Are there computers in the classroom? Does it matter? Students, Computers and Learning: Making the Connection examines how students’ access to and use of information and communication technology (ICT) devices has evolved in recent years, and explores how education systems and schools are integrating ICT into students’ learning experiences. Based on results from PISA 2012, the report discusses differences in access to and use of ICT – what are collectively known as the “digital divide” – that are related to students’ socio-economic status, gender, geographic location, and the school a child attends. The report highlights the importance of bolstering students’ ability to navigate through digital texts. It also examines the relationship among computer access in schools, computer use in classrooms, and performance in the PISA assessment. As the report makes clear, all students first need to be equipped with basic literacy and numeracy skills so that they can participate fully in the hyper-connected, digitised societies of the 21st century.
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
17. 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
18. 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
19. 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.
20. 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
21. 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
22. 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
23. 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
24. 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
26. 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
%
27. 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.
29. 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.
30. 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
%
31. 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.
32. 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.
34. 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
35. 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.
37. 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
38. 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
%
39. 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
41. 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
42. 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
43. 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
44. 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
45. 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
46. 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
47. 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
48. 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"
49. 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
50. 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
51. • 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
52. • 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
53. 81 Mobilise innovation
Innovation
inspired by
science (15/1)
Innovation
inspired by
practitioners
Innovation
inspired by
users
Entrepreneurial
development of
new products
and services
54. • 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
55. • 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
56. • 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
57. • 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
58. 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
59. Using log-file data to
understand what drives
performance in PISA
(Case study)
60. 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
%
%
61. 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
63. 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