Australia’s Schools (SiAS) Survey 2010 (2011). DEEWR: Canberra. 6% drop 15% drop 6% increase 17% increase SAME

13. Inspirational Teaching: “Inspired teaching is undoubtedly the key to the quality of our system, and to raising student interest to more acceptable levels. It is the most common thread running through the responses in every country where the issue has been assessed in any detail.” (p. 7) cf. “Messy Science” 9 News: 30 Oct. 2013 Inspired School Leadership: “Teachers and schools exert a substantial influence on their students and the choices they make. Leadership in schools is a key. Inspiring leaders will encourage innovation and support teachers as they develop particular ways to deliver the curriculum.” (p. 7) Mathematics, Engineering & Science in the National Interest (May 2012). Published by the Office of the Chief Scientist. Keys to improve STEM interest & participation

14. The “STEM Pipeline”

16. Current formulation of the expectancy-value model of achievement choices. Source: Simpkins, S. D., Fredricks, J., & Eccles, J. S. (2015). Parent beliefs to youth choices: Mapping the sequence of predictors from childhood to adolescence. Monographs of the Society for Research in Child Development, 80(2), 1-151.

17. What motivates students in mathematics at school, and beyond? N = 1,323 Australian adolescents, grades 7-11, mid 1990’s  followed up 17 years later in 2015

18. d ranges from.13 to.18 (all p <.05) Mathematical Educational Decisions [ Australian N = 1,323 ]

19. d ranges from.12 to.21 (all p <.05) Mathematical Occupational Decisions [ Australian N = 1,323 ]

20. Why do Girls Have Lower Maths Aspirations? These data reflect those at the national level, and resonate with statistics from other countries (USA?). Not due to achievement differences. Need to search for other explanations. In the Eccles et al. expectancy-value framework expectancies/self-concepts and task values are main influences.

21. Self-Concepts & Task Values Self-concepts (Perceived talent) e.g., “Compared with other students in your class, how talented do you consider yourself to be at maths?” (1: not at all – 7: very talented) Task values Intrinsic value e.g., “How much do you like maths, compared with your other subjects at school?” (1: much less – 7: much more) Utility value e.g., “How useful do you think mathematical skills are in the workplace?” (1: not at all – 7: very useful)

22. gender (M=0, F=1) achievement (grade 9) intrinsic value (grade 10) self-concepts (grade 10) perc. difficulty (grade 10) utility value (grade 10) high school maths level (grade 11) Adj.R 2 =.24 maths career plans (grade 11) Adj.R 2 =.21.12 -.20 -.15 -.43 -.14 -.48.25.31.21.20.15.35 Impacts of Self-Concepts and Task Values on Maths Decisions ?

23. Interaction Effect: Gender X Utility Value on Maths Occupational Decisions

24. (2008, American Psychological Association)

26. Leaky pipelines and degree of choice, Critical filters and glass ceilings… How relevant is degree of choice and early specialisation across cultural settings? Is it possible that girls choose careers that are equally prestigious, but less maths- related? e.g., Lawyer Do mathematical self-concepts and values have relevance to aspired career prestige, and level of education? (“critical filter”) For girls who aspire to mathematical careers, are they of equal status to boys’ maths-related plans? (“leaky pipeline has glass ceiling”)

27. Samples & Measures 3 opportune samples from different (similar) contexts: - Australian grades 9  11, N = 358 (98% retention) - U.S. grades 10  12, N = 418 (67% retention) - Canadian grades 9/10  11/12, N = 471 (98% retention) Key measures (slight sample differences): - expectancy-value motivations T1: self-concepts and values, - senior high school math courses T2 (large missing data for U.S., limitations for Canadian), - aspired education level T2, - aspired career: math-relatedness & prestige T2. Gendered patterns and mean differences per setting were compared using multigroup SEM (partial scalar invariance, acceptable model fits)

28. Settings & Choice Structures Australian sample (NSW): The middle course (2-units) was prerequisite to certain university degrees; none required the very or next highest. In both North American settings, less-difficult maths could satisfy high school graduation, but not university admission: USA sample (Michigan): most universities require algebra I, geometry, and algebra II (or trigonometry/calculus) leaving less room for choices. Canadian sample (Ontario): students took at least 6 advanced courses. To enter university, one must be grade 11 maths. To enter scientific degrees, another is grade 12 maths.

29. Key Questions Gender differences more pronounced when there is a real option for girls to “opt out”? Interests to play a greater role when there is more choice / in culture which values self-expression? Importance value (attainment/utility) more relevant for girls? Maths expectancies/values would relate to aspired career prestige? (“critical filter”) High school math courses  math-related career plans? (“leaky pipeline”) Maths career plans would relate to aspired career prestige … more strongly for boys (= leaky pipeline has glass ceiling)?

30. Stereotypic Gender Differences Comparative testing regime in N. America focuses attention on ability? Degree of choice in Australia focuses attention on interests? Earlier specialisation amplifies gender differences in math participation, with flow-on effects to maths-related career choices? AustralianUSACanadian intrinsic value high school math courses aspired career math-relatedness self-concept

31. Influences across Contexts? Different structures appear to activate different choice processes: - ability/expectancy beliefs important predictor in N. American samples - intrinsic value in Australian Importance value: - played a greater role for girls in career choices: implications for making links to math social uses and purposes in school - boys’ choices may be more constrained Math motivations impacted non-mathematical educational aspirations, and (indirectly) career prestige: - Australian: intrinsic and importance values more relevant, - U.S.: ability/expectancy beliefs, - Canada somewhere in between, cf. Inglehart-Welzel Cultural Map of the World Survival/Self-expression values (Australia=3 rd, Canada 6 th, U.S. 8 th ).

32. High school math as a “pipeline” to particular careers: - Yes, in Australian and Canadian, - No, in U.S. “broken pipeline” Career math-relatedness should relate to career prestige: - moderately related, often assumed but not directly tested, - n.s. gender differences (“leaky pipeline” did not have a “glass ceiling”) Limitations of reliance on aspirations as outcome measures. Influences across Contexts?

33. 17 years later…. How do these translate into actual occupational outcomes? So far, 643 (of the original 1,323) STEPS participants have been followed up, to discover actual occupational outcomes

34. None N = 187 Maths-related career aspirations High school Maths-related careers 17 years later 52 8 Low N = 112 Mid N = 195 High N = 82 None N = 110 Low N = 134 Mid N = 270 High N = 62 53 31 102 15 74 18 53 28 36 11 15 41 Overall (  =.23 p <.001), Boys (  =.20 p <.001) & Girls (  =.21 p <.001) 10 29

35. Motivations matter – even 17 years later! N = 563 (300 boys, 263 girls) Significant (modest) relationships with actual maths-related careers

36. How do self-concepts and values develop?

37. School Environment Research: The Junior High Transition Earlier explanations related to pubertal timing and adolescent stress. Those explanations were challenged by Eccles, Midgley, Wigfield and colleagues. They documented differences in environment pre- and post- transition that account for declining student motivations: - Disrupted peer networks - Increase in normative assessment - Multiple teachers - More curricular differentiation - Etc. Longer-term studies show this may be part of a continuing pattern through school, and students do not “recover” post-transition.

39. Boys’ and Girls’ Self-Concepts through Secondary School Watt, H.M.G. (2004). Development of adolescents' self perceptions, values and task perceptions according to gender and domain in 7th through 11 th grade Australian students. Child Development, 75, 1556-1574. MathsEnglish 7a 7b 8 9 10 11 grade 7a 7b 8 9 10 11 grade boys girls no sig. gender diffs

40. 7a7b891011 boys girls Boys’ and Girls’ Intrinsic Value through Secondary School Watt, H.M.G. (2004). Ibid.

41. 7a7b891011 no gender differences Boys’ and Girls’ Utility Value through Secondary School

42. Explanations Greater “realism” may explain the decline: increase in social comparisons normative assessment [i.e. individual performance evaluated against others] But what about the gender differences? Stable magnitudes of gender difference imply they are in place early on and continue the same. See also: - Nagy, G., Watt, H.M.G., Eccles, J.S., Trautwein, U., Lüdtke, O., & Baumert, J. (2010). The development of students' mathematics self-concept in relation to gender: Different countries, different trajectories? Journal of Research on Adolescence, 20, 482-506. - Frenzel, A.C., Goetz, T., Pekrun, R., & Watt, H.M.G. (2010). Development of mathematics interest in adolescence : Influences of gender, family and school context. Journal of Research on Adolescence, 20, 507-537 In the U.S., Jacobs et al. (2002) found differences as early as grade 2! Differences in self-concepts and values need to be addressed from childhood. Consider achievement data: “illusory glow” for boys? In an intensive qualitative phase (120 Year 9 students), same stimuli could led to opposite interpretations by underestimating girls/overestimating boys (interpretations of encouragement from significant others)

43. A new “contemporary” longitudinal study: Focus on maths and sciences Probing sources of maths & science motivations: teachers, parents, peers & other valued life goals N = 1,172 in 9 Melbourne/Sydney schools, spanning grade 10 until post-school

44. Grade 10 students from 9 schools in Melbourne/Sydney: May-June 2012/2013 (N = 1,172) Sample SchoolCompositionSelective? Government1 Coeducational 1 Coeducational 1 Girls -yy-yy Catholic1 Coeducational 1 Boys 1 Girls ------ Independent1 Coeducational 2 Girls-

45. Year 12 STEM Participation % within gender

46. Year 12 Career Plans mean ratings Sig. effects of gender and time (n.s. interaction) Sig. effect of time (n.s. gender, and interaction) Maths Science

47. What do they want to do? “How much would you like to have these kinds of career?” (Yr 12 mean ratings) * Sig. gender differences

48. Perceived talent Intrinsic value Importance value Effort cost: 3 items e.g. “Achieving in sounds like it really requires more effort than I'm willing to put into it” Psychological cost: 3 items e.g. “I'm concerned that I won't be able to handle the stress that goes along with studying ” Social cost: 2 items e.g. “I'm concerned that working hard in classes might mean I lose some of my close friends” 1: not at all–7: extremely Contemporary Attitudes to Maths & Science: including costs

49. Year 10 Maths: Boys higher: Self-concept, Intrinsic value, Importance value Girls higher: Psychological cost Year 10 Science: Boys higher: Self-concept Girls higher: Psychological cost, Social cost Profiles for different types of students were examined, and: gender and achievement effects consequences for aspired career type and psychological wellbeing (DASS) Gender Differences and Motivation Profiles: Implications for STEM Career plans & Wellbeing

50. Science & Maths Motivation Profiles Maths Science C1: Positively engaged C2: Struggling ambitious C3: Disengaged C4: Indifferent costs (positive) EV

51. Positively engaged and Struggling ambitious had similar reported history of results, high aspired careers, and aimed marks. But, high costs perceived by Struggling ambitious, associated with debilitated psychological wellbeing. Disengaged had similarly good psychological health to Positively engaged, but lowest career aspirations, aimed marks, and history of results. low Perceived talent, Intrinsic and Utility values held by Disengaged, associated with lowered achievement/career-striving; perceived low costs apparently bolstered wellbeing. Indifferent (maths only) had rather depressed wellbeing, moderate aimed marks and history of results, rather low maths career aspirations. Even moderate perceived costs exert negative effects on achievement striving and psychological health. Antecedents & Consequences

52. Disengaged Positively engaged Indifferent Struggling ambitious psychological wellbeing achievement striving hi-hi lo-lo hi wellbeing- lo ach. striving lo wellbeing- hi ach. striving more girls (maths) more boys (maths) (maths) Clusters were similar across mathematics/science, differentiated along dimensions of achievement motivation and psychological health:

53. Domain Specificity? SCIENCE positively engaged struggling ambitiousdisengaged MATHS positively engaged 524833 struggling ambitious 33020 disengaged 131436 indifferent 36 63  2 (6) = 44.008, p <.001

54. What Careers are Youth Today Motivated to Pursue? Motivations for Career Choice: MCC Social influences Prior experiences Task Demand -Expert career -Cognitive challenge Task Return -Social status -Salary Interpersonal environment -Teamwork -Autonomy Self perceptions - Abilities Intrinsic Value Personal Utility Value -Job security -Time for family -Job transferability -Career progression prospects Social Utility Value -Enhance social equity -Make social contribution -Work with youth Easy job Choice of career

55. Motivations for Career Choice framework

56. “It is important to me to have a career that…” Whole Sample

57. MCC Years 10—12 for Girls and Boys girls boys Girls higher Boys higher Similar gender differences in diverse undergraduate new sample (N = 697)

58. Career Conversations “How often have you talked with the following people about your future career or educational plans?” (1: Never – 7: A lot) * Sig. gender differences

59. Most important career motivators for girls and boys were interest, ability and salary; least important were wanting an easy job, social influences and the desire to work with youth. There were no gender differences for motivations related to own abilities, cognitive challenge, prior experiences, salary, status, family-flexibility, autonomy, teamwork, portability, or secure progression prospects. This clearly signals that girls do not prefer lower salary or lower status careers. Boys were significantly more motivated than girls by social influences, to pursue an expert career, and for an easy job. Girls were more motivated than boys by their interests, to make a social contribution, enhance social equity, and work with youth. These differences appear consistent with previous findings that girls and women are more interested in “Social” occupations that allow them to socially contribute and help others. A word on coeducation… Is There a Gender Problem?

60. 60 Within-Class Level -.11.11.14.13.14.27.31.24.35.13.22.30 -.08 What Can Educators Do? Between-Class Level.40.63.03 n.s..59

61. Learning Environments Science (per cohort) Maths (per cohort) 1 2 3 4 5 6 7 8 9 10 11 MASTERY: Our teacher… really wants us to enjoy learning new things recognises us for trying hard wants us to understand our work, not just memorise it PERFORMANCE: Our teacher… tells us how we compare to other students points out those students who get good grades as an example to all of us lets us know which students get the highest scores on tests

62. Conclusions and Outlook

63. STEM shortage especially in advanced maths and physical sciences, more pronounced in contemporary data (need to examine disaggregated STEM). Teachers who are asked to teach OOF in STEM, likely to affect student learning and engagement (but: more women teachers; fewer women in STEM!). “Pipeline”: (especially girls) opt out when they perceive a real choice. “Critical filter”: high school maths participation impacts aspired career prestige, as well as maths-related career plans. EV impact STEM studies and career aspirations:  values play a greater role when there is more choice,  importance value more important for girls,  EV (maths) impact prestige dimension of career aspirations. EV decline through secondary schooling, robust gender gap. Girls perceive lower talents than achievements warrant: consider divergent interpretations of social influences. Costs impact wellbeing : even students with high EV, achievements, aspirations. Aspirations modestly predict actual STEM-related careers:  need for more long-term longitudinal studies,  contrasting more different settings as “natural experiments”.

64. www.stepsstudy.org

65. Thank you! helen.watt@monash.edu