PISA 2015 Results: Excellence and Equity in Education (Volume I)

494 Pages·2016·8.797 MB·English

by  Organization for Economic Cooperation and Development

PISA 2015 Results
EXCELLENCE AND EQUITY IN EDUCATION
VOLUME I
S
P A
I
P r o g r a m m e f o r I n t e r n a t i o n a l S t u d e n t A s s e s s m e n t

PISA
PISA 2015 Results
(Volume I)
EXCELLENCE AND EQUITY
IN EDUCATION
This work is published under the responsibility of the Secretary-General of the OECD.
The opinions expressed and the arguments employed herein do not necessarily reflect
the official views of the OECD 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.
The statistical data for Israel are supplied by and under the responsibility of the relevant
Israeli authorities. The use of such data by the OECD 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.
Please cite this publication as:
OECD (2016), PISA 2015 Results (Volume I): Excellence and Equity in Education, PISA, OECD Publishing, Paris.
http://dx.doi.org/10.1787/9789264266490-en
ISBN (print) 978-92-64-26732-9
ISBN (PDF) 978-92-64-26649-0
Series: PISA
ISSN (print): 1990-8539
ISSN (on line): 1996-3777
Photo credits:
© Geostock / Getty Images
© Hero Images Inc. / Hero Images Inc. / Corbis
© LIUSHENGFILM / Shutterstock
© RelaXimages / Corbis
© Shutterstock /Kzenon
© Simon Jarratt/Corbis
Corrigenda to OECD publications may be found on line at: www.oecd.org/publishing/corrigenda.
© OECD 2016
This work is available under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 IGO (CC BY-NC-SA 3.0 IGO). For specific
information regarding the scope and terms of the licence as well as possible commercial use of this work or the use of PISA data please
consult Terms and Conditions on www.oecd.org.
Foreword
Equipping citizens with the knowledge and skills necessary to achieve their full potential, contribute to an increasingly
interconnected world, and ultimately convert better skills into better lives is a central preoccupation of policy makers
around the world. Results from the OECD’s Survey of Adult Skills show that highly skilled adults are not only twice as
likely to be employed and almost three times more likely to earn an above-median salary than poorly skilled adults,
they are also more likely to volunteer, to report that they are in good to excellent health, to see themselves as actors
rather than as objects of political processes, and to trust others. Fairness, integrity and inclusiveness in public policy thus
all hinge on the skills of citizens.
In working to achieve these goals, more and more countries are looking beyond their own borders for evidence of the most
successful and efficient education policies and practices. Over the past decade, the OECD 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 latest PISA assessment in 2015 focused on science, a discipline that plays an increasing role in our economic and
social lives. From taking a painkiller to determining what is a “balanced” meal, from drinking pasteurised milk to deciding
whether or not to buy a hybrid car, science is pervasive. And science is not just test tubes and the periodic table; it is
the basis of nearly every tool we use – from a simple can opener to the most advanced space explorer. More important,
science is not only the domain of scientists. In the context of massive information flows and rapid change, everyone now
needs to be able to “think like a scientist”: to be able to weigh evidence and come to a conclusion; to understand that
scientific “truth” may change over time, as new discoveries are made, and as humans develop a greater understanding
of natural forces and of technology’s capacities and limitations.
The last time science was the focus of PISA was in 2006. Since then, science and technology have advanced tremendously.
The smartphone was invented and became ubiquitous. Social media, cloud-based services, robotics and machine learning
have transformed our economic and social life. New possibilities of gene sequencing and genome editing, synthetic biology,
bio-printing or regenerative medicine and brain interfaces are changing life itself. Against this backdrop, and the fact
that expenditure per primary and secondary student rose by almost 20% across OECD countries over this period, it is
disappointing that, for the majority of countries with comparable data, science performance in PISA remained virtually
unchanged since 2006. In fact, only a dozen countries showed measurable improvement in the science performance of
their 15-year-olds, including high-performing education systems, such as Singapore and Macao (China), and low-performing
ones, such as Peru and Colombia.
It is also worrying to see how many young people fail to reach even the most essential learning outcomes.
In September 2015, world leaders gathered in New York to set ambitious goals for the future of the global community.
Goal 4 of the Sustainable Development Goals seeks to ensure “inclusive and equitable quality education and promote
3
PISA 2015 RESULTS (VOLUME I): EXCELLENCE AND EQUITY IN EDUCATION © OECD 2016
FOREWORD
A corrigendum has been issued for this page. See: http://www.oecd.org/about/publishing/Corrigenda-PISA2015-VolumeI.pdf
lifelong learning opportunities for all”. This includes that “all learners acquire the knowledge and skills needed to promote
sustainable development” (Target 4.7). Only in Canada, Estonia, Finland, Hong Kong (China), Japan, Macao (China) and
Singapore do at least four out of five 15-year-old students master the baseline level of proficiency in science, reading
and mathematics. These countries show that there are countries on nearly every continent that could achieve the goal of
universal basic skills by 2030. At the same time, the small group of countries that has moved close to securing at least basic
skills for all shows how much remains to be done in most countries – including some of the wealthiest OECD countries –
to attain the Sustainable Development Goals.
The data also show that the world is no longer divided between rich and well-educated nations and poor and badly
educated ones: the 10% most disadvantaged students in Viet Nam compare favourably to the average student in the
OECD area. Clearly, all countries and economies have excellent students, but few have enabled all students to excel.
Achieving greater equity in education is not only a social justice imperative, it is also a way to use resources more
effectively, increase the supply of skills that fuel economic growth, and promote social cohesion.
PISA also finds varying levels of engagement with science and expectations of science-related careers across students
who are similarly capable and interested in science. In a majority of countries and economies, students from advantaged
backgrounds are more likely to expect a career in science – even among students who perform similarly in science and
who reported similar enjoyment of learning science.
Similarly, while it is encouraging that boys and girls now show similar levels of science performance in PISA, large gender
differences remain in students’ dispositions towards science-related careers, even among students who score similarly in
science and who report similar levels of enjoyment in learning science. In Germany, Hungary and Sweden, for instance,
top-performing boys are significantly more likely than top-performing girls to expect a career requiring further training
in science. These findings have serious implications not only for higher education, where young women are already
under-represented in the science, technology, engineering and mathematics fields of study, but also later on, when these
young women enter the labour market.
Gender stereotypes about scientists and about work in science-related occupations can discourage some students from
engaging further with science. Schools can counter these stereotypes, and help both boys and girls cultivate a wider
perspective on science, including through better career information. Employers and educators in perceived “masculine”
or “feminine” fields can also help eliminate existing stereotypes by underscoring the close inter-relationships among
the numerous fields of science.
The subject of science itself suffers from a stereotyped image. Too often, school science is seen as the first segment of
a (leaky) pipeline that will ultimately select those who will work as scientists and engineers. Not only does the “pipeline”
metaphor discount the many pathways successful scientists have travelled to reach their career goals, it also conveys
a negative image of those who do not end up as scientists and engineers. Because knowledge and understanding of
science is useful well beyond the work of scientists and is, as PISA argues, necessary for full participation in a world
shaped by science-based technology, school science should be promoted more positively – perhaps as a “springboard”
to new sources of interest and enjoyment. Expanding students’ awareness about the utility of science beyond teaching
and research occupations can help build a more inclusive view of science, from which fewer students feel excluded.
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 OECD 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 inclusive growth in many countries, we have no time to lose. The OECD stands
ready to support policy makers in this challenging and crucial endeavour.
Angel Gurría
OECD Secretary-General
4
© OECD 2016 PISA 2015 RESULTS (VOLUME I): EXCELLENCE AND EQUITY IN EDUCATION
Acknowledgements
This report is the product of a collaborative effort between the countries participating in PISA, the national and
international experts and institutions working within the framework of the PISA Consortium, and the OECD Secretariat.
The development of this volume was guided by Andreas Schleicher and Yuri Belfali and managed by Miyako Ikeda.
This volume was drafted by Francesco Avvisati with Carlos González-Sancho and edited by Marilyn Achiron. Statistical
and analytical support was co-ordinated by Giannina Rech and provided by Guillaume Bousquet, Hélène Guillou,
Bonaventura Francesco Pacileo and Judit Pál. Rose Bolognini co-ordinated production and Fung Kwan Tam designed
the publication. Administrative support was provided by Claire Chetcuti, Juliet Evans, Audrey Poupon and Lisa Smadja.
Additional members of the OECD PISA and communications teams who provided analytical and communications
support include Peter Adams, Esther Carvalhaes, Anna Choi, Cassandra Davis, Alfonso Echazarra, Tue Halgreen,
Jeffrey Mo, Chiara Monticone, Mario Piacentini, Shun Shirai, Michael Stevenson, Sophie Vayssettes and Michael Ward.
External consultants for analytical and communications support include Przemyslaw Biecek, Simone Bloem,
Maciej Jakubowski, Bartosz Kondratek, Christian Monseur, Jonathan Osborne, Elodie Pools, Jean-François Rouet,
Matthias Von Davier and Kentaro Yamamoto.
To support the technical implementation of PISA, the OECD contracted an international consortium of institutions and
experts, led by Irwin Kirsch of the Educational Testing Service (ETS). Overall co-ordination of the PISA 2015 assessment,
the development of instruments, and scaling and analysis were managed by Claudia Tamassia of the ETS; development
of the electronic platform was managed by Michael Wagner of the ETS. Development of the science and collaborative
problem-solving frameworks, and adaptation of the frameworks for reading and mathematics, were led by John de
Jong and managed by Catherine Hayes of Pearson. Survey operations were led by Merl Robinson and managed by
Michael Lemay of Westat. Sampling and weighting operations were led by Keith Rust and managed by Sheila Krawchuk
of Westat. Design and development of the questionnaires were led by Eckhard Klieme and managed by Nina Jude
of the Deutsches Institut für Pädagogische Forschung (DIPF).
Jonathan Osborne chaired the expert group that guided the preparation of the science assessment framework
and instruments. This group included Marcus Hammann, Sarah Howie, Jody Clarke-Midura, Robin Millar,
Andrée Tiberghien, Russell Tytler and Darren Wong. Charles Alderson and Jean-Francois Rouet assisted in adapting
the reading framework, and Zbigniew Marciniak, Berinderjeet Kaur and Oh Nam Kwon assisted in adapting the
mathematics framework. David Kaplan chaired the expert group that guided the preparation of the questionnaire
framework and instruments. This group included Eckhard Klieme, Gregory Elacqua, Marit Kjærnsli, Leonidas Kyriakides,
Henry M. Levin, Naomi Miyake, Jonathan Osborne, Kathleen Scalise, Fons van de Vijver and Ludger Woessmann.
Keith Rust chaired the Technical Advisory Group, whose members include Theo Eggen, John de Jong, Jean Dumais,
Cees Glas, David Kaplan, Irwin Kirsch, Christian Monseur, Sophia Rabe-Hesketh, Thierry Rocher, Leslie A. Rutkowski,
Margaret Wu and Kentaro Yamamoto.
5
PISA 2015 RESULTS (VOLUME I): EXCELLENCE AND EQUITY IN EDUCATION © OECD 2016
ACKNOWLEDGEMENTS
The development of the report was steered by the PISA Governing Board, chaired by Lorna Bertrand (United Kingdom),
with Maria Helena Guimarães de Castro (Brazil), Sungsook Kim (Korea) and Dana Kelly (United States) as vice chairs.
Annex D of the volume lists the members of the various PISA bodies, including Governing Board members and
National Project Managers in participating countries and economies, the PISA Consortium, and the individual experts
and consultants who have contributed to PISA in general.
6
© OECD 2016 PISA 2015 RESULTS (VOLUME I): EXCELLENCE AND EQUITY IN EDUCATION
Table of contents
EXECUTIVE SUMMARY ........................................................................................................................................................................................................................17
READER’S GUIDE ....................................................................................................................................................................................................................................19
WHAT IS PISA? ..........................................................................................................................................................................................................................................25
CHAPTER 1 OVERVIEW: EXCELLENCE AND EQUITY IN EDUCATION .........................................................................................................33
CHAPTER 2 SCIENCE PERFORMANCE AMONG 15‑YEAR‑OLDS .....................................................................................................................49
How PISA defines science literacy .............................................................................................................................................................................................50
• The PISA 2015 framework for assessing science literacy .................................................................................................................................51
How the PISA 2015 science results are reported ............................................................................................................................................................56
• How the PISA 2015 science test was designed, analysed and scaled ....................................................................................................56
A context for comparing the science performance of countries and economies .....................................................................................60
Students’ proficiency in science ..................................................................................................................................................................................................64
• Where are the top performers in science? .................................................................................................................................................................77
Gender differences in science performance .......................................................................................................................................................................78
Trends in students’ science performance ..............................................................................................................................................................................78
• Average three-year trend in performance ...................................................................................................................................................................82
• Change in science performance between 2012 and 2015 .............................................................................................................................83
• Average three-year trend in performance, accounting for changes in enrolment rates ...............................................................84
• Average three-year trend in performance, adjusted for demographic changes ...............................................................................85
• Comparing mean science performance between 2006 and 2015 ............................................................................................................87
• Trends in performance among low- and high-achieving students .............................................................................................................87
Students’ performance in different areas of science .....................................................................................................................................................93
• Relative strengths and weaknesses of countries/economies in science competency subscales ............................................95
• Relative strengths and weaknesses of countries/economies in science knowledge subscales ...............................................96
• Relative strengths and weaknesses of countries/economies in science content subscales .......................................................98
Students’ epistemic beliefs about science ............................................................................................................................................................................98
• Average levels of support for scientific approaches to enquiry ................................................................................................................100
CHAPTER 3 STUDENTS’ ATTITUDES TOWARDS SCIENCE AND EXPECTATIONS
OF SCIENCE‑RELATED CAREERS .............................................................................................................................................................................................109
Current and future engagement with science among 15‑year‑olds .................................................................................................................111
• Science-related career expectations............................................................................................................................................................................111
• Students’ participation in science activities ...........................................................................................................................................................118
Motivation for learning science ................................................................................................................................................................................................121
• Enjoyment of science ............................................................................................................................................................................................................121
• Interest in broad science topics ......................................................................................................................................................................................125
• Instrumental motivation to learn science .................................................................................................................................................................128
7
PISA 2015 RESULTS (VOLUME I): EXCELLENCE AND EQUITY IN EDUCATION © OECD 2016
TABLE OF CONTENTS
Nurturing future scientists: The role of skills and motivation ..............................................................................................................................131
Bivariate associations of engagement with science and motivation for learning science with performance ...................133
• Within-country associations with performance ..................................................................................................................................................133
• Associations with performance at the country/economy level .................................................................................................................136
Science self‑efficacy ..........................................................................................................................................................................................................................136
CHAPTER 4 READING PERFORMANCE AMONG 15‑YEAR‑OLDS ...............................................................................................................145
Student proficiency in reading...................................................................................................................................................................................................146
• Average performance in reading ...................................................................................................................................................................................148
• Trends in average reading performance since 2009.........................................................................................................................................148
• Long-term trends in reading since PISA 2000 ......................................................................................................................................................159
Students at the different levels of reading proficiency ............................................................................................................................................161
• Trends in the percentage of low performers and top performers in reading ....................................................................................166
Gender differences in reading performance ....................................................................................................................................................................168
CHAPTER 5 MATHEMATICS PERFORMANCE AMONG 15‑YEAR‑OLDS...................................................................................................175
Student proficiency in mathematics ......................................................................................................................................................................................176
• Average performance in mathematics .......................................................................................................................................................................176
• Trends in average mathematics performance........................................................................................................................................................178
Students at the different levels of mathematics proficiency ...............................................................................................................................190
• Proficiency above the baseline .......................................................................................................................................................................................191
• Proficiency below the baseline ......................................................................................................................................................................................194
• Trends in the percentage of low performers and top performers in mathematics ........................................................................194
• Gender differences in mathematics performance ..............................................................................................................................................196
CHAPTER 6 SOCIO‑ECONOMIC STATUS, STUDENT PERFORMANCE AND STUDENTS’ ATTITUDES
TOWARDS SCIENCE .........................................................................................................................................................................................................................201
How PISA examines inclusion and fairness in education .......................................................................................................................................202
• Defining inclusion and fairness ......................................................................................................................................................................................203
• Performance outcomes examined ................................................................................................................................................................................203
• Socio-economic status and other background characteristics ..................................................................................................................204
• Mediating factors .....................................................................................................................................................................................................................206
Successful performance and equity in education ........................................................................................................................................................206
• National income, spending on education and socio-economic heterogeneity ...........................................................................208
Access to education among 15‑year‑olds ...........................................................................................................................................................................209
• PISA’s population coverage as a measure of inclusion in education ....................................................................................................210
• Trends in access to schooling in selected countries with low coverage .............................................................................................211
• How low coverage may affect the interpretation of PISA results .............................................................................................................211
Disparities in performance, by socio‑economic status ............................................................................................................................................214
• How performance differences relate to socio-economic disparities among students ...............................................................214
Differences in students’ science‑related career expectations and beliefs related to socio‑economic background ......224
How performance is related to socio‑economic status between and within schools .........................................................................225
• Differences in access to educational resources, grade repetition and enrolment in vocational tracks related
to socio-economic status ....................................................................................................................................................................................................230
Trends in equity in education .....................................................................................................................................................................................................233
8
© OECD 2016 PISA 2015 RESULTS (VOLUME I): EXCELLENCE AND EQUITY IN EDUCATION