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Schleicher
1. Seeing US education through the prism of international comparisons Organisation for Economic Cooperation and Development (OECD) Alliance for excellent education, Washington, 4 October 2007 Prof. Andreas Schleicher Head, Indicators and Analysis Division OECD Directorate for Education
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3. „ The world is flat“ (Thomas Friedman) How education became a key driver for the success of individuals and nations
4. 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, 45-44 und 25-34 years % 1. Excluding ISCED 3C short programmes 2. Year of reference 2004 3. Including some ISCED 3C short programmes 3. Year of reference 2003. 13 1 1 27
5. Consider Korea 1960s Beginning 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.
6. High school completion rates Percentage of graduates to the population at the typical age of graduation %
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8. The effects of the higher education expansion: A high calibre workforce or the overqualified crowding out the lesser qualified? Lower secondary unemployment rate as a ratio of upper secondary unemployment rate A1.4 In those countries that did not expand higher education (the bottom group), failure to complete high school is now associated with an 80% greater probability of being unemployed, compared to less than 50% in the top group. “ Top group” The nine countries that expanded tertiary education fastest in the 1990s (5.9% on average) “ Middle group” The eight countries with modest increases in tertiary education (2.4% on average) (UK) “ Bottom group” The nine countries with no or very modest increases in tertiary education (0.1% on average)
9. Changes in higher education and changes in unemployment for lower secondary educated adults: late 1990s and early 2000s Percentage point change within the periods Change in tertiary attainment levels between 1990-1994 and 1995-1999 A1.5 Countries in green had the fastest growth in tertiary attainment and close to zero or negative growth in unemployment. Countries in red had low or no growth in tertiary attainment but substantial growth in unemployment among the lower educated.
10. Relative unemployment rate of adults with tertiary level attainment between 1995 and 2004 Lower secondary unemployment rate as a ratio of upper secondary unemployment rate Years The extent to which a tertiary degree protects against unemployment risk has deteriorated slightly in the countries with the fastest rates for tertiary expansion, from 37% to 31%, which is less than the risk among those with only upper secondary education. However, the same rate of deterioration has also occurred among countries with the lowest expansion rates, and a faster deterioration occurred among the countries that expanded slowly in the 1990s. “ Middle group” The eight countries with modest increases in tertiary education (2.4% on average) “ Bottom group” The nine countries with no or very modest increases in tertiary education (0.1% on average) “ Top group” The nine countries that expanded tertiary education fastest in the 1990s (5.9% on average
15. Getting the fundamentals right. What the top-performers achieve in terms of quality, equity and efficiency in schooling outcomes
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17. 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
18. 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.
19. Average performance of 15-year-olds in mathematics – extrapolate and apply High mathematics performance Low mathematics performance
20. Math performance of immigrant students OECD average = 500 Native students Second-generation students First-generation students Where immigrant students succeed – A comparative review of performance and engagement in PISA 2003: Figure 2.2a.
21. Strengths and weaknesses in math The real world The mathematical World A real situation A model of reality A mathematical model Mathematical results Real results Understanding, structuring and simplifying the situation Making the problem amenable to mathematical treatment Interpreting the mathematical results Using relevant mathematical tools to solve the problem Validating the results
22. How the demand for skills has changed Economy-wide measures of routine and non-routine task input (US) (Levy and Murnane) Mean task input as percentiles of the 1960 task distribution
23. 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)
24. 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
25. Durchschnittliche Schülerleistungen im Bereich Mathematik 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 Watch out for new data on this on December 4 !
26. School performance and schools’ socio-economic background - Germany Figure 4.13 School proportional to size Student performance and student SES within schools School performance and school SES Student performance Advantage PISA Index of social background Disadvantage
27. School performance and schools’ socio-economic background – United States Figure 4.13 School proportional to size Student performance and student SES within schools School performance and school SES OECD OECD Student performance Advantage PISA Index of social background Disadvantage
28. Student performance School performance and schools’ socio-economic background - Finland Advantage PISA Index of social background Disadvantage Figure 4.13 Student performance and student SES Student performance and student SES within schools School performance and school SES School proportional to size
29. How can we get there? Levers for policy that emerge from OECD’s international comparisons
30. Money matters - but other things do too Mexico Greece Portugal Italy Spain Germany Austria Ireland United States Norway Korea Czech republic Slovak republic Poland Hungary Finland Netherlands Canada Switzerland Iceland Denmark France Sweden Belgium Australia Japan R 2 = 0.28 Cumulative expenditure (US$) Performance in mathematics
31. High ambitions and universal standards Defining what students should be able to do, not prescribing what teachers should teach Access to best practice and quality professional development
32. Challenge and support Weak support Strong support Low challenge High challenge Strong performance Systemic improvement Poor performance Improvements idiosyncratic Conflict Demoralisation Poor performance Stagnation
33. High ambitions Access to best practice and quality professional development Intelligent accountability and intervention in inverse proportion to success Devolved responsibility, the school as the centre of action
34. School autonomy and central exams Pooled international data Woessmann, 2005 PISA math performance
35. School autonomy and external exams Pooled international data Woessmann, 2005 PISA math performance
36. Public and private schools Private schools perform better Public schools perform better
37. Durchschnittliche Schülerleistungen im Bereich Mathematik 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
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39. Strong ambitions Access to best practice and quality professional development Accountability Devolved responsibility, the school as the centre of action Integrated educational opportunities From prescribed forms of teaching and assessment towards personalised learning
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41. High ambitions Access to best practice and quality professional development Accountability and intervention in inverse proportion to success Personalized learning Devolved responsibility, the school as the centre of action Integrated educational opportunities
42. 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 tradition of education systems has been “knowledge poor” (think of the assembly line in Detroit) The best performing education systems are “knowledge rich” (think of Silicon Valley) National prescription Professional judgement Informed professional judgement, the teacher as a “knowledge worker” Informed prescription Uninformed professional judgement, teachers working in isolation Uninformed prescription, teachers implement curricula
43. Paradigm shifts Prescription Informed profession Uniformity Embracing diversity Demarcation Collaboration Provision Outcomes Bureaucratic – look up Devolved – look outwards Talk equity Deliver equity Hit & miss Universal high standards Received wisdom Data and best practice The old bureaucratic education system The modern enabling education system
44. A second chance? Expected hours in non-formal job-related training (2003) This chart shows the expected number of hours in non-formal job-related education and training, over a forty year period, for 25-to-64 year olds. % C5.1a