Is the sky the limit to educational performance? Redesigning pedagogy - culture, knowledge and understanding

1. Organisation for Economic Cooperation and Development (OECD) Is the sky the limit to educational performance?Redesigning pedagogy - culture, knowledge and understanding CRPP Singapore 2007 Prof. Andreas Schleicher Head, Indicators and Analysis Division OECD Directorate for Education

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

4. In the dark……all students, schools and education systems look the same… But with a little light…. …important differences become apparent….

5. Baseline qualificationsA world of changeApprox. by % of persons with upper secondary qualfications in age groups 55-64, 45-55, 45-44 und 25-34 years 1 9 8 24 1 14

6. Overview 1. The PISA approach • Measuring the quality of learning outcomes and student engagement with learning 2. Where we are today - and where we can be • What PISA shows students in different countries can do with what they have learned… … and how this is mirrored in the distribution of learning opportunities 3. How we can get there • Some policy levers that emerge from international comparisons

7. The PISA approach Measuring the quality of learning outcomes

8. Key features of PISA 2003 Information collected Volume of the tests • 3½ hours of mathematics assessment • 1 hour for each of reading, science and problem solving Students • 2 hours on paper-and-pencil tasks (subset of all questions) • ½ hour for questionnaire on background, learning habits, learning environment, engagement and motivation School principals • Questionnaire (school demography, learning environment) Parents (optional) Coverage • PISA covers roughly nine tens of the world economy • Representative probability samples of between 3,500 and 50,000 15-year-old students

9. PISA countries in 2003 2000 2001 2006 2009 1998

10. Deciding what to assess... looking back at what students were expected to have learned …or… looking ahead to what they can do with what they have learned. For PISA, the OECD countries chose the latter.

11. Where we are - and where we can be What PISA shows students can do Examples of the best performing countries

12. High mathematics performance Average performanceof 15-year-olds in mathematics Low mathematics performance

13. Mathematical literacy in PISA The real world The mathematical World Making the problem amenable to mathematical treatment • The educators’ challenge • The skills that are easiest to teach and test are also the skills that are easiest to digitise, automatise and offshore • Teaching and evaluating skills in a context of real-world complexity • “expert thinking” – the ability to structure a problem • “complex communication” – the ability to convey a particular interpretation of information A mathematical model A model of reality Understanding, structuring and simplifying the situation Using relevant mathematical tools to solve the problem A real situation Validating the results Mathematical results Real results Interpreting the mathematical results

14. Increased likelihood of postsec. particip. at age 19 associated with reading proficiency at age 15 (Canada)after accounting for school engagement, gender, mother tongue, place of residence, parental, education and family income (reference group Level 1)

15. Odds ratios of high school graduation associated with reading prof, at 15 (Ref. PISA Level 5) (Canada),after accounting for school engagement, gender, mother tongue, place of residence, parental, education and family income

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

17. High mathematics performance Durchschnittliche Schülerleistungen im Bereich Mathematik High average performance Large socio-economic disparities High average performance High social equity Strong socio-economic impact on student performance Socially equitable distribution of learning opportunities Low average performance Large socio-economic disparities Low average performance High social equity Low mathematics performance

18. School performance and schools’ socio-economic background - Germany Student performance PISA Index of social background Disadvantage Advantage Student performance and student SES within schools School performance and school SES School proportional to size Figure 4.13

19. Student performance PISA Index of social background Disadvantage Advantage School performance and schools’ socio-economic background – United States OECD Student performance and student SES within schools OECD School performance and school SES School proportional to size Figure 4.13

20. School performance and schools’ socio-economic background - Finland Student performance and student SES Student performance and student SES within schools School performance and school SES School proportional to size Student performance PISA Index of social background Disadvantage Advantage Figure 4.13

21. Student performance PISA Index of social background Disadvantage Advantage Universal policiesUnited States OECD Student performance and student SES within schools OECD School performance and school SES School proportional to size Figure 4.13

22. Student performance PISA Index of social background Disadvantage Advantage Socio-economically targeted policies United States School proportional to size Figure 4.13

23. Student performance PISA Index of social background Disadvantage Advantage Socio-economically targeted policiesFinland School proportional to size Figure 4.13

24. Student performance PISA Index of social background Disadvantage Advantage Socio-economically targeted policiesGermany Student performance and student SES within schools School performance and school SES School proportional to size Figure 4.13

25. Student performance PISA Index of social background Disadvantage Advantage Performance based policiesUnited States OECD Student performance and student SES within schools OECD School performance and school SES School proportional to size Figure 4.13

26. Performance based policiesFinland Student performance and student SES Student performance and student SES within schools School performance and school SES School proportional to size Student performance PISA Index of social background Disadvantage Advantage Figure 4.13

27. Student performance PISA Index of social background Disadvantage Advantage Performance based policiesItaly OECD Student performance and student SES within schools OECD School performance and school SES OECD Student performance and student SES School proportional to size Figure 4.13

28. Student performance and migration Native students OECD average = 500 Second-generation students First-generation students Where immigrant students succeed – A comparative review of performance and engagement in PISA 2003: Figure 2.2a.

29. Variation in student performance 20 OECD (2004), Learning for tomorrow’s world: First results from PISA 2003, Table 4.1a, p.383.

30. In other countries, large performance differences among schools persist • In Austria, Belgium, Germany, Hungary, Italy, Japan, the Netherlands and Turkey, most of the performance variation among schools lies between schools… … and in some of these countries, most notably those that are highly stratified, a large part of that variation is explained by socio-economic inequalities in learning opportunities Variation in student performance In some countries, parents can rely on high and consistent standards across schools • In Canada, Denmark, Finland, Iceland and Sweden average student performance is high… … and largely unrelated to the individual schools in which students are enrolled. Variation of performance within schools Variation of performance between schools OECD (2004), Learning for tomorrow’s world: First results from PISA 2003, Table 4.1a, p.383.

31. How can we get there? Levers for policy that emerge from linking PISA results with system-level data

32. 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

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

34. 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

35. Involvement of stakeholders in decision-making at school Staffing OECD (2004), Learning for tomorrow’s world: First results from PISA 2003, Table 5.12, pp. 428-429.

36. School autonomy and external examsPooled international data PISA math performance Woessmann, 2005

37. School autonomy and external examsPooled international data PISA math performance Woessmann, 2005

38. High mathematics performance Durchschnittliche Schülerleistungen im Bereich Mathematik High average performance Large socio-economic disparities High average performance High social equity Strong socio-economic impact on student performance Socially equitable distribution of learning opportunities Low average performance Large socio-economic disparities Low average performance High social equity Low mathematics performance

39. High mathematics performance Durchschnittliche Schülerleistungen im Bereich Mathematik Strong socio-economic impact on student performance Socially equitable distribution of learning opportunities • School with responsibility for deciding which courses are offered • High degree of autonomy • Low degree of autonomy Low mathematics performance

40. Strong ambitions Devolvedresponsibility,the school as the centre of action Integrated educational opportunities Accountability Individualisedlearning Access to best practice and quality professional development

41. High mathematics performance Durchschnittliche Schülerleistungen im Bereich Mathematik Strong socio-economic impact on student performance Socially equitable distribution of learning opportunities • Early selection and institutional differentiation • High degree of stratification • Low degree of stratification Low mathematics performance

42. 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

43. Creating a knowledge-rich profession in which schools and teachers have the authority to act, the necessary knowledge to do so wisely, and access to effective support systems The future of education systems needs to be “knowledge rich” Informed professional judgement, the teacher as a “knowledge worker” Informed prescription National prescription Professional judgement Uninformed prescription, teachers implement curricula Uninformed professional judgement, teachers working in isolation The tradition of education systems has been “knowledge poor”

44. Further information • 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