Effective Policy for Teaching, Testing, Talent and Technology
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The Programme for International Student Assessment (PISA) is a triennial international survey which aims to evaluate education systems worldwide by testing the skills and knowledge of 15-year-old students. In 2015 over half a million students, representing 28 million 15-year-olds in 72 countries and economies, took the internationally agreed two-hour test. Students were assessed in science, mathematics, reading, collaborative problem solving and financial literacy. The results of the 2015 assessment were published on 6th December 2016.
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Effective Policy for Teaching, Testing, Talent and Technology
- 1. Effective policy for teaching, testing, talent and technology Andreas Schleicher Director for Education and Skills
- 2. Trends in science performance (PISA) 2006 2009 2012 2015 OECD 450 470 490 510 530 550 570 OECD average Studentperformance
- 3. Trends in science performance (PISA) 450 470 490 510 530 550 570 2006 2009 2012 2015 OECD average
- 4. Poverty is not destiny - Science performance by international deciles of the PISA index of economic, social and cultural status (ESCS) 280 330 380 430 480 530 580 630 DominicanRepublic40 Algeria52 Kosovo10 Qatar3 FYROM13 Tunisia39 Montenegro11 Jordan21 UnitedArabEmirates3 Georgia19 Lebanon27 Indonesia74 Mexico53 Peru50 CostaRica38 Brazil43 Turkey59 Moldova28 Thailand55 Colombia43 Iceland1 TrinidadandTobago14 Romania20 Israel6 Bulgaria13 Greece13 Russia5 Uruguay39 Chile27 Latvia25 Lithuania12 SlovakRepublic8 Italy15 Norway1 Spain31 Hungary16 Croatia10 Denmark3 OECDaverage12 Sweden3 Malta13 UnitedStates11 Macao(China)22 Ireland5 Austria5 Portugal28 Luxembourg14 HongKong(China)26 CzechRepublic9 Poland16 Australia4 UnitedKingdom5 Canada2 France9 Korea6 NewZealand5 Switzerland8 Netherlands4 Slovenia5 Belgium7 Finland2 Estonia5 VietNam76 Germany7 Japan8 ChineseTaipei12 B-S-J-G(China)52 Singapore11 Scorepoints Bottom decile Second decile Middle decile Ninth decile Top decile Figure I.6.7 % of students in the bottom international deciles of ESCS OECD median student
- 5. Students expecting a career in science Figure I.3.2 0 5 10 15 20 25 30 35 40 45 50 DominicanRep.12 CostaRica11 Jordan6 UnitedArabEm.11 Mexico6 Colombia8 Lebanon15 Brazil19 Peru7 Qatar19 UnitedStates13 Chile18 Tunisia19 Canada21 Slovenia16 Turkey6 Australia15 UnitedKingdom17 Malaysia4 Kazakhstan14 Spain11 Norway21 Uruguay17 Singapore14 TrinidadandT.13 Israel25 CABA(Arg.)19 Portugal18 Bulgaria25 Ireland13 Kosovo7 Algeria12 Malta11 Greece12 NewZealand24 Albania29 Estonia15 OECDaverage19 Belgium16 Croatia17 FYROM20 Lithuania21 Iceland22 Russia19 HKG(China)20 Romania20 Italy17 Austria23 Moldova7 Latvia19 Montenegro18 France21 Luxembourg18 Poland13 Macao(China)10 ChineseTaipei21 Sweden21 Thailand27 VietNam13 Switzerland22 Korea7 Hungary22 SlovakRepublic24 Japan18 Finland24 Georgia27 CzechRepublic22 B-S-J-G(China)31 Netherlands19 Germany33 Indonesia19 Denmark48 % Percentage of students who expect to work in science-related professional and technical occupations when they are 30 Science-related technicians and associate professionals Information and communication technology professionals Health professionals Science and engineering professionals %ofstudentswithvag ueormissingexpectati ons
- 6. 0 10 20 30 40 50 300 400 500 600 700 Percentageofstudentsexpectinga careerinscience Score points in science Low enjoyment of science High enjoyment of science Students expecting a career in science by performance and enjoyment of learning Figure I.3.17
- 7. Singapore Canada Slovenia Australia United Kingdom Ireland Portugal Chinese Taipei Hong Kong (China) New Zealand Denmark Japan Estonia Finland Macao (China) Viet Nam B-S-J-G (China) Korea Germany Netherlands Switzerland Belgium Poland Sweden Lithuania Croatia Iceland Georgia Malta United States Spain Israel United Arab Emirates Brazil Bulgaria Chile Colombia Costa Rica Dominican Republic Jordan Kosovo Lebanon Mexico Peru Qatar Trinidad and Tobago Tunisia Turkey Uruguay Above-average science performance Stronger than average beliefs in science Above-average percentage of students expecting to work in a science-related occupation Norway Multipleoutcomes
- 8. 8 Looking forward to… Better anticipate the evolution of the demand for 21st century skills and better integrate the world of work and learning Leverage the potential of all learners Find more innovative solutions to what we learn, how we learn, when we learn and where we learn Advance from an industrial towards a professional work organisation Building learning systems that…
- 9. The kind of things that are easy to teach are now easy to automate, digitize or outsource 35 40 45 50 55 60 65 70 1960 1970 1980 1990 2000 2006 2009 Routine manual Nonroutine manual Routine cognitive Nonroutine analytic Nonroutine interpersonal Mean task input in percentiles of 1960 task
- 10. Robotics The Auto-auto >1m km, one minor accident, occasional human intervention
- 12. A lot more to come • 3D printing • Synthetic biology • Brain enhancements • Nanomaterials • Etc.
- 13. Education in the past
- 14. Education now
- 15. What teachers say and what teachers do
- 16. What knowledge, skills and character qualities do successful teachers require? 96% of teachers: My role as a teacher is to facilitate students own inquiry
- 17. What knowledge, skills and character qualities do successful teachers require? 86%: Students learn best by findings solutions on their own
- 18. What knowledge, skills and character qualities do successful teachers require? 74%: Thinking and reasoning is more important than curriculum content
- 19. Prevalence of memorisation rehearsal, routine exercises, drill and practice and/or repetition -2.00 -1.50 -1.00 -0.50 0.00 0.00 0.50 1.00 1.50 2.00 Switzerland Poland Germany Japan Korea France Sweden Shanghai-China Canada Singapore United States Norway Spain Netherlands United Kingdom Prevalence of elaboration reasoning, deep learning, intrinsic motivation, critical thinking, creativity, non-routine problems High Low Low High
- 20. Learning time and science performance Figure II.6.23 Finland Germany Switzerland Japan Estonia Sweden Netherlands New Zealand Macao (China) Iceland Hong Kong (China) Chinese Taipei Uruguay Singapore Poland United States Israel Bulgaria Korea Russia Italy Greece B-S-J-G (China) Colombia Chile Mexico Brazil Costa Rica Turkey Montenegro Peru Qatar Thailand United Arab Emirates Tunisia Dominican Republic R² = 0.21 300 350 400 450 500 550 600 35 40 45 50 55 60 PISAsciencescore Total learning time in and outside of school OECD average OECD average OECDaverage
- 21. Learning time and science performance Figure II.6.23 6 7 8 9 10 11 12 13 14 15 16 0 10 20 30 40 50 60 70 Finland Germany Switzerland Japan Estonia Sweden Netherlands NewZealand Australia CzechRepublic Macao(China) UnitedKingdom Canada Belgium France Norway Slovenia Iceland Luxembourg Ireland Latvia HongKong(China) OECDaverage ChineseTaipei Austria Portugal Uruguay Lithuania Singapore Denmark Hungary Poland SlovakRepublic Spain Croatia UnitedStates Israel Bulgaria Korea Russia Italy Greece B-S-J-G(China) Colombia Chile Mexico Brazil CostaRica Turkey Montenegro Peru Qatar Thailand UnitedArabEmirates Tunisia DominicanRepublic Scorepointsinscienceperhouroftotallearningtime Hours Intended learning time at school (hours) Study time after school (hours) Score points in science per hour of total learning time
- 22. 23 Teachers’ skills Numeracy test scores of tertiary graduates and teachers Numeracy score215 235 255 275 295 315 335 355 375 Spain Poland Estonia United States Canada Ireland Korea England (UK) England/N. Ireland (UK) Denmark Northern Ireland (UK) France Australia Sweden Czech Republic Austria Netherlands Norway Germany Flanders (Belgium) Finland Japan Numeracy score Numeracy skills of middle half of college graduates
- 23. 24 Teachers’ skills Numeracy test scores of tertiary graduates and teachers Numeracy score215 235 255 275 295 315 335 355 375 Spain Poland Estonia United States Canada Ireland Korea England (UK) England/N. Ireland (UK) Denmark Northern Ireland (UK) France Australia Sweden Czech Republic Austria Netherlands Norway Germany Flanders (Belgium) Finland Japan Numeracy score Numeracy skills of teachers
- 24. 25 Professional knowledge and expertise in teaching Behaviour Cognition Content Character • Effectiveness is evidenced by teacher behaviour and student learning outcomes • Teachers as thoughtful, sentient beings, characterised by intentions, strategies, decisions and reflections • The nature and adequacy of teacher knowledge of the substance of the curriculum being taught • The teachers serve as moral agents, deploying a moral-pedagogical craft Teacher knowledge of, and sensitivity to, cultural, social and political contexts and the environments of their students.
- 25. External forces exerting pressure and influence inward on an occupation Internal motivation and efforts of the members of the profession itself 26 Professionalism Professionalism is the level of autonomy and internal regulation exercised by members of an occupation in providing services to society
- 26. Policy levers to teacher professionalism Knowledge base for teaching (initial education and incentives for professional development) Autonomy: Teachers’ decision- making power over their work (teaching content, course offerings, discipline practices) Peer networks: Opportunities for exchange and support needed to maintain high standards of teaching (participation in induction, mentoring, networks, feedback from direct observations) Teacher professionalism
- 27. Teacher professionalism Knowledge base for teaching (initial education and incentives for professional development) Autonomy: Teachers’ decision- making power over their work (teaching content, course offerings, discipline practices) Peer networks: Opportunities for exchange and support needed to maintain high standards of teaching (participation in induction, mentoring, networks, feedback from direct observations)
- 28. High Peer Networks/ Low Autonomy High Autonomy Knowledge Emphasis Balanced Domains/ High Professionalism Balanced Domains/ Low Professionalism Teacher professionalism
- 29. 0 1 2 3 4 5 6 7 8 9 10 Spain Japan France Brazil Finland Flanders Norway Alberta(Canada) Australia Denmark Israel Korea UnitedStates CzechRepublic Shanghai(China) Latvia Netherlands Poland England NewZealand Singapore Estonia Networks Autonomy Knowledge Mean mathematics performance, by school location, after accounting for socio-economic status Fig II.3.3 3030 TALIS Teacher professionalism index
- 30. 0 10 20 30 40 50 60 70 80 90 100 Discussindividual students Shareresources Teamconferences Collaboratefor common standards Teamteaching CollaborativePD Jointactivities Classroom observations Percentageofteachers Average Shanghai (China) Professional collaboration Percentage of lower secondary teachers who report doing the following activities at least once per month Teacher co-operation Informal exchange
- 31. Teachers Self-Efficacy and Professional Collaboration 11.40 11.60 11.80 12.00 12.20 12.40 12.60 12.80 13.00 13.20 13.40 Never Onceayearorless 2-4timesayear 5-10timesayear 1-3timesamonth Onceaweekormore Teacherself-efficacy(level) Teach jointly as a team in the same class Observe other teachers’ classes and provide feedback Engage in joint activities across different classes Take part in collaborative professional learning Less frequently More frequently
- 32. Technology can amplify innovative teaching • As tools for inquiry- based pedagogies with learners as active participants • Make it faster and more granular • Collaborative platforms for teachers to share and enrich teaching materials • Well beyond textbooks, in multiple formats, with little time and space constraints Expand access to content Collaboration for knowledge creation Support new pedagogies Feedback
- 33. 450 460 470 480 490 500 510 520 -2 -1 0 1 2 Scorepoints Technology in schools and digital skills still don’t square Source: Figure 6.5 Relationship between students’ skills in reading and computer use at school (average across OECD countries) OECD average Digital reading skills of 15-year- olds Intensive technology useNo technology use
- 34. Routine cognitive skills Conceptual understanding, complex ways of thinking, ways of working Some students learn at high levels All students need to learn at high levels Student inclusion Curriculum, instruction and assessment Standardisation and compliance High-level professional knowledge workers Teacher quality ‘Tayloristic’, hierarchical Flat, collegial Work organisation Primarily to authorities Primarily to peers and stakeholders Accountability What it all means The old bureaucratic system The modern enabling system
- 35. 3838Lessonsfromhighperformers 38 38 Thank you Find out more about our work at www.oecd.org/edu – All publications – The complete micro-level database Email: Andreas.Schleicher@OECD.org Twitter: SchleicherOECD and remember: