PISA 2012 Results: Excellence through Equity

Transcription

1 PISA 2012 Results: Excellence through Equity Giving Every Student the Chance to Succeed Volume II Programme for International Student Assessment

2

3 PISA 2012 Results: Excellence Through Equity Giving Every Student The Chance To Succeed (Volume II)

4 This work is published on the responsibility of the Secretary-General of the. The opinions expressed and arguments employed herein do not necessarily reflect the official views of the Organisation or of the governments of its member countries. This document and any map included herein are without prejudice to the status of or sovereignty over any territory, to the delimitation of international frontiers and boundaries and to the name of any territory, city or area. Please cite this publication as: (2013), PISA 2012 Results: Excellence Through Equity: Giving Every Student the Chance to Succeed (Volume II), PISA, Publishing. ISBN (print) ISBN (PDF) Note by Turkey: The information in this document with reference to Cyprus relates to the southern part of the Island. There is no single authority representing both Turkish and Greek Cypriot people on the Island. Turkey recognises the Turkish Republic of Northern Cyprus (TRNC). Until a lasting and equitable solution is found within the context of the United Nations, Turkey shall preserve its position concerning the Cyprus issue. Note by all the European Union Member States of the and the European Union: The Republic of Cyprus is recognised by all members of the United Nations with the exception of Turkey. The information in this document relates to the area under the effective control of the Government of the Republic of Cyprus. The statistical data for Israel are supplied by and under the responsibility of the relevant Israeli authorities. The use of such data by the is without prejudice to the status of the Golan Heights, East Jerusalem and Israeli settlements in the West Bank under the terms of international law. Photo credits: Flying Colours Ltd /Getty Images Jacobs Stock Photography /Kzenon khoa vu /Flickr/Getty Images Mel Curtis /Corbis Shutterstock /Kzenon Simon Jarratt /Corbis Corrigenda to publications may be found on line at: You can copy, download or print content for your own use, and you can include excerpts from publications, databases and multimedia products in your own documents, presentations, blogs, websites and teaching materials, provided that suitable acknowledgement of as source and copyright owner is given. All requests for public or commercial use and translation rights should be submitted to rights@oecd.org. Requests for permission to photocopy portions of this material for public or commercial use shall be addressed directly to the Copyright Clearance Center (CCC) at info@copyright.com or the Centre français d exploitation du droit de copie (CFC) at contact@cfcopies.com.

5 Foreword Equipping citizens with the skills necessary to achieve their full potential, participate in an increasingly interconnected global economy, and ultimately convert better jobs into better lives is a central preoccupation of policy makers around the world. Results from the s recent Survey of Adult Skills show that highly skilled adults are twice as likely to be employed and almost three times more likely to earn an above-median salary than poorly skilled adults. In other words, poor skills severely limit people s access to better-paying and more rewarding jobs. Highly skilled people are also more likely to volunteer, see themselves as actors rather than as objects of political processes, and are more likely to trust others. Fairness, integrity and inclusiveness in public policy thus all hinge on the skills of citizens. The ongoing economic crisis has only increased the urgency of investing in the acquisition and development of citizens skills both through the education system and in the workplace. At a time when public budgets are tight and there is little room for further monetary and fiscal stimulus, investing in structural reforms to boost productivity, such as education and skills development, is key to future growth. Indeed, investment in these areas is essential to support the recovery, as well as to address long-standing issues such as youth unemployment and gender inequality. In this context, more and more countries are looking beyond their own borders for evidence of the most successful and efficient policies and practices. Indeed, in a global economy, success is no longer measured against national standards alone, but against the best-performing and most rapidly improving education systems. Over the past decade, the Programme for International Student Assessment, PISA, has become the world s premier yardstick for evaluating the quality, equity and efficiency of school systems. But the evidence base that PISA has produced goes well beyond statistical benchmarking. By identifying the characteristics of high-performing education systems PISA allows governments and educators to identify effective policies that they can then adapt to their local contexts. The results from the PISA 2012 assessment, which was conducted at a time when many of the 65 participating countries and economies were grappling with the effects of the crisis, reveal wide differences in education outcomes, both within and across countries. Using the data collected in previous PISA rounds, we have been able to track the evolution of student performance over time and across subjects. Of the 64 countries and economies with comparable data, 40 improved their average performance in at least one subject. Top performers such as Shanghai in China or Singapore were able to further extend their lead, while countries like Brazil, Mexico, Tunisia and Turkey achieved major improvements from previously low levels of performance. Some education systems have demonstrated that it is possible to secure strong and equitable learning outcomes at the same time as achieving rapid improvements. Of the 13 countries and economies that significantly improved their mathematics performance between 2003 and 2012, three also show improvements in equity in education during the same period, and another nine improved their performance while maintaining an already high level of equity proving that countries do not have to sacrifice high performance to achieve equity in education opportunities. Nonetheless, PISA 2012 results show wide differences between countries in mathematics performance. The equivalent of almost six years of schooling, 245 score points, separates the highest and lowest average performances Excellence through Equity: Giving every Student the Chance to Succeed Volume II

6 Foreword of the countries that took part in the PISA 2012 mathematics assessment. The difference in mathematics performances within countries is even greater, with over 300 points the equivalent of more than seven years of schooling often separating the highest- and the lowest-achieving students in a country. Clearly, all countries and economies have excellent students, but few have enabled all students to excel. The report also reveals worrying gender differences in students attitudes towards mathematics: even when girls perform as well as boys in mathematics, they report less perseverance, less motivation to learn mathematics, less belief in their own mathematics skills, and higher levels of anxiety about mathematics. While the average girl underperforms in mathematics compared with the average boy, the gender gap in favour of boys is even wider among the highest-achieving students. These findings have serious implications not only for higher education, where young women are already underrepresented in the science, technology, engineering and mathematics fields of study, but also later on, when these young women enter the labour market. This confirms the findings of the Gender Strategy, which identifies some of the factors that create and widen the gender gap in education, labour and entrepreneurship. Supporting girls positive attitudes towards and investment in learning mathematics will go a long way towards narrowing this gap. PISA 2012 also finds that the highest-performing school systems are those that allocate educational resources more equitably among advantaged and disadvantaged schools and that grant more autonomy over curricula and assessments to individual schools. A belief that all students can achieve at a high level and a willingness to engage all stakeholders in education including students, through such channels as seeking student feedback on teaching practices are hallmarks of successful school systems. PISA is not only an accurate indicator of students abilities to participate fully in society after compulsory school, but also a powerful tool that countries and economies can use to fine-tune their education policies. There is no single combination of policies and practices that will work for everyone, everywhere. Every country has room for improvement, even the top performers. That s why the produces this triennial report on the state of education across the globe: to share evidence of the best policies and practices and to offer our timely and targeted support to help countries provide the best education possible for all of their students. With high levels of youth unemployment, rising inequality, a significant gender gap, and an urgent need to boost growth in many countries, we have no time to lose. The stands ready to support policy makers in this challenging and crucial endeavour. Angel Gurría Secretary-General Excellence through Equity: Giving every Student the Chance to Succeed Volume II

7 Acknowledgements This report is the product of a collaborative effort between the countries participating in PISA, the experts and institutions working within the framework of the PISA Consortium, and the Secretariat. The report was drafted by Andreas Schleicher, Francesco Avvisati, Francesca Borgonovi, Miyako Ikeda, Hiromichi Katayama, Flore-Anne Messy, Chiara Monticone, Guillermo Montt, Sophie Vayssettes and Pablo Zoido of the Directorate for Education and Skills and the Directorate for Financial Affairs, with statistical support from Simone Bloem and Giannina Rech and editorial oversight by Marilyn Achiron. Additional analytical and editorial support was provided by Adele Atkinson, Jonas Bertling, Marika Boiron, Célia Braga-Schich, Tracey Burns, Michael Davidson, Cassandra Davis, Elizabeth Del Bourgo, John A. Dossey, Joachim Funke, Samuel Greiff, Tue Halgreen, Ben Jensen, Eckhard Klieme, André Laboul, Henry Levin, Juliette Mendelovits, Tadakazu Miki, Christian Monseur, Simon Normandeau, Mathilde Overduin, Elodie Pools, Dara Ramalingam, William H. Schmidt (whose work was supported by the Thomas J. Alexander fellowship programme), Kaye Stacey, Lazar Stankov, Ross Turner, Elisabeth Villoutreix and Allan Wigfield. The system level data collection was conducted by the NESLI (INES Network for the Collection and Adjudication of System-Level Descriptive Information on Educational Structures, Policies and Practices) team: Bonifacio Agapin, Estelle Herbaut and Jean Yip. Volume II also draws on the analytic work undertaken by Jaap Scheerens and Douglas Willms in the context of PISA Administrative support was provided by Claire Chetcuti, Juliet Evans, Jennah Huxley and Diana Tramontano. The contracted the Australian Council for Educational Research (ACER) to manage the development of the mathematics, problem solving and financial literacy frameworks for PISA Achieve was also contracted by the to develop the mathematics framework with ACER. The expert group that guided the preparation of the mathematics assessment framework and instruments was chaired by Kaye Stacey; Joachim Funke chaired the expert group that guided the preparation of the problem-solving assessment framework and instruments; and Annamaria Lusardi led the expert group that guided the preparation of the financial literacy assessment framework and instruments. The PISA assessment instruments and the data underlying the report were prepared by the PISA Consortium, under the direction of Raymond Adams at ACER. The development of the report was steered by the PISA Governing Board, which is chaired by Lorna Bertrand (United Kingdom), with Benő Csapó (Hungary), Daniel McGrath (United States) and Ryo Watanabe (Japan) as vice chairs. Annex C of the volumes lists the members of the various PISA bodies, as well as the individual experts and consultants who have contributed to this report and to PISA in general. Excellence through Equity: Giving every Student the Chance to Succeed Volume II

8

9 Table of Contents Executive Summary...13 Reader s Guide...17 What is PISA?...19 Chapter 1 Defining and Measuring Equity in Education...25 How PISA examines equity in education opportunities...28 The quantity and quality of educational resources...28 Instructional content and practices...29 Combining better performance with greater equity...29 Examining equity throughout this report...30 Chapter 2 Equity in Outcomes...33 Performance and socio-economic status across school systems...34 Disparities in performance related to socio-economic status...38 Resilient students...40 Mean performance, after taking account of socio-economic status...40 Between-school variation in performance...44 Performance differences across schools and socio-economic disparities...46 Providing access to schooling to all 15-year-olds...53 Trends in equity between PISA 2003 and PISA Trends in the profile and distribution of students in schools...59 Chapter 3 The Challenge of Diversity...63 Family structure and student performance...64 Parents job status: Targeting education policies through social policy for the unemployed...66 School location and variation in performance across geographical areas...69 Equity in outcomes for immigrant students...71 The impact of other social policies on the profile of immigrant students...72 High levels of performance across a diverse student population...72 Higher levels of performance among an increasingly diverse student population...74 Language minorities among immigrant students...79 First- and second-generation students...80 The late-arrival penalty...80 Concentration of disadvantage...82 Performance, immigrant status and country of origin...84 Chapter 4 Equity in Opportunities to Learn and in Resources...87 Disparities in exposure to formal mathematics, socio-economic status and performance...90 Disparities within countries...91 Differences in exposure to mathematics and average mathematics performance across school systems...91 Between-school differences in opportunity to learn, socio-economic status and performance...91 Excellence through Equity: Giving every Student the Chance to Succeed Volume II

10 Table of contents Equity in educational resources...93 More is not always better...95 Challenging school environments...95 Learning opportunities outside school and parents expectations of schools...95 Opportunities, resources, performance and socio-economic status...98 Participation in pre-primary education...99 Chapter 5 Policy Implications of Equity in Education Patterns in the relationship between performance and socio-economic status A disproportionate number of low-performing students Different slopes and strengths of socio-economic gradients Universal policies for countries where performance differences are small and there is a weak relationship between performance and socio-economic status Policies that target socio-economic disadvantage for those countries where there are small performance differences and a strong relationship between performance and socio-economic status Policies that target both performance and socio-economic disadvantage for countries where there are large performance differences and a strong relationship between performance and socio-economic status Large socio-economic disparities Targeting low-performing and socio-economically disadvantaged schools Targeting students within schools Annex A PISA 2012 Technical background Annex A1 Construction of mathematics scales and indices from the student, school and parent context questionnaires Annex A2 The PISA target population, the PISA samples and the definition of schools Annex A3 Technical notes on analyses in this volume Annex A4 Quality assurance Annex A5 Technical details of trends analyses Annex B PISA 2012 Data Annex B1 Results for countries and economies Annex B2 Results for regions within countries Annex B3 List of tables available on line Annex C THE DEVELOPMENT AND IMPLEMENTATION OF PISA A COLLABORATIVE EFFORT Excellence through Equity: Giving every Student the Chance to Succeed Volume II

11 Table of contents BOXES Box II.1.1. What do the PISA scores mean?...28 Box II.2.1. What is socio-economic status and how is it measured?...37 Box II.2.2. Geographic location, regions and variation in student performance...44 Box II.2.3. What are the main characteristics of students attending socio economically advantaged and disadvantaged schools?...49 Box II.2.4. Improving in PISA: Mexico...54 Box II.2.5. Comparing indices in PISA 2003 and PISA Box II.3.1. Population relevance or attributable risk...66 Box II.3.2. Improving in PISA: Germany...76 Box II.3.3. Language minorities among non-immigrant students...78 Box II.5.1. A framework of policies to improve performance and equity in education FIGURES Map of PISA countries and economies...20 Figure II.1.1 Likelihood of positive social and economic outcomes among highly literate adults...26 Figure II.1.2 Student performance and equity...27 Figure II.2.1 Students socio-economic status and performance, countries...35 Figure II.2.2 Comparing countries performance in mathematics and equity in education outcomes...36 Figure II.2.3 Proportion of the variation in mathematics performance explained by elements of socio economic status...39 Figure II.2.4 Percentage of resilient students...41 Figure II.2.5 Mean mathematics performance, before and after accounting for countries / economies socio economic profile...42 Figure II.2.6 Mean mathematics performance, by national quarter of socio-economic status...43 Figure II.2.a Mean mathematics performance in countries and regions...45 Figure II.2.7 Total variation in mathematics performance and variation between and within schools...47 Figure II.2.8 Performance differences between and within schools explained by students and schools socio economic status...48 Figure II.2.9 Performance differences within and between schools across socio-economic groups...50 Figure II.2.10 Mathematics performance of students in socio-economically advantaged, average and disadvantaged schools...51 Figure II.2.11 Distribution of students across school performance and socio-economic profile...52 Figure II.2.12 Change between 2003 and 2012 in the strength of the socio-economic gradient and annualised mathematics performance...57 Figure II.2.13 Change between 2003 and 2012 in the slope of the socio-economic gradient and annualised mathematics performance...58 Figure II.2.14 Change between 2003 and 2012 in student resiliency to socio-economic status...59 Figure II.2.15 Change between 2003 and 2012 in social inclusion...60 Figure II.3.1 Difference in mathematics performance, by type of family...65 Figure II.3.2 Difference in mathematics performance, by parents work status...67 Figure II.3.3 Mean mathematics performance, by school location, after accounting for socio-economic status...70 Figure II.3.4 Difference in mathematics performance between immigrant and non-immigrant students...73 Figure II.3.5 Change between 2003 and 2012 in immigrant students mathematics performance...75 Figure II.3.6 Change between 2003 and 2012 in the share of students with an immigrant background...76 Figure II.3.7 Difference in mathematics performance, by immigrant and language background...79 Figure II.3.8 Percentage of students with mathematics performance below and above the baseline level (Level 2), by immigrant background...81 Figure II.3.9 Proportion of immigrant students in socio-economically disadvantaged, average and advantaged schools...83 Excellence through Equity: Giving every Student the Chance to Succeed Volume II

12 Table of contents Figure II.4.1 Summary of PISA measures of equity in exposure to formal mathematics...89 Figure II.4.2 Magnitude of performance differences related to students exposure to formal mathematics, by schools socio-economic profile...90 Figure II.4.3 Relationship between mathematics performance and variation in students exposure to formal mathematics...92 Figure II.4.4 Between-school differences in exposure to formal mathematics, socio-economic status and performance...93 Figure II.4.5 Summary of PISA measures of equity in educational resources...94 Figure II.4.6 Teacher quantity and quality, by schools socio-economic profile...96 Figure II.4.7 Differences in teacher quality explained by students and schools socio-economic profile...97 Figure II.4.8 Differences in disciplinary climate, by schools socio-economic profile...97 Figure II.4.9 Differences in disciplinary climate explained by students and schools socio-economic profile...98 Figure II.4.10 Performance differences related to differences in exposure to formal mathematics to learn and resources...99 Figure II.4.11 Differences in mathematics performance, by attendance at pre-primary school Figure II.4.12 Pre-primary school, mathematics performance and students socio-economic status Figure II.5.1a Summary of PISA measures of equity in education Figure II.5.1b Summary of PISA measures of equity in education (continued) Figure II.5.2 Figure II.5.3 Figure II.5.4 Figure II.5.5 Figure II.5.6 Figure II.5.7 Figure II.5.8 Figure II.5.9 Figure II.5.10 Figure II.5.11 Figure II.5.12 Relationship between school performance and schools socio-economic profile in countries that have large proportions of students performing below Level 2: Indonesia, Kazakhstan, Peru and Tunisia Relationship between school performance and schools socio-economic profile in countries with high performance and flat and weak gradients: Canada and Viet Nam Relationship between school performance and schools socio-economic profile in countries with average or low performance and flat and weak gradients: Brazil, Italy, Mexico and the United States Relationship between school performance and schools socio-economic profile with average or low performance and flat and strong gradients: Chile, Greece, Malaysia and Turkey Relationship between school performance and schools socio-economic profile in countries with high performance and steep and strong gradients: Germany and New Zealand Relationship between school performance and schools socio-economic profile in countries with low performance and steep and strong gradients: Hungary, Israel, Singapore and the Slovak Republic Relationship between school performance and schools socio-economic profile in countries with marked socio-economic disparities and average equity in outcomes: Bulgaria, Colombia, Costa Rica and Ireland Relationship between school performance and schools socio-economic profile in countries with high equity but marked socio-economic disparities: Hong Kong China, Jordan, Qatar and Thailand Relationship between school performance and schools socio-economic profiles in countries with strong between-school gradients: Iceland and Uruguay Relationship between school performance and schools socio-economic profile in countries with weak between-school gradients: Norway and Portugal Relationship between school performance and schools socio-economic profile in countries with marked performance differences within schools: Denmark, Finland, Spain and Switzerland Figure II.5.13 Relationship between school performance and schools socio-economic profile for all other countries and economies Figure A.3.1 Labels used in a two-way table Figure A5.1 Figure A5.2 Figure A5.3 Annualised change in mathematics performance since PISA 2003 and observed differences in performance between PISA 2012 and PISA Annualised change in reading performance since PISA 2000 and observed differences in performance between PISA 2012 and PISA Annualised change in science performance since PISA 2006 and observed differences in performance between PISA 2012 and PISA TABLES Table II.A Snapshot of equity in education in PISA 2012 and change since PISA Table A1.1 Levels of parental education converted into years of schooling Table A1.2 A multilevel model to estimate grade effects in mathematics accounting for some background variables Table A1.3 Student questionnaire rotation design Excellence through Equity: Giving every Student the Chance to Succeed Volume II

13 Table of contents Table A2.1 PISA target populations and samples Table A2.2 Exclusions Table A2.3 Response rates Table A2.4a Percentage of students at each grade level Table A2.4b Percentage of students at each grade level, by gender Table A5.1 Link error for comparisons of performance between PISA 2012 and previous assessments Table A5.2 Link error for comparisons of proficiency levels between PISA 2012 and previous assessments Table A5.3 Link error for comparisons of annualised and curvilinear change between PISA 2012 and previous assessments Table II.2.1 Relationship between performance in mathematics, reading and science, and socio-economic status Table II.2.2 Elements of socio-economic status, by quarters of socio-economic status within countries Table II.2.3 Elements of socio-economic status across countries Table II.2.4a Students socio-economic status and mathematics performance Table II.2.4b Change between 2003 and 2012 in students socio-economic status and mathematics performance Table II.2.5 Performance and selected elements of socio-economic status across countries Table II.2.6 Relationship between mathematics performance and elements of socio-economic status Table II.2.7a Percentage of resilient students and low-achievers among disadvantaged students, by gender Table II.2.7b Change between 2003 and 2012 in the percentage of resilient students among disadvantaged students, by gender Table II.2.8a Variation in mathematics performance Table II.2.8b Change between 2003 and 2012 in the variation in mathematics performance Table II.2.9a Relationship between mathematics performance and socio-economic status, between and within schools Table II.2.9b Change between 2003 and 2012 in the relationship between mathematics performance and socio economic status, between and within schools Table II.2.10 Parents education and occupation, and students home possessions, by schools socio-economic profile Table II.2.11 School performance and schools socio-economic profile Table II.2.12 Change between 2003 and 2012 in enrolment of 15-year-olds Table II.2.13a Students socio-economic status Table II.2.13b Change between 2003 and 2012 in the socio-economic status of students Table II.3.1 Mathematics performance and type of family Table II.3.2 Mathematics performance, parents work status and socio-economic status Table II.3.3a Relationship between mathematics performance and school location Table II.3.3b Change between 2003 and 2012 in the relationship between mathematics performance and school location Table II.3.4a Mathematics performance and immigrant background Table II.3.4b Change between 2003 and 2012 in the relationship between mathematics performance and immigrant background Table II.3.5 Mathematics performance, immigrant background and language spoken at home Table II.3.6a Mathematics performance and immigrant background for first- and second-generation students Table II.3.6b Change between 2003 and 2012 in the relationship between mathematics performance and immigrant background for first- and second-generation students Table II.3.7 Proficiency levels in mathematics, by immigrant background for first- and second-generation students Table II.3.8 Mathematics performance, first-generation immigrant students, and age at arrival Table II.3.9 Concentration of immigrant students in school Table II.3.10 Concentration, in school, of students who do not speak the language of assessment at home Table II.3.11 Host country/economy, country/economy of origin and mathematics performance Table II.4.1 Equity in opportunity to learn: Formal mathematics Table II.4.2 Mathematics performance and student population, by schools socio-economic profile Table II.4.3 Inequity in access to instructional content: Formal mathematics Excellence through Equity: Giving every Student the Chance to Succeed Volume II

14 Table of contents Table II.4.4 Correlation between student performance and selected student and school characteristics Table II.4.5 Correlation between student socio-economic status and selected student and school characteristics Table II.4.6 Correlation between school socio-economic profile and selected student and school characteristics Table II.4.7 Correlation of the variation of students socio-economic status within a school and selected student and school characteristics Table II.4.8 Inequity in access to educational resources: Student-teacher ratio Table II.4.9 Inequity in access to educational resources: Percentage of teachers with university-level qualifications Table II.4.10 Inequity in access to educational resources: Disciplinary climate Table II.4.11 Impact of socio-economic status after accounting for student characteristics and educational resources at school Table II.4.12 Pre-primary school attendance, mathematics performance and students socio-economic status Table II.4.13 Relationship between performance, pre-primary school attendance and socio-economic status Table II.4.14 Relationship between performance, pre-primary school attendance and immigrant background Table B2.II.1 Relationship between mathematics performance and socio-economic background (the socio economic gradient), by region Table B2.II.2 Students socio-economic status and mathematics performance, by region Table B2.II.3 Relationship between mathematics performance and the individual elements of socio-economic background, by region Table B2.II.4 Percentage of resilient students and low-achievers among disadvantaged students in PISA 2012, by gender and region Table B2.II.5 Variation in mathematics performance in PISA 2012, by region Table B2.II.6 Relationship between performance in mathematics and socio-economic status, between and within schools, by region Table B2.II.7 The socio-economic background of students in PISA 2012, by region Table B2.II.9 Mathematics performance and immigrant background, by region Table B2.II.15 Mathematics performance and student population, by schools socio-economic profile and region Table B2.II.16 Inequity in access to instructional content: Formal mathematics, by region Table B2.II.24 Pre-primary school attendance, mathematics performance and students socio-economic status, by region Table B2.II.25 Variation in mathematics performance across students, schools and regions This book has... StatLinks 2 A service that delivers Excel files from the printed page! Look for the StatLinks at the bottom left-hand corner of the tables or graphs in this book. To download the matching Excel spreadsheet, just type the link into your Internet browser, starting with the prefix. If you re reading the PDF e-book edition, and your PC is connected to the Internet, simply click on the link. You ll find StatLinks appearing in more books Excellence through Equity: Giving every Student the Chance to Succeed Volume II

15 Executive Summary PISA defines equity in education as providing all students, regardless of gender, family background or socio-economic status, with opportunities to benefit from education. Defined in this way, equity does not imply that everyone should have the same results. It does mean, however, that students socio-economic status or the fact that they have an immigrant background has little or no impact on their performance, and that all students, regardless of their background, are offered access to quality educational resources and opportunities to learn. Of the 39 countries and economies that participated in both PISA 2003 and 2012, Mexico, Turkey and Germany improved both their mathematics performance and their levels of equity in education during the period. These three countries improved both equity and performance either by reducing the extent to which students socioeconomic background predicts their mathematics performance or by reducing the average difference in performance between advantaged and disadvantaged students. Nine additional countries and economies improved their average performance between 2003 and 2012 while maintaining their equity levels. Australia, Canada, Estonia, Finland, Hong Kong-China, Japan, Korea, Liechtenstein and Macao-China achieve high levels of performance and equity in education opportunities as assessed in PISA Equity in education opportunities is average in 12 countries and economies and below average in two of the 23 countries and economies that perform above the average in mathematics. In all countries and economies that participated in PISA 2012, a student s socio-economic status has a strong impact on his or her performance. Across countries, 15% of the variation in student performance in mathematics is attributed to differences in students socio-economic status. Among high-performing countries, this proportion ranges from 3% in Macao-China to 18% in New Zealand. In contrast, in Bulgaria, Chile, France, Hungary, Peru, the Slovak Republic and Uruguay more than 20% of the difference in student performance can be attributed to students socio-economic status. Across countries, a more socio-economically advantaged student scores 39 points higher in mathematics the equivalent of nearly one year of schooling than a less-advantaged student. Among the 23 highest-performing countries and economies, performance differences related to socio-economic status are narrower-than-average in Canada, Estonia, Finland, Hong Kong-China, Macao-China and Viet Nam, about average in 11 countries and economies, and wider-than-average in 6. Striking performance differences are also observed between students in advantaged schools and those in disadvantaged schools: students attending socioeconomically advantaged schools outscore those in disadvantaged schools by more than 104 points in mathematics, on average across countries. Across countries 6% of the entire student population are resilient, meaning that they beat the socioeconomic odds against them and exceed expectations, when compared with students in other countries. In Hong Kong-China, Korea, Macao-China, Shanghai-China, Singapore and Viet Nam 13% or more of the overall student population are resilient and perform among the top 25% across all participating countries after taking socio-economic status into account. Between 2003 and 2012, the share of resilient students increased in Germany, Italy, Poland, Tunisia and Turkey. Excellence through Equity: Giving every Student the Chance to Succeed Volume II

16 Executive Summary The share of immigrant students in countries increased from 9% in 2003 to 12% in 2012 while the difference in mathematics performance between immigrant and non-immigrant students shrank by 11 score points during the same period. Immigrant students tend to be socio-economically disadvantaged in comparison to non-immigrant students, yet even when comparing students of similar socio-economic status, immigrant students perform worse in mathematics than non immigrant students. In 2012, they scored an average of 33 points lower in mathematics than non-immigrant students before accounting for socio-economic status, and an average of 21 points lower after accounting for socio economic status. In Canada, Ireland, Israel, New Zealand and the United Kingdom, however, immigrant and non-immigrant students perform equally well. The concentration of immigrant students in a school is not, in itself, associated with poor performance. In general, immigrant students and those who do not speak the language of assessment at home tend to be concentrated in disadvantaged schools. In the United States, for example, 40% of students in disadvantaged schools are immigrants, whereas 13% of students in advantaged schools are. Across countries, students who attend schools where more than one in four students are immigrants tend to perform worse than those in schools with no immigrant students. However, the 19 score-point difference between the two groups is more than halved to 8 points after the socio economic status of the students and schools is taken into account. Belgium, Estonia, Greece, Mexico and Portugal are the only countries where there are performance differences of 20 score points or more between the two groups, after accounting for socio economic status. Across countries, students who reported that they had attended pre-primary school for more than one year score 53 points higher in mathematics the equivalent of more than one years of schooling than students who had not attended pre-primary education. In all but two countries with available data, students who had attended pre-primary education for more than one year outperformed students who had not, after taking socio-economic status into account. This finding remains unchanged even after socio-economic status is accounted for, except in Croatia, Estonia, Ireland, Korea, Latvia, Slovenia and the United States. countries allocate at least an equal, if not a larger, number of mathematics teachers to socio-economically disadvantaged schools as to advantaged schools; but disadvantaged schools tend to have great difficulty in attracting qualified teachers. In the Netherlands, the proportion of qualified teachers in advantaged schools (52%) is three times larger than the proportion of qualified teachers in disadvantaged schools (14%), while the student-teacher ratio is 28% higher in advantaged schools than it is in disadvantaged schools (18 students compared with 14 students per teacher, respectively) Excellence through Equity: Giving every Student the Chance to Succeed Volume II

17 Executive Summary Table II.A [1/2] Snapshot of equity in education in PISA 2012 and change since PISA 2003 Countries/economies with mean mathematics performance above the average Countries/economies where the strength of the relationship between mathematics performance and socio-economic status is below the average Countries/economies where performance differences across socio-economic groups are below the average Countries/economies with mean mathematics performance not statistically different from the average Countries/economies where the strength of the relationship between mathematics performance and socio-economic status is not statistically different from the average Countries/economies where performance differences across socio-economic spectrum are not statistically different from the average Countries/economies with mean mathematics performance below the average Countries/economies where the strength of the relationship between mathematics performance and socio-economic status is above the average Countries/economies where performance differences across socio-economic spectrum are above the average Mean performance in mathematics Strength of the relationship between mathematics performance and socio-economic status Performance difference across socio-economic groups Score-point difference in mathematics associated with one unit increase in ESCS 1 Percentage of resilient students Percentage of disadvantaged students who perform among the top 25% of students across all participating countries and economies, after accounting for ESCS 1 Percentage of explained variance Mean score in mathematics performance average Macao-China Hong Kong-China Liechtenstein Estonia Finland Canada Japan Korea Netherlands Australia Switzerland Singapore Ireland Viet Nam Belgium Shanghai-China Slovenia Austria Denmark Poland Germany Chinese Taipei New Zealand Norway Iceland United Kingdom Latvia Czech Republic Portugal France Qatar Kazakhstan Jordan Indonesia United Arab Emirates Thailand Italy Mexico Sweden Russian Federation Serbia Croatia Tunisia Montenegro Malaysia Lithuania Cyprus* Turkey United States Argentina Colombia Greece Brazil Spain Israel Luxembourg Costa Rica Romania Bulgaria Uruguay Hungary Chile Peru Slovak Republic Note: Countries/economies in which the change between PISA 2003 and PISA 2012 ( ) is statistically significant are marked in bold. 1. ESCS refers to the PISA index of economic, social and cultural status. Countries and economies are presented in three groups: those whose mean performance is above the average, those whose mean performance is not statistically different from the average, and those whose mean performance is below the average. Within each group, countries and economies are ranked in ascending order of the strength of the relationship between performance and socio-economic status observed in PISA * See notes in the Reader s Guide. Source:, PISA 2012 Database, Tables II.2.1, II.2.7a, II.2.7b, II.2.8b and II.2.9b Excellence through Equity: Giving every Student the Chance to Succeed Volume II

18 Executive Summary Table II.A [2/2] Snapshot of equity in education in PISA 2012 and change since PISA 2003 Countries/economies with mean mathematics performance above the average Countries/economies where the strength of the relationship between mathematics performance and socio-economic status is below the average Countries/economies where performance differences across socio-economic groups are below the average Countries/economies with mean mathematics performance not statistically different from the average Countries/economies where the strength of the relationship between mathematics performance and socio-economic status is not statistically different from the average Countries/economies where performance differences across socio-economic spectrum are not statistically different from the average Countries/economies with mean mathematics performance below the average Countries/economies where the strength of the relationship between mathematics performance and socio-economic status is above the average Countries/economies where performance differences across socio-economic spectrum are above the average Trends in mathematics performance Change between PISA 2003 and PISA 2012 in mathematics mean score ( ) Trends in the strength of the relationship between mathematics performance and socio-economic status Change between PISA 2003 and PISA 2012 in the percentage of variance in mathematics performance explained by students ESCS 1 ( ) Trends in the slope of the socio-economic gradient for mathematics Change between PISA 2003 and PISA 2012 in the score-point difference in mathematics performance associated with one unit increase on ESCS 1 ( ) Trends in the percentage of resilient students Change between PISA 2003 and PISA 2012 in the percentage of resilient students ( ) average Macao-China Hong Kong-China Liechtenstein c Estonia m m m m Finland Canada Japan Korea Netherlands Australia Switzerland Singapore m m m m Ireland Viet Nam m m m m Belgium Shanghai-China m m m m Slovenia m m m m Austria Denmark Poland Germany Chinese Taipei m m m m New Zealand Norway Iceland United Kingdom m m m m Latvia Czech Republic Portugal France Qatar m m m m Kazakhstan m m m m Jordan m m m m Indonesia United Arab Emirates m m m m Thailand Italy Mexico Sweden Russian Federation Serbia m m m m Croatia m m m m Tunisia Montenegro m m m m Malaysia m m m m Lithuania m m m m Cyprus* m m m m Turkey United States Argentina m m m m Colombia m m m m Greece Brazil Spain Israel m m m m Luxembourg Costa Rica m m m m Romania m m m m Bulgaria m m m m Uruguay Hungary Chile m m m m Peru m m m m Slovak Republic Note: Countries/economies in which the change between PISA 2003 and PISA 2012 ( ) is statistically significant are marked in bold. 1. ESCS refers to the PISA index of economic, social and cultural status. Countries and economies are presented in three groups: those whose mean performance is above the average, those whose mean performance is not statistically different from the average, and those whose mean performance is below the average. Within each group, countries and economies are ranked in ascending order of the strength of the relationship between performance and socio-economic status observed in PISA * See notes in the Reader s Guide. Source:, PISA 2012 Database, Tables II.2.1, II.2.7a, II.2.7b, II.2.8b and II.2.9b Excellence through Equity: Giving every Student the Chance to Succeed Volume II

19 Reader s Guide Data underlying the figures The data referred to in this volume are presented in Annex B and, in greater detail, including some additional tables, on the PISA website ( Four symbols are used to denote missing data: a The category does not apply in the country concerned. Data are therefore missing. c There are too few observations or no observation to provide reliable estimates (i.e. there are fewer than 30 students or fewer than 5 schools with valid data). m Data are not available. These data were not submitted by the country or were collected but subsequently removed from the publication for technical reasons. w Data have been withdrawn or have not been collected at the request of the country concerned. Country coverage This publication features data on 65 countries and economies, including all 34 countries and 31 partner countries and economies (see map in the section What is PISA?). The statistical data for Israel are supplied by and under the responsibility of the relevant Israeli authorities. The use of such data by the is without prejudice to the status of the Golan Heights, East Jerusalem and Israeli settlements in the West Bank under the terms of international law. Two notes were added to the statistical data related to Cyprus: 1. Note by Turkey: The information in this document with reference to Cyprus relates to the southern part of the Island. There is no single authority representing both Turkish and Greek Cypriot people on the Island. Turkey recognises the Turkish Republic of Northern Cyprus (TRNC). Until a lasting and equitable solution is found within the context of the United Nations, Turkey shall preserve its position concerning the Cyprus issue. 2. Note by all the European Union Member States of the and the European Union: The Republic of Cyprus is recognised by all members of the United Nations with the exception of Turkey. The information in this document relates to the area under the effective control of the Government of the Republic of Cyprus. Calculating international averages An average corresponding to the arithmetic mean of the respective country estimates was calculated for most indicators presented in this report. The average is used to compare performance across school systems. In the case of some countries, data may not be available for specific indicators, or specific categories may not apply. Readers should, therefore, keep in mind that the term average refers to the countries included in the respective comparisons. Rounding figures Because of rounding, some figures in tables may not exactly add up to the totals. Totals, differences and averages are always calculated on the basis of exact numbers and are rounded only after calculation. All standard errors in this publication have been rounded to one or two decimal places. Where the value 0.0 or 0.00 is shown, this does not imply that the standard error is zero, but that it is smaller than 0.05 or 0.005, respectively. Excellence through Equity: Giving every Student the Chance to Succeed Volume II

20 Reader s Guide Reporting student data The report uses 15-year-olds as shorthand for the PISA target population. PISA covers students who are aged between 15 years 3 months and 16 years 2 months at the time of assessment and who are enrolled in school and have completed at least 6 years of formal schooling, regardless of the type of institution in which they are enrolled and of whether they are in full-time or part-time education, of whether they attend academic or vocational programmes, and of whether they attend public or private schools or foreign schools within the country. Reporting school data The principals of the schools in which students were assessed provided information on their schools characteristics by completing a school questionnaire. Where responses from school principals are presented in this publication, they are weighted so that they are proportionate to the number of 15-year-olds enrolled in the school. Focusing on statistically significant differences This volume discusses only statistically significant differences or changes. These are denoted in darker colours in figures and in bold font in tables. See Annex A3 for further information. Abbreviations used in this report ESCS PISA index of economic, social and cultural status PPP Purchasing power parity GDP Gross domestic product S.D. Standard deviation ISCED International Standard Classification of Education S.E. Standard error ISCO International Standard Classification of Occupations STEM Science, Technology, Engineering and Mathematics Further documentation For further information on the PISA assessment instruments and the methods used in PISA, see the PISA 2012 Technical Report (, forthcoming). The reader should note that there are gaps in the numbering of tables because some tables appear on line only and are not included in this publication. To consult the set of web only data tables, visit the PISA website ( This report uses the StatLinks service. Below each table and chart is a url leading to a corresponding Excel TM workbook containing the underlying data. These urls are stable and will remain unchanged over time. In addition, readers of the e-books will be able to click directly on these links and the workbook will open in a separate window, if their internet browser is open and running Excellence through Equity: Giving every Student the Chance to Succeed Volume II