1. Responsive Education Systems and Skills for the Knowledge Economy Using education as a lever to compete by working smarter, rather than working harder or cheaper Organisation for Economic Cooperation and Development (OECD) Knowledge economy forum Andreas Schleicher Head, Indicators and Analysis Division OECD Directorate for Education

2. „The world is flat“ (Thomas Friedman) Key competencies for tomorrow’s world

3. r The personal computer enabled millions of individuals to become authors of their own content in digital form r The spread of the Internet and the emergence of the World Wide Web enabled more people than ever to be connected and to share their knowledge r The emergence of software standards meant that people were able to seamlessly work together and upload and globalise content

4. Economy-wide measures of routine and non-routine task input (Levy and Murnane, 2007)

5. Delivering high level skills. Quantity - A world of change.

6. Baseline qualifications – A world of change Approximated by the percentage of persons with ISCED 3 qualfication born in the period shown below (2004) 24 1 23 1 11 14 A1.2a

7. Growth in university-level qualifications Approximated by the percentage of persons with ISCED 5A/6 qualfication born in the period shown below (2004) 12 22 3 20 A1.3a +2.9 +3.5 +3.7

8. The returns on high level qualifications Private internal rates of return (RoR) for an individual obtaining a university- level degree (ISCED 5/6) from an upper secondary and post-secondary non- tertiary level of education (ISCED 3/4), MALES

9. r Rising tertiary level qualifications seem generally not to have led to an “inflation” of the labour-market value of qualifications. In all but three of the 20 countries with available data, the earnings benefit increased between 1997 and 2003, in Germany, Italy and Hungary by between 20% and 40% (UK 9%). Growing benefits in many of the countries with the steepest attainment growth

10. The earnings advantage of education Relative earnings of 25-64-year-olds with income from employment (upper secondary education=100) A9.1a

11. The earnings advantage of education Relative earnings of 25-64-year-olds with income from employment (upper secondary education=100) In the UK, females with a tertiary-Type A qualification earn, on average, twice as much as females who completed only upper secondary education (OECD average 161%). In the UK, males without upper secondary education earn 71% of those with it (OECD average 80%). r Rising tertiary level qualifications seem generally not to have led to an “inflation” of the labour-market value of qualifications. In all but three of the 20 countries with available data, the earnings benefit increased between 1997 and 2004, in Germany, Italy and Hungary by between 20% and 40% (UK 5%). A9.1a

12. Where do high skills pay? Distribution of 25-64-year-olds by level of earnings EU United States

13. The driving forces of GDP per capita growth Average annual percentage change (1990-2000)

14. Ireland, Korea, Mexico and Turkey were the only countries where demography made a significant positive impact on GDP per capita growth… While declines in employment rates reduced growth in others

15. The driving forces of GDP per capita growth Average annual percentage change (1990-2000) But in almost all countries, the biggest contribution came from increased labour productivity …in others it is beginning to act as a slight drag on growth While declines in employment rates reduced growth in others

16. Enhancements in human capital contribute to labour productivity growth Average annual percentage change (1990-2000)

17. Delivering high level skills. Quality – Getting the fundamentals right

18. Who will be “safe” from outsourcing, digitalisation and automatisation? r The great collaborators and orchestrators The more complex the globalised world becomes, the more individuals and companies need various forms of co-ordination and management r The great synthesisers Conventionally, our approach to problems was breaking them down into manageable bits and pieces, today we create value by synthesising disparate bits together r The great explainers The more content we can search and access, the more important the filters and explainers become

19. Who will be “safe” from outsourcing, digitalisation and automatisation? r The great versatilists Specialists generally have deep skills and narrow scope, giving them expertise that is recognised by peers but not valued outside their domain Generalists have broad scope but shallow skills Versatilists apply depth of skill to a progressively widening scope of situations and experiences, gaining new competencies, building relationships, and assuming new roles. They are capable not only of constantly adapting but also of constantly learning and growing r The great personalisers A revival of interpersonal skills, skills that have atrhophied to some degree because of the industrial age and the Internet r The great localisers Localising the global

20. Average performance of 15-year-olds in mathematics High mathematics performance Low mathematics performance

21. Mathematical literacy in PISA The real world The mathematical World A real situation A model of reality A mathematical model Mathematical results Real results

22. Mathematical literacy in PISA The real world The mathematical World 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

23. Mathematical literacy in PISA The real world The mathematical World r 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

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

26. OECD (2004), Learning for tomorrow’s world: First results from PISA 2003, Table 4.1a, p.383. 20 Consistency in quality standards Variation in the performance of 15-year-olds in mathematics

27. Variation of performance between schools Variation of performance within schools Consistency in quality standards Variation in the performance of 15-year-olds in mathematics OECD (2004), Learning for tomorrow’s world: First results from PISA 2003, Table 4.1a, p.383. 111 14 12 5

28. Using the potential. Equality in outcomes and equity in opportunities.

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

30. 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

31. 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 Nether lands Canada Switzerland Iceland Denmark France Sweden Belgium Australia Japan R 2 = 0.28 Cumulative expenditure (US$) Performance in mathematics

32. 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 r Spending per student is positively associated with average student performance… …but not a guarantee for high outcomes Australia, Belgium, Canada, the Czech Republic, Finland, Japan, Korea and the Netherlands do well in terms of “value for money”… …while some of the big spenders perform below-average

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

34. 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

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

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

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

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

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

40. 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 Accountability

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

42. Paradigm shifts Prescription Informed profession UniformityEmbracing diversity DemarcationCollaboration Provision Outcomes Bureaucratic – look upDevolved – look outwards Talk equityDeliver equity Hit & miss Universal high standards Received wisdomData and best practice The old bureaucratic education systemThe modern enabling education system

43. 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