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Student learning outcomes from a gender perspective What do international assessments tell us?

Organisation for Economic Cooperation and Development (OECD). Student learning outcomes from a gender perspective What do international assessments tell us?. Washington, 2 October 2007 Andreas Schleicher Head, Indicators and Analysis Division OECD Directorate for Education.

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Student learning outcomes from a gender perspective What do international assessments tell us?

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  1. Organisation for Economic Cooperation and Development (OECD) Student learning outcomes from a gender perspectiveWhat do international assessments tell us? Washington, 2 October 2007 Andreas Schleicher Head, Indicators and Analysis Division OECD Directorate for Education

  2. How gender patterns in education have changed in the industrialised world A story of rapid progress

  3. Growth in baseline qualificationsA world of changeApproximated by percentage of persons with high school or equivalent qualfications in the age groups 55-64, 45-55, 45-44 und 25-34 years % 1 13 1 27 1. Excluding ISCED 3C short programmes 2. Year of reference 2004 3. Including some ISCED 3C short programmes 3. Year of reference 2003.

  4. Growth in university-level qualificationsApproximated by the percentage of persons with ISCED 5A/6 qualification born in the age groups shown below (2005) % A1.3a • Year of reference 2004. • Year of reference 2003.

  5. Rising female participation in university education explains much of this expansionGender difference in university attainment in percentage points Example 1: 22% of older Japanese men have a university degree, only 5% women do so 35% of younger men have a university degree, 21% of younger women do Gender difference in percentage points Men have higher attainment Women have higher attainment Example 2: 32% of older US American men have a university degree, only 25% women do so 27% of younger men have a university degree, 33% of younger women do A8.3

  6. How could the future look like?Percentage of 15-year-old boys and girls expecting to complete university (2003) *Statistically significant difference girls > boys

  7. In some sectors large gender differences persistNumber of tertiary science graduates per 100 000 employed 25-to-34-year-olds (2005) A3.4

  8. Where we are - and where we can be A review of gender differences in performance and student attitudes in today’s schools around the world

  9. PISA - OECD’s global assessment of what students know and can do with their knowledge Coverage of world economy 83% 77% 81% 85% 86% 87%

  10. Deciding what to assess... looking back at what students were expected to have learned …or… looking ahead to how well they can extrapolate from what they have learned and apply their knowledge and skills in novel settings. For PISA, the OECD countries chose the latter.

  11. High mathematics performance Average performanceof 15-year-olds in mathematics (PISA 2003) Low mathematics performance

  12. High mathematics performance High average performance Large socio-economic disparities High average performance High social equity Low mathematics performance Average performanceof 15-year-olds in mathematics Socially equitable distribution of learning opportunities Strong socio-economic impact on student performance Low average performance Large socio-economic disparities Low average performance High social equity

  13. High mathematics performance High average performance Large socio-economic disparities High average performance High social equity Low mathematics performance Socially equitable distribution of learning opportunities Strong socio-economic impact on student performance Low average performance Large socio-economic disparities Low average performance High social equity

  14. Science and problem solving Females perform better Males perform better Females perform better Males perform better Performance in science Performance in problem solving OECD (2004), Learning for tomorrow’s world: First results from PISA 2003, Table 6.7, p.449. OECD (2004), Problem solving for tomorrow’s world: First results from PISA 2003.

  15. Mathematics and reading Males perform better Females perform better Females perform better Males perform better Performance in mathematics Performance in reading

  16. Performance in mathematics Performance in reading Low performing boys and girlsPercentage of students at or below PISA level 1 % OECD (2004), Learning for tomorrow’s world: First results from PISA 2003, Tables 2.5b, 6.5, pp.355, 447.

  17. Gender differences in student performanceSome observations • In reading, girls are far ahead • In all countries, girls significantly outperform boys in reading • In mathematics, boys tend to be somewhat ahead • In most countries, boys outperform girls … but mostly by modest amounts… … and mainly because boys are overrepresented among top-performers while boys and girls tend to be equally represented in the “at risk” group • Within classrooms and schools, the gender gap is often larger • Strong problem-solving performance for girls suggests… … that it is not the cognitive processes underlying mathematics that give boys an advantage… … but the context in which mathematics appears in school • Why is the mathematics performance difference in PISA smaller than in other assessments? • Girls better on open-ended tasks (which dominate PISA) • Boys tend to do better on multiple-choice tasks (which dominate other assessments)

  18. Motivational patterns and math performance Higher Females Higher Males Higher Females Higher Males Higher Females Higher Males Higher Females Higher Males Instrumental motivation Anxiety in mathematics Performance in mathematics Interest in mathematics

  19. Do attitudes matter?Gender difference in interest in math among 15-year-oldsand gender differences in math/computer university graduates Equal proportions of male and female math/computer graduates Percentage of tertiary type-A qualifications awarded to females in mathematics and computing (2005) R2=0.35 15-year-old boys show higher math interest 15-year-old boys and girls show equal math interest Gender difference (M-F) in iinstrumental motivation in mathematics at 15 years-old (2003) A3.5 • Percentage of females graduated in mathematics and computing for tertiary-type A and advanced programmes. • The greater the gender difference, the less females are motivated compared to males.

  20. Gender differences in attitudesSome observations • In mathematics, attitudinal differences are far more pronounced than performance differences • Girls report much lower interest in mathematics, less self-belief as mathematics learners, less motivation to use mathematics in the future and much greater anxiety when learning mathematics • Boys perform slightly better than girls in mathematics, but are much more confident and less anxious learning mathematics… • Attitudinal patterns of school children are closely matched by current study and career choices, much more closely than performance patterns

  21. Relative earnings from employment (2005 or latest available year)By level of educational attainment and gender for 25-to-64-year-olds (upper secondary and post-secondary non-tertiary education=100) % of index A9.2 1. Year of reference 2002. 3. Year of reference 2004. 2. Year of reference 2003. 4. Year of reference 2005.

  22. Differences in earnings between females and males (2005 or latest available year)Average female earnings as a percentage of male earnings (30 to 44 age group), by level of educational attainment % A9.3

  23. Gender differences – policy levers Domain 1 Individual learner LevelA LevelB Instructional settings LevelC Schools, other institutions Country or system LevelD Domain 2 Domain 3 Outputs and Outcomesimpact of learning Policy Leversshape educational outcomes Antecedentscontextualise or constrain ed policy Quality and distribution of knowledge & skills Individ attitudes, engagement and behaviour Socio-economic background of learners Quality of instructional delivery Student learning, teacher working conditions Teaching, learning practices and classroom climate The learning environment at school Community and school characteristics Output and performance of institutions Social & economic outcomes of education National educ., social and economic context Structures, resource alloc. and policies

  24. Thank you ! www.pisa.oecd.org All national and international publications The complete micro-level database email: pisa@oecd.org Andreas.Schleicher@OECD.org … and remember: Without data, you are just another person with an opinion

  25. Sympathy doesn’t raise standards – aspiration does • PISA suggests that students and schools perform better in a climate characterised by high expectations and the readiness to invest effort, the enjoyment of learning, a strong disciplinary climate, and good teacher-student relations • Among these aspects, students’ perception of teacher-student relations and classroom disciplinary climate display the strongest relationships High ambitions and clear standards Access to best practice and quality professional development

  26. Challenge and support Strong support Poor performance Improvements idiosyncratic Strong performance Systemic improvement Lowchallenge Highchallenge Poor performance Stagnation Conflict Demoralisation Weak support

  27. High ambitions Devolved responsibility,the school as the centre of action Accountability and intervention in inverse proportion to success Access to best practice and quality professional development

  28. The quality of an education system cannot exceed the quality of its teachers Strong ambitions Devolvedresponsibility,the school as the centre of action Integrated educational opportunities Accountability Individualisedlearning Access to best practice and quality professional development

  29. High ambitions Devolved responsibility, the school as the centre of action Integrated educational opportunities Accountabilityand intervention in inverse proportion to success Individualisedlearning Access to best practice and quality professional development

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