Title |
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Figure 1 – Variation in computer and information literacy scores across and within countries, 2013 |
[.xls] |
IEA, ICILS 2013. |
Figure 2 – Variation in computer and information literacy scores across and within countries, 2018 |
[.xls] |
IEA, ICILS 2018. |
Figure 3 – Distribution of computer and information literacy scores across achievement scale levels, 2013 and 2018 |
[.xls] |
IEA, ICILS 2018 & ICILS 2013. |
Figure 4 – Underachievement in computer and information literacy, 2013 and 2018 |
[.xls] |
IEA, ICILS 2018 & ICILS 2013. |
Figure 5 – Variation in computational thinking scores across and within countries, 2018 |
[.xls] |
IEA, ICILS 2018. |
Figure 6 – Digital competence areas addressed in terms of learning outcomes in national curricula
(ISCED 2), 2018/19 |
[.xls] |
European Commission/EACEA/Eurydice (2019). Digital Education at School in Europe. Annex 1b |
Figure 7 – Persons who cannot afford a computer, by group of country of birth, 2018 [%] |
[.xls] |
Eurostat, EU-SILC. Special extraction. |
Figure 8 – Percentage of school principals who report that the following shortages of resources hinder the
school’s capacity to provide quality instruction ‘quite a bit’ or ‘a lot’ |
[.xls] |
OECD, TALIS 2018 Database, Table I.3.63. |
Figure 9 – Percentage of teachers who reported investing in ICT to be of ‘high importance’ |
[.xls] |
OECD, TALIS 2018 Database, Table I.3.66. |
Figure 10 – Percentage of teachers for whom use of ICT for teaching was included in their formal
education, by year of completion |
[.xls] |
OECD, TALIS 2018 Database, Table I.4.13. |
Figure 11 – Percentage of teachers who felt ‘well prepared’ or ‘very well prepared’ for the use of ICT for
teaching, by year of completion |
[.xls] |
OECD, TALIS 2018 Database, Table I.4.20. |
Figure 12 – Percentage of teachers reporting a high level of need of professional development in ICT
skills for teaching |
[.xls] |
OECD, TALIS 2018 Database, Table I.5.21. |
Figure 13 – Percentage of teachers who reported that they ‘frequently’ or ‘always’ let pupils in the target
class use ICT for projects or class work in their class |
[.xls] |
OECD, TALIS 2018 Database, Table I.2.1. |
Figure 14 – Percentage of teachers who reported that they ‘frequently’ or ‘always’ let pupils use ICT for
projects or class work in their class, change from 2013 to 2018 |
[.xls] |
OECD, TALIS 2018 Database, Table I.2.4. |
Figure 15 – Change in the rate of early school leavers from education and training, 2009-2019 |
[.xls] |
Eurostat, EU Labour Force Survey. Online data code: [edat_lfse_14]. |
Figure 16 – Early leavers from education and training by sex, country of birth and degree of
urbanisation, 2019 [%] |
[.xls] |
Eurostat, EU Labour Force Survey 2019. Online data code: [edat_lfse_14], [edat_lfse_02] and [edat_lfse_30]. |
Figure 17 – Evolution of the ELET and completion rates in the EU-27 (2009-19) |
[.xls] |
Eurostat, EU Labour Force Survey. Online data code: [edat_lfse_14] and [edat_lfse_03] |
Figure 18 – ELET rate versus completion rate (2019) |
[.xls] |
European Commission, DG EAC. |
Figure 19 – Contextual factors influencing ELET: value of the regression coefficients |
[.xls] |
DG EAC calculations. |
Figure 20 – Urban-rural divide in tertiary educational attainment (30-34) by country, 2019 [%] |
[.xls] |
Eurostat, EU Labour Force Survey. Online data code: [edat_lfs_9913]. |
Figure 21 – TEA rate (30-34 year-olds) by country and sex, 2019 [%] |
[.xls] |
Eurostat, EU Labour Force Survey. Online data code: [edat_lfse_03]. |
Figure 22 – TEA rate (30-34 year-olds) by country, 2009, 2019 and national targets [%] |
[.xls] |
Eurostat, EU Labour Force Survey. Online data code: [edat_lfse_03]. |
Figure 23 – Average annual increase of TEA rate in 1999-2009 and 2009-2019 in groups of countries
above and below the EU-target in 2009 (pps) |
[.xls] |
Eurostat, EU Labour Force Survey, special data extraction, 2020. |
Figure 24 – Participation in ECEC by children between 4-years-old and the starting age of compulsory
education, 2017 and 2018 [%] |
[.xls] |
Eurostat, educ_uoe_enra10 |
Figure 25 – Participation in ECE by children between 4-years-old, respectively 3-years-old, and the
starting age of compulsory education, 2018 (%) |
[.xls] |
Eurostat, educ_uoe_enra10 and educ_UOE_enra21. |
Figure 26 – Participation in ECE of children from birth to the starting age of compulsory primary
education, 2018 [%] |
[.xls] |
Eurostat calculation from educ_uoe_enra02 on 2018 data (demo_pjan) |
Figure 27 – Underachievement rate in reading, 2018 [%] |
[.xls] |
PISA 2018, OECD. |
Figure 28 – Long-term change in underachievement rate in reading, 2009-2018 [%] |
[.xls] |
PISA 2018 and 2009, OECD. |
Figure 29 – Underachievement rate in mathematics, 2018 [%] |
[.xls] |
PISA 2018, OECD. |
Figure 30 – Change in underachievement rate in mathematics, 2015-2018 [pps] |
[.xls] |
PISA 2018 and 2015, OECD. |
Figure 31 – Underachievement rate in science in 2018 [%] |
[.xls] |
PISA 2018, OECD. |
Figure 32 – Top performers in reading, 2018 and 2015 [%] |
[.xls] |
PISA 2018 and 2015, OECD. |
Figure 33 – Top performers in mathematics, 2018 and 2015 [%] |
[.xls] |
Eurostat, EU-SILC. Special extraction, PISA 2018, OECD. |
Figure 34 – Top performers in science, 2018 and 2015 [%] |
[.xls] |
PISA 2018, OECD. |
Figure 35 – Underachievement rates of boys and girls in reading, 2018 [%] |
[.xls] |
PISA 2018, OECD. |
Figure 36 – Underachievement rates of boys and girls in mathematics, 2018 [%] |
[.xls] |
PISA 2018, OECD. |
Figure 37 – Underachievement rates of boys and girls in science, 2018 [%] |
[.xls] |
PISA 2018, OECD. |
Figure 38 – Underachievers in reading [%] by socio-economic status (ESCS), 2018 |
[.xls] |
PISA 2018, OECD. |
Figure 39 – Underachievers in reading [%] by migrant background, 2018 |
[.xls] |
PISA 2018, OECD. |
Figure 40 – The employment rate of recent graduates, 2010-2019 |
[.xls] |
Eurostat, LFS, online data code: [edat_lfse_24] |
Figure 41 – The employment rate premium of recent graduates by level and orientation of education
compared to the average employment rate of young adults aged 20-34 who are not in further education
or training, 2019 |
[.xls] |
Eurostat, LFS, online data code: [edat_lfse_24] |
Figure 42 – Absolute change in employment rates of recent graduates by level and orientation of
education, 2014-2019 |
[.xls] |
Eurostat, LFS, online data code: [edat_lfse_24] |
Figure 43 – Full-time equivalent gross monthly wage by educational attainment (2018) age group 16-34 |
[.xls] |
- |
Figure 44 – Full-time equivalent gross monthly wage by type of secondary education: vocational v
general (2018), age group 16- |
[.xls] |
Eurostat, EU-SILC. Special extraction |
Figure 45 – Adult (aged 25-64) participation in learning, 4-week reference period, 2010 and 2019 |
[.xls] |
Eurostat, LFS, online data code: [trng_lfse_01]. |
Figure 46 – Adult (aged 25-64) participation in learning, 12-month reference period, 2011 and 2016 |
[.xls] |
Eurostat, AES, online data code: [trng_aes_100] |
Figure 47 – Adult (aged 25-64) participation in learning, 12-month reference period, distinguishing
guided on the job training (GOJT), 2016 |
[.xls] |
Eurostat, AES, special data extraction for DG EMPL. |
Figure 48 – Adult (aged 25-64) participation in learning, 12-month reference period, changes between
2011 and 2016, distinguishing guided on the job training (GOJT) |
[.xls] |
Eurostat, AES, special data extraction for DG EMPL. |
Figure 49 – Adult (aged 25-64) participation in learning by employment status and level of qualification,
12-month reference period, EU-27, 2016 |
[.xls] |
Eurostat, AES, special data extraction for DG EMPL. |
Figure 50 – The structure of non-learners (aged 25-64) by employment situation and country, 12-month
reference period, 2016 |
[.xls] |
Eurostat, AES, special data extraction for DG EMPL. |
Figure 51 – The percentage non-learners (aged 25-64) who are employed in the private sector, by level
of qualification, 12-month reference period, 2016 |
[.xls] |
Eurostat, AES, special data extraction for DG EMPL. |
Figure 52 – Relative importance of adult learning determinants across countries: personal v education vs
job-related characteristics |
[.xls] |
CEDEFOP, AES 2016. |
Figure 53 – Relative importance of adult learning determinants across countries: personal characteristics |
[.xls] |
CEDEFOP, AES 2016. |
Figure 54 – Relative importance of adult learning determinants across countries: job-related
characteristics |
[.xls] |
CEDEFOP, AES 2016. |
Figure 55 – Outward degree and credit mobility of graduates, 2018 [%] |
[.xls] |
Eurostat, UOE, and OECD. Online data codes: [educ_uoe_grad01], [educ_uoe_mobg02] and [educ_uoe_mobc01] for graduates, degree-mobile graduates and credit-mobile graduates in the EU, EFTA, EEA and candidate countries. Special extraction from the OECD of international graduate data for degree-mobile graduates of EU origin who graduated in non-European countries (Australia, Canada, Chile, Colombia, Israel, Japan, Korea, New Zealand, Brazil and Russia). Eurostat, UOE, data extracted on 5 June 2020 and OECD data on 11 May 2020. |
Figure 56 – Outward credit mobility by type of mobility scheme, ISCED 5-8, 2018 |
[.xls] |
Eurostat, UOE. Online data code: [educ_uoe_mobc01]. Data extracted on 5 June 2020. |
Figure 57 – Inward degree mobility rates for tertiary education graduates by level of education and
origin, 2018 |
[.xls] |
Eurostat, UOE, and OECD. Online data codes: [educ_uoe_grad01] and [educ_uoe_mobg02] and [educ_uoe_mobc01] for graduates and degree-mobile graduates in the EU, EFTA, EEA and candidate countries. Special extraction from the OECD of international graduate data for degree-mobile graduates of EU origin who graduated in non-European countries (Australia, Canada, Chile, Colombia, Israel, Japan, Korea, New Zealand, Brazil and Russia). Eurostat, UOE, data extracted on 5 June 2020 and OECD data on 11 May 2020. |
Figure 58 – Inward degree-mobile graduates (ISCED 5-8) by area of origin, 2018 |
[.xls] |
Eurostat, UOE. Online data code: [educ_uoe_mobg02]. Data extracted on 5 June 2020. |
Figure 59 – Comparison of EU performance on the ET2020 targets before and after BREXIT |
[.xls] |
Eurostat, online data codes: [sdg_04_10], [sdg_04_20], [sdg_04_30], [sdg_04_40] [sdg_04_50] and [sdg_04_60]. |
Figure 60 – Public expenditure on education, 2015-2018 |
[.xls] |
Eurostat, General Government Expenditure by Function (COFOG), online data base: [gov_10a_exp] |
Figure 61 – Public expenditure on education by level, 2018 |
[.xls] |
Eurostat, COFOG, online data base: [gov_10a_exp] |
Figure 62 – Public expenditure on education by category of expenditure, 2018 |
[.xls] |
Eurostat, General Government Expenditure by Function (COFOG), online data base: [gov_10a_exp] |
Figure 63 – Change in real expenditure in pre-primary and primary level, 2015-2018 |
[.xls] |
Eurostat, COFOG, online data base: [gov_10a_exp]. |
Figure 64 – Change in real expenditure in secondary and post-secondary (non-tertiary) level, 2015-2018 |
[.xls] |
Eurostat, COFOG, online data base: [gov_10a_exp] |
Figure 65 – Change in real expenditure over time at tertiary level, 2015-2018 |
[.xls] |
Eurostat, COFOG, online data base: [gov_10a_exp] |
Figure 66 – Change in number of pupils and students at various education levels, 2013-2018 |
[.xls] |
European Commission, based on Eurostat data and UOE. Online data codes: [educ_uoe_enra01]. |
Figure 67 – ECEC summary table 1: Legal framework, 2019/20 |
[.xls] |
European Commission/EACEA/Eurydice (forthcoming). Structural Indicators for Monitoring Education and Training Systems in Europe – 2020. |
Figure 68 – ECEC summary table 2: Selected quality aspects, 2019/20 |
[.xls] |
European Commission/EACEA/Eurydice (forthcoming). Structural Indicators for Monitoring Education and Training Systems in Europe – 2020. |
Figure 69 – Staff with a minimum of a Bachelor's level qualification (ISCED 6), 2019/2020 |
[.xls] |
European Commission/EACEA/Eurydice. |
Figure 70 – Summary table on achievement in basic skills, 2019/2020 |
[.xls] |
European Commission/EACEA/Eurydice (forthcoming). Structural Indicators for Monitoring Education and Training Systems in Europe – 2020. |
Figure 71 – Top-level guidelines on underachievement as a topic in ITE, 2019/2020 |
[.xls] |
European Commission/EACEA/Eurydice. |
Figure 72 – ELET Summary table 1, 2019/2020 |
[.xls] |
European Commission/EACEA/Eurydice (forthcoming). Structural Indicators for Monitoring Education and Training Systems in Europe – 2020. |
Figure 73 – ELET Summary table 2, 2019/2020 |
[.xls] |
European Commission/EACEA/Eurydice (forthcoming). Structural Indicators for Monitoring Education and Training Systems in Europe – 2020. |
Figure 74 – Policies/measures encouraging the inclusion of ELET in ITE and/or CPD, 2019/2020 |
[.xls] |
European Commission/EACEA/Eurydice. |
Figure 75 – Summary table on higher education, 2019/2020 |
[.xls] |
European Commission/EACEA/Eurydice (forthcoming). Structural Indicators for Monitoring Education and Training Systems in Europe – 2020. |
Figure 76 – Quantitative targets for widening participation and/or attainment of under -represented
groups |
[.xls] |
European Commission/EACEA/Eurydice. |
Figure 77 – Summary table on graduate employability, 2019/2020 |
[.xls] |
European Commission/EACEA/Eurydice (forthcoming). Structural Indicators for Monitoring Education and Training Systems in Europe – 2020. |
Figure 78 – Summary table on learning mobility, 2018/2019 |
[.xls] |
European Commission/EACEA/Eurydice (forthcoming). Structural Indicators for Monitoring Education and Training Systems in Europe – 2020. |
Figure 79 – Requirements or incentives for work placements for ALL students |
[.xls] |
European Commission/EACEA/Eurydice. |
Figure 80 – Measures to support the participation of disadvantaged learners in learning mobility, EU-27
countries, 2018/2019 |
[.xls] |
European Commission/EACEA/Eurydice. |
All figures |
[.zip] |