Students, Computers and Learning: Making the Connection (Andreas Schleicher, (Director, OECD Directorate for Education and Skills)

1. STUDENTS, COMPUTERS AND LEARNING: MAKING THE CONNECTION September 2015 Andreas Schleicher Director for Education and Skills

2. The kind of things that are easy to teach are now easy to automate, digitize or outsource

3. Robotics

4. Google Autonomous Vehicle >1m km, one minor accident, occasional human intervention

5. Augmented Reality

6. A lot more to come • 3D printing • Synthetic biology • Brain enhancements • Nanomaterials • Etc.

7. The Race between Technology and Education Inspired by “The race between technology and education” Pr. Goldin & Katz (Harvard) Industrial revolution Digital revolution Social pain Universal public schooling Technology Education Prosperity Social pain Prosperity

8. Digital skills of 15-year-olds

9. Singapore Korea Hong Kong-China Japan CanadaShangai-China EstoniaIreland Australia Chinese TapeiMacao-China France United States ItalyBelgium NorwaySweden Denmark Portugal Austria Poland Slovak Republic Slovenia Spain Russian Federation Israel Chile Hungary Brazil United Arab Emirates Colombia 390 400 410 420 430 440 450 460 470 480 490 500 510 520 530 540 550 560 570 Mean score Strong performance in in digital reading Low performance in digital reading 18 Average performance in digital reading Fig 3.1

10. Countries doing better/worse in digital literacy than in print reading? -60 -50 -40 -30 -20 -10 0 10 20 30 40 Singapore Korea Japan HongKong-China Italy Canada UnitedStates Sweden Australia Estonia Macao-China France Brazil SlovakRepublic Ireland ChineseTaipei Chile OECDaverage Denmark Norway Belgium Portugal Austria Slovenia RussianFederation Spain Shanghai-China Colombia Israel Poland Hungary UnitedArabEmirates Students' performance in digital reading is higher than their expected performance Students' performance in digital reading is lower than their expected performance Source: Figure 3.7 Score-point difference Performance that would be expected based solely on print- reading

11. Think, then click: Task-oriented browsing Average rank of students in the international comparison of students taking the same test form 25 30 35 40 45 50 55 60 65 70 75 Singapore Australia Korea Canada UnitedStates Ireland HongKong-China France Japan Belgium Portugal OECDaverage Denmark Sweden Macao-China Estonia Norway Shanghai-China Italy ChineseTaipei Austria Poland Israel Slovenia Spain Chile SlovakRepublic Hungary RussianFederation UnitedArabEmirates Brazil Colombia Percentile rank Source: Figure 4.7 The index of task-oriented browsing varies from 0 to 100. High values on this index reflect long navigation sequences that contain a high number of task-relevant steps and few or no missteps or task-irrelevant steps.

12. Classification of students based on the quality of their browsing activity 0 10 20 30 40 50 60 70 80 90 100 Singapore10 Korea15 HongKong-China19 Australia8 Canada8 UnitedStates10 Ireland9 Japan16 Macao-China23 Shanghai-China23 France10 ChineseTaipei23 OECDaverage12 Belgium11 Italy15 Sweden9 Norway11 Estonia13 Portugal10 Israel11 Austria14 Denmark11 Poland9 Slovenia11 Chile14 Spain13 SlovakRepublic16 Hungary13 RussianFederation18 UnitedArabEmirates14 Brazil11 Colombia17 Mostly unfocused browsing activity No browsing activity Insufficient or mixed browsing activity Highly focused browsing activity % Source: Figure 4.8 Percentage of students whose Internet browsing is mostly unfocused Mostly unfocused browsing activity: students for whom the sum of navigation missteps and task-irrelevant steps is higher than the number of task-relevant steps No browsing activity: no navigation steps recorded in log files Insufficient or mixed browsing activity: the sum of navigation missteps and task-irrelevant steps is equal to the number of task-relevant steps or lower, and the index of task-relevant browsing is equal to 75 or lower Highly focused browsing activity: index of task-relevant browsing higher than 75

13. Explained variation in the digital reading performance of countries and economies Variation in digital reading performance explained by print- reading performance Residual variation explained by the quantity of navigation steps (overall browsing activity) Residual variation uniquely explained by the quality of navigation (task-oriented browsing) Unexplained variation 80.4 % 10.4 % 4.4 % 4.9 % Source: Figure 4.9

14. Relationship between digital reading performance and navigation behaviour Australia Austria Belgium Canada Chile Denmark Estonia France Hungary Ireland Israel Italy Japan Korea Norway Poland Portugal Slovak Republic Slovenia Spain Sweden United StatesBrazil Colombia Hong Kong-China Macao-China Russian Federation Shanghai-China Singapore Chinese Taipei United Arab Emirates -60 -50 -40 -30 -20 -10 0 10 20 30 40 30 35 40 45 50 55 60 65 70 Relativeperformanceindigitalreading,after accountingforperformanceinprintreading Index of task-oriented browsing OECD average OECD average R² = 0.50 Source: Figure 4.10 Percentile rank

15. Strong performance in in computer-based assessment of mathematics Low performance in computer-based assessment of mathematics 26 Average performance in computer-based assesment of mathematics Fig 3.10 Singapore Shangai-China Korea Hong-Kong Macao-China JapanChinese-Tapei Canada Estonia BelgiumFranceAustralia Austria ItalyNorwayUnited States Slovak RepublicDenmark Ireland SwedenPoland Russian Federation Portugal Slovenia Spain Hungary Israel United Arab EmiratesChile Brazil Colombia 390 400 410 420 430 440 450 460 470 480 490 500 510 520 530 540 550 560 570 Mean score

16. Relative success on mathematics tasks that require the use of computers to solve problems Compared to the OECD average 0.80 0.85 0.90 0.95 1.00 1.05 1.10 1.15 1.20 UnitedArabEmirates Canada UnitedStates Japan Macao-China Brazil Slovenia Austria RussianFederation ChineseTaipei SlovakRepublic Australia Israel Portugal Korea Shanghai-China Norway Hungary HongKong-China Singapore OECDaverage Sweden Denmark Estonia Belgium Colombia Italy Spain Poland Ireland Chile France Better-than-expected performance on tasks that do not require the use of computers to solve mathematics problems Better-than-expected performance on tasks that require the use of computers to solve mathematics problems Odds ratio (OECD average = 1.00) Source: Figure 3.13

17. Students’ use of computers

18. Access to computers at home 0 10 20 30 40 50 60 70 80 90 100 Denmark Norway Sweden Iceland Netherlands Australia Liechtenstein Qatar Switzerland Luxembourg Finland Belgium UnitedArabEmirates Germany Canada UnitedKingdom Singapore Austria France Israel Slovenia OECDaverage NewZealand Spain UnitedStates Estonia CzechRepublic Portugal Ireland HongKong-China ChineseTaipei Italy SlovakRepublic Macao-China Hungary Poland Chile Uruguay Latvia Argentina Greece Shanghai-China Japan Bulgaria Lithuania Croatia Malaysia CostaRica Jordan Serbia RussianFederation Montenegro Korea Brazil Mexico Romania Peru Thailand Colombia Tunisia Turkey Albania Kazakhstan VietNam Indonesia At least one computer 3 or more computers Source: Figure 1.1 %

19. Access to computers at home: Change between 2009 and 2012 0 10 20 30 40 50 60 70 80 90 100 Denmark Norway1 Sweden Iceland1 Netherlands1 Australia1 Liechtenstein1 Qatar Switzerland Luxembourg1 Finland Belgium UnitedArabEmirates Germany Canada1 UnitedKingdom1 Singapore1 Austria France Israel Slovenia OECDaverage NewZealand1 Spain UnitedStates1 Estonia CzechRepublic Portugal Ireland HongKong-China ChineseTaipei Italy SlovakRepublic Macao-China Hungary Poland Chile Uruguay Latvia Argentina Greece Shanghai-China Japan Bulgaria Lithuania Croatia Malaysia CostaRica Jordan Serbia RussianFederation Montenegro Korea1 Brazil Mexico Romania Peru Thailand Colombia Tunisia Turkey Albania Kazakhstan VietNam Indonesia1,2 PISA 2009 - At least one computer PISA 2012 - At least one computer PISA 2009 - 3 or more computers PISA 2012 - 3 or more computers Source: Figure 1.1 % Note: The share of students with at least one computer at home (1) or with 3 or more computers at home (2) is not significantly different in 2009 and 2012.

20. Bridging the social divide

21. Access to the Internet at home and students' socio-economic status 0 10 20 30 40 50 60 70 80 90 100 Denmark Iceland Finland HongKong-China Netherlands Norway Switzerland Sweden Slovenia Estonia Austria UnitedKingdom Germany Macao-China Liechtenstein1 France Luxembourg Belgium Ireland Canada Korea Australia Italy CzechRepublic Singapore ChineseTaipei Croatia Portugal Spain Poland OECDaverage UnitedArabEmirates Qatar Lithuania Israel Hungary NewZealand UnitedStates RussianFederation Bulgaria Latvia SlovakRepublic Japan Serbia Greece Montenegro Shanghai-China Uruguay Romania Brazil Argentina Chile CostaRica Jordan Malaysia Turkey Kazakhstan Colombia Tunisia Thailand Peru Mexico Indonesia VietNam Top quarter Third quarter Second quarter Bottom quarter The PISA index of economic, social and cultural status (ESCS) Source: Figure 5.2 % 1. The difference between the top and the bottom quarter of ESCS is not statistically significant.

22. Early exposure to computers % of students who first used a computer when they were 6 years or younger 0 10 20 30 40 50 60 70 Denmark Sweden Norway Finland Iceland Australia NewZealand Israel Estonia Slovenia OECDaverage HongKong-China Ireland Spain Belgium Poland Singapore CzechRepublic Italy Chile Hungary Austria Switzerland Germany Jordan Serbia Latvia Croatia Liechtenstein Macao-China Uruguay Portugal CostaRica Korea SlovakRepublic ChineseTaipei RussianFederation Japan Greece Turkey Shanghai-China Mexico Top quarter Third quarter Second quarter Bottom quarter The PISA index of economic, social and cultural status (ESCS) Source: Figure 5.4 %

23. Early exposure to computers, by gender % of students who first used a computer when they were 6 years or younger 0 10 20 30 40 50 60 70 Denmark Sweden Israel Norway NewZealand1 Finland Australia Iceland Estonia HongKong-China1 Ireland Singapore Spain Poland OECDaverage Slovenia CostaRica1 Chile Jordan Uruguay Belgium Serbia Croatia Macao-China Portugal Italy Hungary Latvia Austria CzechRepublic Switzerland Germany Korea ChineseTaipei Liechtenstein Japan1 RussianFederation Shanghai-China Mexico Turkey SlovakRepublic Greece Boys Girls Source: Figure 5.5 % 1. The difference between boys and girls is not statistically significant.

24. Percentage of students with access to the Internet at school, but not at home 0 10 20 30 40 50 60 Mexico Turkey Jordan CostaRica Chile Uruguay Greece Shanghai-China Japan NewZealand Serbia Latvia RussianFederation OECDaverage Hungary SlovakRepublic Spain Portugal Poland ChineseTaipei Croatia Australia Singapore Korea Italy Ireland Israel CzechRepublic Macao-China Belgium Estonia Germany Austria Switzerland Liechtenstein1 HongKong-China Slovenia Sweden Norway Denmark Finland Iceland Netherlands1 All students Socio-economically disadvantaged students Socio-economically advantaged students Source: Figure 5.7 % 1. The difference between socio-economically advantaged and disadvantaged students is not statistically significant.

25. Time online

26. Time spent on line in school and outside of school 0 20 40 60 80 100 120 140 160 180 200 Macao-China45 Denmark44 Sweden44 Estonia41 Norway41 HongKong-China39 RussianFederation39 Iceland37 Australia38 Poland36 Hungary37 CzechRepublic36 ChineseTaipei36 Netherlands34 SlovakRepublic35 Singapore35 Spain33 Portugal35 Chile36 Latvia34 Germany32 Uruguay34 Croatia32 Belgium30 Greece31 Slovenia29 OECDaverage30 Serbia30 Israel30 Liechtenstein31 Finland20 NewZealand27 Switzerland24 Austria24 CostaRica25 Japan23 Jordan25 Shanghai-China20 Ireland18 Italy17 Korea14 Mexico18 Turkey13 During weekdays, outside of school During weekdays, at school During weekend days, outside of school Minutes per day Source: Figure 1.5 Percentage of students spending at least 4 hours on line, during weekend days

27. Feeling lonely at school, by time spent on the Internet outside of school during weekdays 0 5 10 15 20 25 30 35 Shanghai-China Jordan Macao-China Singapore Turkey Uruguay HongKong-China NewZealand Finland Korea1 SlovakRepublic Greece Australia Hungary Iceland Japan Norway Ireland Latvia Mexico OECDaverage Sweden Serbia ChineseTaipei Poland Estonia Belgium Denmark Portugal Slovenia CostaRica1 CzechRepublic1 RussianFederation Chile Netherlands Austria Italy Israel1 Spain Croatia1 Germany1 Switzerland Liechtenstein Low Internet users: one hour or less Moderate Internet users : 1 to 2 hours High Internet users: 2 to 6 hours Extreme Internet users: more than 6 hours % of students who agree with the statement « I feel lonely at school » Source: Figure 1.8 1. The difference between moderate and extreme Internet users is not statistically significant.

28. Technology in teaching and learning

29. Number of students per school computer 0 1 2 3 4 5 6 7 8 9 10 Australia NewZealand Macao-China UnitedKingdom CzechRepublic Norway UnitedStates Lithuania SlovakRepublic Singapore Liechtenstein Estonia HongKong-China Spain Luxembourg Hungary Latvia Denmark Kazakhstan Ireland Bulgaria Netherlands Switzerland Belgium Canada France Shanghai-China Austria RussianFederation Thailand Finland Slovenia Japan Colombia Sweden Portugal Poland Iceland Italy Qatar UnitedArabEmirates Germany Romania OECDaverage Israel Chile Jordan Croatia Korea ChineseTaipei Montenegro Peru Greece VietNam Uruguay Serbia Albania Argentina Mexico Indonesia Malaysia CostaRica Brazil Turkey Tunisia Magnified Students per computer Source: Figure 2.14

30. Use of ICT at school % of students who reported engaging in each activity at least once a week Shanghai- China Japan Japan Shanghai- China Japan Japan Japan Korea Korea Australia Denmark Australia Liechtenstein Denmark Denmark Norway Norway Jordan 0 10 20 30 40 50 60 70 80 90 100 Browse the Internet for schoolwork Use school computers for group work and communication with other students Do individual homework on a school computer Use e-mail at school Download, upload or browse material from the school's website Chat on line at school Practice and drilling, such as for foreign- language learning or mathematics Post work on the school's website Play simulations at school OECD average Top country/economy Bottom country/economy Source: Figure 2.1 %

31. Index of ICT use at school -1.50 -1.00 -0.50 0.00 0.50 1.00 Denmark Norway Australia Netherlands CzechRepublic Liechtenstein Sweden NewZealand SlovakRepublic Greece Spain Jordan Chile Finland Austria Slovenia Mexico OECDaverage Switzerland Portugal Uruguay Macao-China Hungary Italy Croatia Singapore Iceland CostaRica Israel Belgium Estonia ChineseTaipei HongKong-China Serbia Latvia RussianFederation Germany Turkey Ireland Poland Shanghai-China Japan Korea Source: Figure 2.3 Mean index

32. Computer use and learning outcomes

33. Trends in mathematics performance and increase in computers in schools Australia Austria Belgium Canada Czech Republic Denmark Finland France GermanyGreece Hungary Iceland Ireland Italy Japan Korea Luxembourg Mexico Netherlands New Zealand Norway Poland Portugal Slovak Republic Spain Sweden Switzerland Turkey United States Brazil Hong Kong-China Indonesia Latvia Macao-China Russian Federation Thailand Tunisia Uruguay R² = 0.27 -40 -30 -20 -10 0 10 20 30 40 Differenceinmathematicsperformance (PISA2012-PISA2003) Number of computers per student, after accounting for per capita GDP All countries and economies Fewer computers More computersFewer computers More computers Expected number of computers per student, based on per capita GDP Source: Figure 6.3

34. Students who use computers at school only moderately score the highest in reading 450 460 470 480 490 500 510 520 -2 -1 0 1 2 Scorepoints Index of ICT use at school Source: Figure 6.5 Relationship between students’ skills in reading and computer use at school (average across OECD countries) OECD average Highest score Print reading Digital reading Students with a value above 1 use chat or email at least once a week at school, browse the Internet for schoolwork almost every day, and practice and drill on computers (e.g. for foreign language or maths) at least weekly Most students with a value above 0 use email at school at least once a month, browse the Internet for schoolwork at least once a week, and practice and drill on computers (e.g. for foreign language or maths) at least once a month

35. 460 470 480 490 500 510 520 530 -2 -1 0 1 2 Scorepoints Index of ICT use at school OECD average Australia Source: Figure 6.5 Students who use computers at school only moderately score the highest in reading OECD average

36. Frequency of computer use at school and digital reading skills OECD average relationship, after accounting for the socio-economic status of students and schools 420 430 440 450 460 470 480 490 500 510 520 Never or hardly ever Once or twice a month Once or twice a week Almost every day Every day Performance in digital reading Browse the Internet for schoolwork Use e-mail at school Chat on line at school Practice and drill (e.g. for foreign-language learning or mathematics) Scorepoints Source: Figure 6.6 35 37 39 41 43 45 47 49 51 53 55 Never or hardly ever Once or twice a month Once or twice a week Almost every day Every day Quality of navigation Indexoftask-oreintedbrowsing

37. Students who do not use computers in maths lessons score highest in mathematics 450 460 470 480 490 500 510 520 -2 -1 0 1 2 Scorepoints Index of computer use in mathematics lessons Source: Figure 6.7 Relationship between students’ skills in reading and computer use at school (average across OECD countries) Paper-based mathematics Computer-based mathematics Highest score OECD average

38. Teaching practices and computer use in math lessons (OECD average) -0.25 -0.20 -0.15 -0.10 -0.05 0.00 0.05 0.10 0.15 0.20 0.25 Index of mathematics teachers' behaviour: structuring practices Index of mathematics teachers' behaviour: formative assessment Index of mathematics teachers' behaviour: student orientation Index of mathematics teachers' behaviour: cognitive activation Index of disciplinary climate in mathematics lessons Students use computers Only the teacher uses computers No use of computersMean index Source: Figure 2.19

39. Mean mathematics performance, by school location, after accounting for socio-economic status Fig II.3.3 7575 Most teachers value 21st century pedagogies… Percentage of lower secondary teachers who "agree" or "strongly agree" that: 0 10 20 30 40 50 60 70 80 90 100 Students learn best by finding solutions to problems on their own Thinking and reasoning processes are more important than specific curriculum content Students should be allowed to think of solutions to practical problems themselves before the teacher shows them how they are solved My role as a teacher is to facilitate students' own inquiry Average

40. 0 20 40 60 80 100 Students work on projects that require at least one week to complete Students use ICT for projects or class work Give different work to the students who have difficulties learning and/or to those who can advance faster Students work in small groups to come up with a joint solution to a problem or task Let students practice similar tasks until teacher knows that every student has understood the subject matter Refer to a problem from everyday life or work to demonstrate why new knowledge is useful Check students' exercise books or homework Present a summary of recently learned content Average Mean mathematics performance, by school location, after acc ounting for socio-economic status Fig II.3.3 7676 …but teaching practices do not always reflect that Percentage of lower secondary teachers who report using the following teaching practices "frequently" or "in all or nearly all lessons"

41. Mean mathematics performance, by school location, after acc ounting for socio-economic status Fig II.3.3 7777 Teachers' needs for professional development Percentage of lower secondary teachers indicating they have a high level of need for professional development in the following areas 0 5 10 15 20 25 30 35 40 Knowledge of the curriculum Knowledge of the subject field(s) School management and administration Pedagogical competencies Developing competencies for future work Teaching cross-curricular skills Student evaluation and assessment practice Student career guidance and counselling Approaches to individualised learning Teaching in a multicultural or multilingual setting Student behaviour and classroom management New technologies in the workplace ICT skills for teaching Teaching students with special needs Average

42. 78 The potential of technology Four dimensions Regrouping educators Regrouping learners Rescheduling learning Widening pedagogic repertoires • To gain the benefits of collaborative planning, work, and shared professional development strategies • To open up pedagogical options • To give extra attention to groups of learners • To give learners a sense of belonging & engagement • To mix students of different ages • To mix different abilities and strengths • To widen pedagogical options, including peer teaching • To allow for deeper learning • To create flexibility for more individual choices • To accelerate learning • To use out-of-school learning in effective & innovative ways • Inquiry, authentic learning, collaboration, and formative assessment • A prominent place for student voice & agency

43. • Expand access to content – As specialised materials well beyond textbooks, in multiple formats, with little time and space constraints • Support new pedagogies with learners as active participants – As tools for inquiry-based pedagogies and collaborative workspaces • Collaboration for knowledge creation – Collaboration platforms for teachers to share and enrich teaching materials • Feedback – Make it faster and more granular • Automatise data-intensive processes – Visualisation Technology can amplify innovative teaching

44. • Experiential learning – E.g. remote and virtual labs, project-based and enquiry- based pedagogies • Hands-on pedagogies – E.g. game development • Cooperative learning – E.g. local and global collaboration • Interactive and metacognitive pedagogies – E.g. real-time assessment Using digital technology

45. 81 Mobilise innovation Innovation inspired by science (15/1) Innovation inspired by practitioners Innovation inspired by users Entrepreneurial development of new products and services

46. • Education is a heavily personalised service, so productivity gains through technology are limited, especially in the teaching & learning process • Impact of technology on educational delivery remains sub-optimal – Over-estimation of digital skills among teachers AND students – Naïve policy and implementation strategies – Resistance of teachers AND students – Lack of understanding of pedagogy and instructional design – Low quality of educational software and courseware Some conclusions

47. • Some new developments seem to be more promising: – Highly interactive, non-linear courseware, based on state-of- the-art instructional design – Sophisticated software for experimentation, simulation – Social media to support learning communities and communities of practice among teachers – Use of gaming in instruction • Concerted influence on the ‘education industry’ Some conclusions

48. • Make costs and benefits of educational innovation as symmetric as possible – Everyone supports innovation • (except for their own children) – The benefits for ‘winners’ are often insufficient to mobilise support, the costs for ‘losers’ are concentrated • That’s the power of interest groups – Need for consistent, co-ordinated efforts to persuade those affected of the need for change and, in particular, to communicate the costs of inaction Some conclusions

49. • Given the uncertainties that accompany change, education stakeholders will always value the status quo. • Successful innovations… – are good at communicating the need for change and building support for change – tend to invest in capacity development and change- management skills – develop evidence and feed this back to institutions along with tools with which they can use the information – Are backed by sustainable financing • Teachers need to be active agents, not just in the implementation of innovations, but also in their design Some conclusions

50. 86 86 Thank you Find out more about our work at www.oecd.org – All publications – The complete micro-level database Email: Andreas.Schleicher@OECD.org Twitter: SchleicherEDU and remember: Without data, you are just another person with an opinion

51. Using log-file data to understand what drives performance in PISA (Case study)

52. Relationship between long reaction time on Task 2 in the unit SERAING and low performance in reading Across countries and economies 0 5 10 15 20 25 30 35 40 Japan Korea HongKong-China ChineseTaipei Macao-China Shanghai-China Singapore UnitedStates Denmark Slovenia Italy Norway Estonia Australia Belgium Israel France Canada Portugal OECDaverage Austria Ireland Poland Spain Sweden RussianFederation SlovakRepublic Hungary Chile UnitedArabEmirates Brazil Colombia Reaction time longer than 30 sec. No action recorded Source: Figure 7.4 0510152025303540455055 For comparison: low performers in reading % %

53. Success from perseverance Percentage of students who succeed on Task 3 in the unit SERAING, by time spent on the task 0 10 20 30 40 50 60 70 80 Canada UnitedStates Australia France Estonia Shanghai-China Belgium Austria Italy HongKong-China ChineseTaipei Japan Norway Sweden Denmark OECDaverage Singapore Portugal Poland Slovenia Macao-China Korea Israel Ireland RussianFederation SlovakRepublic Spain Hungary Colombia Chile UnitedArabEmirates Brazil Full credit in less than 4 minutes Full credit in 4 to 7 minutes Full credit in more than 7 minutes % Source: Figure 7.6

54. Navigation behaviour in Task 2 in the unit SERAING Source: Figure 7.9

55. Quality and quantity of navigation steps in Task 2 in the unit SERAING, by performance on the task OECD average values Task-relevant steps 3.1 Task-relevant steps 1.1 Missteps 0.4 Missteps 0.9 Corrections 0.4 Corrections 0.7 Task- irrelevant steps 0.1 Task- irrelevant steps 0.2 0 1 2 3 4 Successful students Unsuccessful students Source: Figure 7.10 Navigation steps

Students, Computers and Learning: Making the Connection (Andreas Schleicher, (Director, OECD Directorate for Education and Skills)

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