students, computers and learning: making the connection (andreas schleicher, (director, oecd...
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STUDENTS, COMPUTERSAND LEARNING:
MAKING THE CONNECTION
September 2015
Andreas SchleicherDirector for Education and Skills
The kind of things that are easy to teach are
now easy to automate, digitize or outsource
Robotics
Google Autonomous Vehicle
>1m km, one minor accident,
occasional human intervention
Augmented Reality
A lot more to come
• 3D printing• Synthetic biology• Brain enhancements• Nanomaterials• Etc.
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
Digital skills of 15-year-olds
390400410420430440450460470480490500510520530540550560570 Singapore
KoreaHong Kong-China
Japan
CanadaShangai-ChinaEstoniaIreland Australia Chinese TapeiMacao-China
France United StatesItalyBelgium NorwaySwedenDenmark
PortugalAustriaPoland Slovak RepublicSlovenia
Spain Russian FederationIsrael
Chile Hungary
Brazil
United Arab Emirates
Colombia
Chart TitleMean score
Strong performance in in digital reading
Low performance in digital reading
18
Average performancein digital reading
Fig 3.1
Countries doing better/worse in digital literacy than in print reading?
Sing
apor
e
Kore
a
Japa
n
Hong
Kon
g-Ch
ina
Italy
Cana
da
Unite
d St
ates
Swed
en
Aust
ralia
Esto
nia
Mac
ao-C
hina
Fran
ce
Braz
il
Slov
ak R
epub
lic
Irela
nd
Chin
ese
Taip
ei
Chile
OECD
ave
rage
Denm
ark
Norw
ay
Belg
ium
Portu
gal
Aust
ria
Slov
enia
Russ
ian
Fede
ratio
n
Spai
n
Shan
ghai
-Chi
na
Colo
mbi
a
Isra
el
Pola
nd
Hung
ary
Unite
d Ar
ab E
mira
tes
-60
-50
-40
-30
-20
-10
0
10
20
30
40Students' performance in digital read-ing is higher than their expected per-
formance
Students' performance in digital reading is lower than their ex-
pected performance
Source: Figure 3.7
Score-point difference
Performance that would be expected based solely on
print-reading
Think, then click: Task-oriented browsingAverage rank of students in the international comparison of students taking the same test form
Sing
apor
e
Aust
ralia
Kore
a
Cana
da
Unite
d St
ates
Irela
nd
Hong
Kon
g-Ch
ina
Fran
ce
Japa
n
Belg
ium
Portu
gal
OECD
ave
rage
Denm
ark
Swed
en
Mac
ao-C
hina
Esto
nia
Norw
ay
Shan
ghai
-Chi
na
Italy
Chin
ese
Taip
ei
Aust
ria
Pola
nd
Isra
el
Slov
enia
Spai
n
Chile
Slov
ak R
epub
lic
Hung
ary
Russ
ian
Fede
ratio
n
Unite
d Ar
ab E
mira
tes
Braz
il
Colo
mbi
a
25
30
35
40
45
50
55
60
65
70
75
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.
Classification of students based on the quality of their browsing activity
Sing
apor
e
10
Kore
a
15
Hong
Kon
g-Ch
ina
19
Aust
ralia
8
Cana
da
8
Unite
d St
ates
1
0
Irela
nd
9
Japa
n
16
Mac
ao-C
hina
2
3
Shan
ghai
-Chi
na
23
Fran
ce
10
Chin
ese
Taip
ei
23
OECD
ave
rage
1
2
Belg
ium
1
1
Italy
1
5
Swed
en
9
Norw
ay
11
Esto
nia
13
Portu
gal
10
Isra
el
11
Aust
ria
14
Denm
ark
11
Pola
nd
9
Slov
enia
1
1
Chile
1
4
Spai
n
13
Slov
ak R
epub
lic
16
Hung
ary
13
Russ
ian
Fede
ratio
n
18
Unite
d Ar
ab E
mira
tes
14
Braz
il
11
Colo
mbi
a
17
0
10
20
30
40
50
60
70
80
90
100
Mostly unfocused browsing activity No browsing activity Insufficient or mixed browsing activityHighly 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
Explained variation in the digital reading performance of countries and economies
Variation in dig-ital reading per-
formance ex-plained by print- reading perfor-
mance
Residual variation ex-plained by the quantity
of navigation steps(overall browsing ac-
tivity)
Residual variation uniquely explained by the quality of naviga-
tion(task-oriented brows-
ing)
Unexplained variation
80.4 %
10.4 %
4.4 %
4.9 %
Source: Figure 4.9
Relationship between digital reading performance and navigation behaviour
30 35 40 45 50 55 60 65 70-60
-50
-40
-30
-20
-10
0
10
20
30
40
United Arab Emirates
Chinese Taipei
Singapore
Shanghai-China
Russian Federation
Macao-China
Hong Kong-China
Colombia
Brazil United StatesSweden
Spain Slovenia
Slovak Republic
Portugal
Poland
Norway
Korea
JapanItaly
Israel
Ireland
Hungary
FranceEstonia
DenmarkChile
Canada
Belgium
Austria
Australia
Index of task-oriented browsing
Rel
ativ
e pe
rfor
man
ce in
dig
ital
rea
ding
, af
ter
acco
unti
ng fo
r pe
rfor
man
ce in
pri
nt
read
ing
OEC
D a
ver-
age
OECD average
R² = 0.50
Source: Figure 4.10
Percentile rank
Strong performance in in computer-based assessment of mathematics
Low performance in computer-based assessment of mathematics
26
Average performancein computer-based
assesmentof mathematics
Fig 3.10
390400410420430440450460470480490500510520530540550560570 Singapore
Shangai-ChinaKoreaHong-KongMacao-ChinaJapanChinese-Tapei
CanadaEstoniaBelgiumFranceAustralia AustriaItalyNorwayUnited States Slovak RepublicDenmark Ireland
SwedenPolandRussian FederationPortugal Slovenia
SpainHungary
Israel
United Arab EmiratesChile
Brazil
Colombia
Chart TitleMean score
Relative success on mathematics tasks that require the use of computers to solve problemsCompared to the OECD averageUn
ited
Arab
Em
irate
s
Cana
da
Unite
d St
ates
Japa
n
Mac
ao-C
hina
Braz
il
Slov
enia
Aust
ria
Russ
ian
Fede
ratio
n
Chin
ese
Taip
ei
Slov
ak R
epub
lic
Aust
ralia
Isra
el
Portu
gal
Kore
a
Shan
ghai
-Chi
na
Norw
ay
Hung
ary
Hong
Kon
g-Ch
ina
Sing
apor
e
OECD
ave
rage
Swed
en
Denm
ark
Esto
nia
Belg
ium
Colo
mbi
a
Italy
Spai
n
Pola
nd
Irela
nd
Chile
Fran
ce
0.80
0.85
0.90
0.95
1.00
1.05
1.10
1.15
1.20
Better-than-expected performance on tasks that do not require the use of computers to solve mathemat-
ics 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
Students’ use of computers
Access to computers at homeDe
nmar
kNo
rway
Swed
enIc
elan
dNe
ther
land
sAu
stra
liaLi
echt
enst
ein
Qata
rSw
itzer
land
Luxe
mbo
urg
Finl
and
Belg
ium
Unite
d Ar
ab E
mira
tes
Germ
any
Cana
daUn
ited
King
dom
Sing
apor
eAu
stria
Fran
ceIs
rael
Slov
enia
OECD
ave
rage
New
Zeal
and
Spai
nUn
ited
Stat
esEs
toni
aCz
ech
Repu
blic
Portu
gal
Irela
ndHo
ng K
ong-
Chin
aCh
ines
e Ta
ipei
Italy
Slov
ak R
epub
licM
acao
-Chi
naHu
ngar
yPo
land
Chile
Urug
uay
Latv
iaAr
gent
ina
Gree
ceSh
angh
ai-C
hina
Japa
nBu
lgar
iaLi
thua
nia
Croa
tiaM
alay
sia
Cost
a Ri
caJo
rdan
Serb
iaRu
ssia
n Fe
dera
tion
Mon
tene
gro
Kore
aBr
azil
Mex
ico
Rom
ania
Peru
Thai
land
Colo
mbi
aTu
nisi
aTu
rkey
Alba
nia
Kaza
khst
anVi
et N
amIn
done
sia
0
10
20
30
40
50
60
70
80
90
100
At least one computer3 or more computers
Source: Figure 1.1
%
Access to computers at home:Change between 2009 and 2012
Denm
ark
Norw
ay 1
Swed
en
Icel
and
1Ne
ther
land
s 1
Aust
ralia
1Li
echt
enst
ein
1Qa
tar
Switz
erla
nd
Luxe
mbo
urg
1Fi
nlan
d Be
lgiu
m
Unite
d Ar
ab E
mira
tes
Germ
any
Cana
da 1
Unite
d Ki
ngdo
m 1
Sing
apor
e 1
Aust
ria
Fran
ce
Isra
el
Slov
enia
OE
CD a
vera
ge
New
Zeal
and
1Sp
ain
Unite
d St
ates
1Es
toni
a Cz
ech
Repu
blic
Po
rtuga
l Ire
land
Ho
ng K
ong-
Chin
a Ch
ines
e Ta
ipei
Ita
ly
Slov
ak R
epub
lic
Mac
ao-C
hina
Hu
ngar
y Po
land
Ch
ile
Urug
uay
Latv
ia
Arge
ntin
a Gr
eece
Sh
angh
ai-C
hina
Ja
pan
Bulg
aria
Li
thua
nia
Croa
tia
Mal
aysi
a Co
sta
Rica
Jo
rdan
Se
rbia
Ru
ssia
n Fe
dera
tion
Mon
tene
gro
Kore
a 1
Braz
il M
exic
o Ro
man
ia
Peru
Th
aila
nd
Colo
mbi
a Tu
nisi
a Tu
rkey
Al
bani
a Ka
zakh
stan
Vi
et N
amIn
done
sia
1,2
0
10
20
30
40
50
60
70
80
90
100
PISA 2009 - At least one computer PISA 2012 - At least one computer PISA 2009 - 3 or more computersPISA 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.
Bridging the social divide
Access to the Internet at home and students' socio-economic status
Denm
ark
Icel
and
Finl
and
Hong
Kon
g-Ch
ina
Neth
erlan
dsNo
rway
Switz
erla
ndSw
eden
Slov
enia
Esto
nia
Aust
riaUn
ited
King
dom
Germ
any
Mac
ao-C
hina
Liec
hten
stei
n 1
Fran
ceLu
xem
bour
gBe
lgiu
mIre
land
Cana
daKo
rea
Aust
ralia
Italy
Czec
h Re
publ
icSi
ngap
ore
Chin
ese
Taip
eiCr
oatia
Portu
gal
Spai
nPo
land
OECD
ave
rage
Unite
d Ar
ab E
mira
tes
Qata
rLi
thua
nia
Isra
elHu
ngar
yNe
w Ze
alan
dUn
ited
Stat
esRu
ssia
n Fe
dera
tion
Bulg
aria
Latv
iaSl
ovak
Rep
ublic
Japa
nSe
rbia
Gree
ceMo
nten
egro
Shan
ghai
-Chi
naUr
ugua
yRo
man
iaBr
azil
Arge
ntin
aCh
ileCo
sta
Rica
Jord
anM
alays
iaTu
rkey
Kaza
khst
anCo
lom
bia
Tuni
sia
Thai
land
Peru
Mexic
oIn
done
sia
Viet
Nam
0
10
20
30
40
50
60
70
80
90
100
Top quarterThird quarterSecond 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.
Early exposure to computers% of students who first used a computer when they were 6 years or younger
Denm
ark
Swed
enNo
rway
Finl
and
Icel
and
Aust
ralia
New
Zeal
and
Isra
elEs
toni
aSl
oven
iaOE
CD a
vera
geHo
ng K
ong-
Chin
aIre
land
Spai
nBe
lgiu
mPo
land
Sing
apor
eCz
ech
Repu
blic
Italy
Chile
Hung
ary
Aust
riaSw
itzer
land
Germ
any
Jord
anSe
rbia
Latv
iaCr
oatia
Liec
hten
stei
nM
acao
-Chi
naUr
ugua
yPo
rtuga
lCo
sta
Rica
Kore
aSl
ovak
Rep
ublic
Chin
ese
Taip
eiRu
ssia
n Fe
dera
tion
Japa
nGr
eece
Turk
eySh
angh
ai-C
hina
Mex
ico
0
10
20
30
40
50
60
70Top quarterThird quarterSecond quarter
The PISA index of economic, social and cultural status (ESCS)
Source: Figure 5.4
%
Early exposure to computers, by gender% of students who first used a computer when they were 6 years or younger
Denm
ark
Swed
enIs
rael
Norw
ayNe
w Ze
alan
d 1
Finl
and
Aust
ralia
Icel
and
Esto
nia
Hong
Kon
g-Ch
ina
1Ire
land
Sing
apor
eSp
ain
Pola
ndOE
CD a
vera
geSl
oven
iaCo
sta
Rica
1Ch
ileJo
rdan
Urug
uay
Belg
ium
Serb
iaCr
oatia
Mac
ao-C
hina
Portu
gal
Italy
Hung
ary
Latv
iaAu
stria
Czec
h Re
publ
icSw
itzer
land
Germ
any
Kore
aCh
ines
e Ta
ipei
Liec
hten
stei
nJa
pan
1Ru
ssia
n Fe
dera
tion
Shan
ghai
-Chi
naM
exic
oTu
rkey
Slov
ak R
epub
licGr
eece
0
10
20
30
40
50
60
70
Boys Girls
Source: Figure 5.5
%
1. The difference between boys and girls is not statistically significant.
Percentage of students with access to the Internet at school, but not at home
MexicoTurke
yJo
rdan
Costa RicaChile
UruguayGree
ce
Shanghai-
ChinaJap
an
New Zeal
andSerb
iaLatv
ia
Russian
Federa
tion
OECD avera
ge
Hungary
Slovak Rep
ublicSpain
Portugal
Poland
Chinese Taip
ei
Croatia
Australia
Singapore
Korea ItalyIre
landIsr
ael
Czech Rep
ublic
Macao-China
BelgiumEsto
nia
German
y
Austria
Switzerla
nd
Liechten
stein 1
Hong Kong-China
Slovenia
Sweden
Norway
Denmark
Finland
Icelan
d
Netherl
ands 1
0
10
20
30
40
50
60
All students Socio-economically disadvantaged studentsSocio-economically advantaged students
Source: Figure 5.7
%
1. The difference between socio-economically advantaged and disadvantaged students is not statistically significant.
Time online
Time spent on line in school and outside of schoolM
acao
-Chi
na
45
Denm
ark
44
Swed
en
44
Esto
nia
41
Norw
ay
41
Hong
Kon
g-Ch
ina
39
Russ
ian
Fede
ratio
n
39Ic
elan
d
37Au
stra
lia
38
Pola
nd
36
Hung
ary
37
Czec
h Re
publ
ic
36
Chin
ese
Taip
ei
36
Neth
erla
nds
34
Slov
ak R
epub
lic
35
Sing
apor
e
35Sp
ain
33
Portu
gal
35
Chile
3
6La
tvia
3
4Ge
rman
y
32Ur
ugua
y
34Cr
oatia
3
2Be
lgiu
m
30
Gree
ce
31
Slov
enia
2
9OE
CD a
vera
ge
30
Serb
ia
30
Isra
el
30
Liec
hten
stei
n
31Fi
nlan
d
20Ne
w Ze
alan
d
27Sw
itzer
land
2
4Au
stria
2
4Co
sta
Rica
2
5Ja
pan
23
Jord
an
25
Shan
ghai
-Chi
na
20
Irela
nd
18
Italy
1
7Ko
rea
14
Mex
ico
18
Turk
ey
13
0
20
40
60
80
100
120
140
160
180
200
During weekdays, outside of school During weekdays, at schoolDuring 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
Feeling lonely at school,by time spent on the Internet outside of school during weekdays
Shan
ghai-
China
Jord
an
Mac
ao-C
hina
Sing
apor
e
Turk
ey
Urug
uay
Hong
Kon
g-Ch
ina
New
Zeala
nd
Finla
nd
Kore
a 1
Slov
ak R
epub
lic
Gre
ece
Aust
ralia
Hung
ary
Icela
nd
Japa
n
Norw
ay
Irelan
d
Latv
ia
Mex
ico
OEC
D av
erag
e
Swed
en
Serb
ia
Chine
se T
aipei
Polan
d
Esto
nia
Belgi
um
Denm
ark
Portu
gal
Slov
enia
Cost
a Ri
ca 1
Czec
h Re
publi
c 1
Russ
ian F
eder
ation
Chile
Neth
erlan
ds
Aust
ria
Italy
Isra
el 1
Spain
Croa
tia 1
Ger
man
y 1
Switz
erlan
d
Liech
tens
tein
0
5
10
15
20
25
30
35Low Internet users: one hour or lessModerate Internet users : 1 to 2 hoursHigh Internet users: 2 to 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.
Technology in teaching and learning
Number of students per school computerAu
stra
liaNe
w Ze
alan
dM
acao
-Chi
naUn
ited
King
dom
Czec
h Re
publ
icNo
rway
Unite
d St
ates
Lith
uani
aSl
ovak
Rep
ublic
Sing
apor
eLi
echt
enst
ein
Esto
nia
Hong
Kon
g-Ch
ina
Spai
nLu
xem
bour
gHu
ngar
yLa
tvia
Denm
ark
Kaza
khst
anIre
land
Bulg
aria
Neth
erla
nds
Switz
erla
ndBe
lgiu
mCa
nada
Fran
ceSh
angh
ai-C
hina
Aust
riaRu
ssia
n Fe
dera
tion
Thai
land
Finl
and
Slov
enia
Japa
nCo
lom
bia
Swed
enPo
rtuga
lPo
land
Icel
and
Italy
Qata
rUn
ited
Arab
Em
irate
sGe
rman
yRo
man
iaOE
CD a
vera
geIs
rael
Chile
Jord
anCr
oatia
Kore
aCh
ines
e Ta
ipei
Mon
tene
gro
Peru
Gree
ceVi
et N
amUr
ugua
ySe
rbia
Alba
nia
Arge
ntin
aM
exic
oIn
done
sia
Mal
aysi
aCo
sta
Rica
Braz
ilTu
rkey
Tuni
sia 0
1
2
3
4
5
6
7
8
9
10
Mag
nifie
dStudents
per computer
Source: Figure 2.14
Use of ICT at school% of students who reported engaging in each activity at least once a week
Browse the Internet for schoolwork
Use school computers for group work and commu-nication with
other stu-dents
Do individual homework
on a school computer
Use e-mail at school
Download, upload or
browse ma-terial 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 web-
site
Play simu-lations at
school
0
10
20
30
40
50
60
70
80
90
100
Shanghai-China
Japan Japan Shanghai-China Japan Japan Japan Korea Korea
Australia
Denmark
AustraliaLiechtenstein
Denmark
Denmark
Norway
Norway
Jordan
OECD average Top country/economy Bottom country/economy
Source: Figure 2.1
%
Index of ICT use at school
Denmark
Norway
Australia
Netherl
ands
Czech Rep
ublic
Liechten
stein
Sweden
New Zeal
and
Slovak Rep
ublicGree
ceSpain
Jordan
Chile
Finland
Austria
SloveniaMexi
co
OECD avera
ge
Switzerla
nd
Portugal
Uruguay
Macao-China
Hungary Italy
Croatia
Singapore
Icelan
d
Costa Rica
Israel
BelgiumEsto
nia
Chinese Taip
ei
Hong Kong-ChinaSerb
iaLatv
ia
Russian
Federa
tion
German
yTurke
yIre
landPolan
d
Shanghai-
ChinaJap
anKorea
-1.50
-1.00
-0.50
0.00
0.50
1.00
Source: Figure 2.3
Mean index
Computer use and learning outcomes
Trends in mathematics performance and increase in computers in schools
-40
-30
-20
-10
0
10
20
30
40
-20.1152381896973
-0.0702400356531143
-14.7571697235107
-14.4163360595703
-17.4971179962158
-14.2609186172485
-25.5388507843018
-15.8147993087769
10.53952407836918.06156444549561
-12.9679374694824
-22.312952041626
-1.33985912799835
19.6569709777832
2.27041673660278
11.5392894744873
-3.36345863342285
28.0631275177002
-14.8515176773071
-23.7366809844971
-5.81232023239136
27.262321472168
21.0464859008789
-16.5387115478516
-0.788758635520935
-30.7858123779297
4.37785577774048
24.5650043487549
-1.51600325107574
35.4440994262695
10.85785579681414.9556198120117
7.1960730552673310.8642139434814
13.76276206970219.75953102111816
29.0914077758789
-12.9083108901978
R² = 0.214392916630603R² = 0.267658948927542
All countries and economies
Number of computers per student, after accounting for per capita GDP
Diff
eren
ce in
mat
hem
atic
s pe
rfor
man
ce
(PIS
A 20
12 -
PISA
200
3)
Fewer computers More computersFewer computers More computers
Expected number of com-puters per student, based on
per capita GDP
Source: Figure 6.3
Students who use computers at school only moderately score the highest in reading
-2.0 -1.8 -1.6 -1.4 -1.2 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0450
460
470
480
490
500
510
520
Index of ICT use at school
Scor
e po
ints
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
-2.0 -1.8 -1.6 -1.4 -1.2 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0460
470
480
490
500
510
520
530
OECD average Australia
Index of ICT use at school
Scor
e po
ints
Source: Figure 6.5
Students who use computers at school only moderately score the highest in reading
OECD average
Frequency of computer use at school and digital reading skillsOECD average relationship, after accounting for the socio-economic status of students and schools
Never or
hardly ever
Once or
twice a month
Once or
twice a week
Almost every day
Every day
420
430
440
450
460
470
480
490
500
510
520Performance in digital reading
Browse the In-ternet for schoolwork
Use e-mail at school
Chat on line at school
Practice and drill (e.g. for foreign-language learning or mathematics)
Scor
e po
ints
Source: Figure 6.6
Never or
hardly ever
Once or twice a month
Once or twice a week
Almost every day
Every day
35
37
39
41
43
45
47
49
51
53
55
Quality of navigation
Inde
x of
tas
k-or
eint
ed
brow
sing
Students who do not use computers in maths lessons score highest in mathematics
450
460
470
480
490
500
510
520
Index of computer use in mathematics lessons
Scor
e po
ints
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
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
Students use computers Only the teacher uses computersNo use of computers
Mean index
Source: Figure 2.19
Mean mathematics performance, by school location, after accounting for socio-economic status Fig II.3.37
575 Most teachers value 21st century pedagogies…
Percentage of lower secondary teachers who "agree" or "strongly agree" that:
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
0 10 20 30 40 50 60 70 80 90 100
Average
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
0 20 40 60 80 100
Average
Mean mathematics performance, by school location, after accounting for socio-economic status Fig II.3.37676 …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"
Mean mathematics performance, by school location, after accounting for socio-economic status Fig II.3.37777 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
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
0 5 10 15 20 25 30 35 40
Average
78 The potential of technology
Four dimension
s
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
• 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
• 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
81 Mobilise innovation
Innovation inspired by
science (15/1)
Innovation inspired by
practitioners
Innovation inspired by
users
Entrepreneurial
development of new
products and services
• 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
• 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
• 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
• 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
86
86 Thank you
Find out more about our work at www.oecd.org– All publications– The complete micro-level database
Email: [email protected]: SchleicherEDU
and remember:Without data, you are just another person with an opinion
Using log-file data to understand what drives performance in PISA
(Case study)
Relationship between long reaction time on Task 2 in the unit SERAING and low performance in readingAcross countries and economies
Japa
n
Kore
a
Hong
Kon
g-Ch
ina
Chin
ese T
aipei
Mac
ao-C
hina
Shan
ghai-
Chin
a
Sing
apor
e
Unite
d St
ates
Denm
ark
Slov
enia
Italy
Norw
ay
Esto
nia
Aust
ralia
Belg
ium
Israe
l
Fran
ce
Cana
da
Portu
gal
OECD
aver
age
Aust
ria
Irelan
d
Polan
d
Spain
Swed
en
Russ
ian F
eder
atio
n
Slov
ak R
epub
lic
Hung
ary
Chile
Unite
d Ar
ab E
mira
tes
Braz
il
Colo
mbi
a0
10
20
30
40Reaction time longer than 30 sec. No action recorded
Source: Figure 7.4
05
1015
2025
3035
4045
5055
%
%
Success from perseverancePercentage of students who succeed on Task 3 in the unit SERAING, by time spent on the task
Cana
da
Unite
d St
ates
Aust
ralia
Fran
ce
Esto
nia
Shan
ghai
-Chi
na
Belg
ium
Aust
ria
Italy
Hong
Kon
g-Ch
ina
Chin
ese
Taip
ei
Japa
n
Norw
ay
Swed
en
Denm
ark
OECD
ave
rage
Sing
apor
e
Portu
gal
Pola
nd
Slov
enia
Mac
ao-C
hina
Kore
a
Isra
el
Irela
nd
Russ
ian
Fede
ratio
n
Slov
ak R
epub
lic
Spai
n
Hung
ary
Colo
mbi
a
Chile
Unite
d Ar
ab E
mira
tes
Braz
il
0
10
20
30
40
50
60
70
80
Full credit in less than 4 minutes Full credit in 4 to 7 minutes%
Source: Figure 7.6
Navigation behaviour in Task 2 in the unit SERAING
Source: Figure 7.9
Quality and quantity of navigation steps in Task 2 in the unit SERAING, by performance on the taskOECD average values
Successfulstudents
Unsuccessfulstudents
0 1 2 3 4
Task-relevant steps 3.1
Task-relevant steps 1.1
Missteps0.4
Missteps 0.9
Corrections0.4
Corrections 0.7
Task-irrelevant
steps 0.1
Task-irrelevant
steps 0.2
Source: Figure 7.10
Navigation steps