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

In the dark… …all schools and education systems look the same… But with a little light….

But with a little light…. …important differences become apparent….

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

% 1. Excluding ISCED 3C short programmes 2. Year of reference Including some ISCED 3C short programmes 3. Year of reference Growth in baseline qualifications A world of change Approximated by percentage of persons with high school or equivalent qualfications in the age groups 55-64, 45-55, und years

Consider Korea 1960sBeginning of 21 st Century Wealth Below all South American countries. Around level of Afghanistan. 20 th in OECD. Educational expenditure 1 st in OECD in % of GDP. Educational attainment completing secondary – 24 th in OECD. completing tertiary – 20 th in OECD. completing secondary – 1 st in OECD. completing tertiary – 3 rd in OECD. Educational quality 2 nd in reading, 2 nd in mathematics 4 th in science in OECD. Educational equity 2 nd in OECD.

Growth in university-level qualifications Approximated by the percentage of persons with ISCED 5A/6 qualification born in the age groups shown below (2005) % A1.3a 1.Year of reference Year of reference 2003.

Gender difference in percentage points A8.3 Rising female participation in university education explains much of this expansion Gender difference in university attainment in percentage points Men have higher attainment Women have higher attainment 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 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

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

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

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

Key features of PISA PISA is a three-yearly international assessment that… …examines the performance of 15-year-olds in key subject areas as well as a wider range of educational outcomes that include students attitudes to learning, their beliefs about themselves, and their learning strategies …collects contextual data from school principals and parents Volume 3½ hours of mathematics assessment, large part open-ended (focus subject changes every three years) 1 hour for each of reading, science and problem solving ½ hour for questionnaire on background, learning habits, learning environment, engagement and motivation Coverage Representative probability samples of between 3,500 and 50, year-old students Most federal countries also draw regional samples PISA covers roughly nine tens of the world economy

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

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.

Average performance of 15-year-olds in mathematics (PISA 2003) High mathematics performance Low mathematics performance

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

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

Performance in science Females perform better Males perform better Performance in problem solving Females perform better Males perform better 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 Science and problem solving

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

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

Gender differences in student performance Some observations r In reading, girls are far ahead In all countries, girls significantly outperform boys in reading r 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 r 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)

Motivational patterns and math performance Performance in mathematics Higher FemalesHigher Males Interest in mathematics Instrumental motivation Higher FemalesHigher MalesHigher FemalesHigher MalesHigher FemalesHigher Males Anxiety in mathematics

Interest in and enjoyment of mathematics and performance in mathematics Females Males LowHigh OECD (2004), Learning for tomorrow’s world: First results from PISA 2003, Table 3.1, p.359.

Instrumental motivation in mathematics and performance in mathematics Females Males LowHigh OECD (2004), Learning for tomorrow’s world: First results from PISA 2003, Table 3.2a, p.360.

Anxiety in mathematics and performance in mathematics Females Males LowHigh OECD (2004), Learning for tomorrow’s world: First results from PISA 2003, Table 3.8, p.374.

Do attitudes matter? Gender difference in interest in math among 15-year-olds and gender differences in math/computer university graduates Gender difference (M-F) in iinstrumental motivation in mathematics at 15 years-old (2003) Percentage of tertiary type-A qualifications awarded to females in mathematics and computing (2005) A3.5 1.Percentage of females graduated in mathematics and computing for tertiary-type A and advanced programmes. 2.The greater the gender difference, the less females are motivated compared to males. R 2 = year-old boys show higher math interest 15-year-old boys and girls show equal math interest Equal proportions of male and female math/computer graduates

Gender differences in attitudes Some observations r 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

Percentage points A8.3 Difference between unemployment rates of females and males, by level of education attainment

% of index A9.2 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) 1. Year of reference Year of reference Year of reference Year of reference 2005.

% of index A9.2 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). 1. Year of reference Year of reference Year of reference Year of reference 2005.

Private internal rates of return for an individual obtaining an upper secondary or post-secondary non- tertiary education, ISCED 3/4 and for an individual obtaining a university-level degree, ISCED 5/6 (2003) A9.1

% A9.3 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

Gender differences – policy levers National educ., social and economic context Structures, resource alloc. and policies Social & economic outcomes of education Community and school characteristics Student learning, teacher working conditions Socio-economic background of learners Antecedents contextualise or constrain ed policy The learning environment at school Teaching, learning practices and classroom climate Individ attitudes, engagement and behaviour Output and performance of institutions Quality of instructional delivery Quality and distribution of knowledge & skills Policy Levers shape educational outcomes Outputs and Outcomes impact of learning Individual learner Level A Instructional settings Level B Schools, other institutions Level C Country or system Level D Domain 3Domain 2 Domain 1

–All national and international publications –The complete micro-level database …and remember: Without data, you are just another person with an opinion

High ambitions and clear standards Access to best practice and quality professional development r 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

Challenge and support Weak support Strong support Low challenge High challenge Strong performance Systemic improvement Poor performance Improvements idiosyncratic Conflict Demoralisation Poor performance Stagnation

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

Woessmann, 2005 PISA math performance School autonomy and external exams Pooled international data

Strong ambitions Access to best practice and quality professional development Accountability Devolved responsibility, the school as the centre of action Integrated educational opportunities Individualised learning The quality of an education system cannot exceed the quality of its teachers

High ambitions Access to best practice and quality professional development Accountability and intervention in inverse proportion to success Individualised learning Devolved responsibility, the school as the centre of action Integrated educational opportunities