Programme for International Student Assessment
Ready to Learn
StudentS’ engAgement, dRIve And SeLf-BeLIefS
voLume III
Ready to Learn
STUDENTS’ ENGAGEMENT, DRIVE AND SELF-BELIEFS
(VOLUME III)
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OECD (2013), PISA 2012 Results: Ready to Learn: Students’ Engagement, Drive and Self-Beliefs (Volume III), PISA, OECD Publishing.
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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 OECD’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 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 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
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 under- represented 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 OECD 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 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 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
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 OECD 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 OECD 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 OECD 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 2000.
Administrative support was provided by Claire Chetcuti, Juliet Evans, Jennah Huxley and Diana Tramontano.
The OECD contracted the Australian Council for Educational Research (ACER) to manage the development of the mathematics, problem solving and financial literacy frameworks for PISA 2012. Achieve was also contracted by the OECD 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.
Preliminar
y v ersion:
Table of contents to be a
EXECUTIVE SUMMARY ... 17
READER’S GUIDE ... 21
WHAT IS PISA? ... 23
Who are the PISA students? ... 25
What kinds of results does the test provide? ... 26
Where can you find the results? ... 26
CHAPTER 1 WHAT IT TAKES TO LEARN ... 29
A comprehensive approach to measuring educational success among 15-year-olds ... 30
The economic and social dynamics shaping the need to prepare students for lifelong learning ... 34
Structure of the volume ... 35
CHAPTER 2 ENGAGEMENT WITH AND AT SCHOOL ... 39
Lack of punctuality: Arriving late for school ... 41
Absenteeism: Skipping classes or days of school ... 47
Sense of belonging ... 51
Attitudes towards school ... 56
CHAPTER 3 STUDENTS’ DRIVE AND MOTIVATION ... 63
Perseverance ... 65
Openness to problem solving... 67
Locus of control ... 69
• Perceived self-responsibility for failing in mathematics ... 69
• Perceived control of success in mathematics and at school ... 70
Motivation to learn mathematics ... 72
• Intrinsic motivation to learn mathematics... 72
• Instrumental motivation to learn mathematics ... 78
The role of gender and socio-economic differences in the relationship between students’ drive and motivation and performance ... 82
CHAPTER 4 MATHEMATICS SELF-BELIEFS AND PARTICIPATION IN MATHEMATICS-RELATED ACTIVITIES ... 87
Mathematics self-efficacy ... 89
Mathematics self-concept ... 95
Mathematics anxiety ... 98
Participation in mathematics activities, mathematics intentions and norms ... 106
The role of gender and socio-economic differences in the relationship between dispositions towards mathematics and performance ... 110
CHAPTER 5 THE ROLE OF TEACHERS AND SCHOOLS IN SHAPING STUDENTS’ ENGAGEMENT,
DRIVE AND SELF-BELIEFS ... 113
The association between school climate and dispositions to learn ... 115
The role of social comparisons ... 117
The relationship between what happens in the classroom and student engagement, drive and motivation, and mathematics self-beliefs ... 123
• Experience with pure and applied mathematics ... 129
Students’ drive, motivation and self-beliefs and school practices: Teacher behaviour in class and school climate ... 139
• Trends in the relationship between students’ engagement, motivation and dispositions and the schools they attend ... 145
CHAPTER 6 THE ROLE OF FAMILIES IN SHAPING STUDENTS’ ENGAGEMENT, DRIVE AND SELF-BELIEFS ... 149
The home environment and parental behaviour ... 153
Parents’ circumstances ... 155
Parental expectations and dispositions ... 157
CHAPTER 7 GENDER AND SOCIO-ECONOMIC DISPARITIES IN STUDENTS’ ENGAGEMENT, DRIVE AND SELF-BELIEFS ... 165
Disparities in engagement with and at school, drive and self-beliefs among students who perform at the same level ... 171
Gender and socio-economic differences in the association between engagement with and at school, drive and self-beliefs and mathematics performance ... 173
Trends in the relationship between engagement with and at school, drive and self-beliefs and mathematics performance related to gender and socio-economic status ... 179
The gender gap in mathematics performance among top performers: The role of engagement with and at school, drive and self-beliefs ... 179
CHAPTER 8 POLICY IMPLICATIONS OF STUDENTS’ DISPOSITIONS TOWARDS LEARNING ... 185
The impact of schools and families ... 186
• Engagement with and at school ... 186
• Drive and motivation ... 187
• Mathematics self-beliefs ... 187
• The role of social comparisons... 187
• Parents’ expectations for their child ... 187
The impact of a level playing field ... 188
ANNEX A PISA 2012 TECHNICAL BACKGROUND ... 191
Annex A1 Construction of mathematics scales and indices from the student, school and parent context questionnaires .... 192
Annex A2 The PISA target population, the PISA samples and the definition of schools ... 209
Annex A3 Technical notes on analyses in this volume ... 221
Annex A4 Quality assurance ... 225
Annex A5 Technical details of trends analyses ... 226
Annex A6 Anchoring vignettes in the PISA 2012 Student Questionnaire ... 229
ANNEX B PISA 2012 DATA ... 231
Annex B1 Results for countries and economies ... 232
Annex B2 Results for regions within countries ... 473
Annex B3 List of tables available on line ... 511
ANNEX C THE DEVELOPMENT AND IMPLEMENTATION OF PISA – A COLLABORATIVE EFFORT ... 515
BOXES
Box III.2.1 The cyclical nature of the relationship between students’ dispositions, behaviours and self-beliefs and mathematics
performance ... 46
Box III.2.2 Interpreting PISA indices ... 53
Box III.2.3 The association between students’ dispositions, behaviours and self-beliefs and mathematics performance ... 56
Box III.3.1 Improving in PISA: Japan ... 81
Box III.4.1 Improving in PISA: Portugal ... 104
Box III.5.1 Students’ reports on teachers’ behaviours in class ... 124
FIGURES Figure III.1.1 Percentage of students who are in schools where there is a consensus on the importance of the social and emotional development of students ... 31
Figure III.1.2 Percentage of students who report being happy at school ... 32
Figure III.1.3 Students’ engagement, drive and self-beliefs in PISA 2012... 33
Figure III.2.1 How PISA 2012 measures students’ engagement with and at school ... 41
Figure III.2.2 Percentage of students who arrive late for school ... 42
Figure III.2.3 Socio-economic disparities in arriving late for school ... 43
Figure III.2.4 Change between 2003 and 2012 in students arriving late for school ... 44
Figure III.2.5 Change between 2003 and 2012 in socio-economic disparities in arriving late for school... 44
Figure III.2.6 Relationship between arriving late for school and mathematics performance ... 45
Figure III.2.7 Percentage of students who skip classes ... 48
Figure III.2.8 Percentage of students who skip days of school ... 49
Figure III.2.9 Socio-economic disparities in skipping classes ... 50
Figure III.2.10 Socio-economic disparities in skipping days of school ... 50
Figure III.2.11 The relationship between skipping classes and days of school and mathematics performance ... 51
Figure III.2.12 Students’ sense of belonging ... 52
Figure III.2.13 Change between 2003 and 2012 in students’ sense of belonging ... 52
Figure III.2.14 Relationship between sense of belonging and mathematics performance ... 55
Figure III.2.15 Students’ attitudes towards school: Learning outcomes ... 57
Figure III.2.16 Students’ attitudes towards school: Learning activities ... 58
Figure III.2.17 Change between 2003 and 2012 in students’ attitudes towards school (learning outcomes) ... 59
Figure III.3.1 How PISA 2012 measures students’ drive and motivation... 65
Figure III.3.2 Students’ perseverance ... 66
Figure III.3.3 Relationship between perseverance and mathematics performance ... 67
Figure III.3.4 Openness to problem solving ... 68
Figure III.3.5 Relationship between openness to problem solving and mathematics performance ... 69
Figure III.3.6 Perceived self-responsibility for failing in mathematics ... 70
Figure III.3.7 Perceived control of success in mathematics ... 71
Figure III.3.8 Relationship between perceived control of success in mathematics and mathematics performance ... 72
Figure III.3.9 Students’ intrinsic motivation to learn mathematics ... 73
Figure III.3.10 Change between 2003 and 2012 in students’ intrinsic motivation to learn mathematics ... 74
Figure III.3.11 Gender and socio-economic differences in students’ intrinsic motivation to learn mathematics ... 75
Figure III.3.12a Change between 2003 and 2012 in socio-economic disparities in students’ intrinsic motivation to learn mathematics ... 76
Figure III.3.12b Change between 2003 and 2012 in the gender gap in students’ intrinsic motivation to learn mathematics ... 76
Figure III.3.13 Relationship between intrinsic motivation to learn mathematics and mathematics performance ... 77
Figure III.3.14 Students’ instrumental motivation to learn mathematics ... 79
Figure III.3.15 Change between 2003 and 2012 in students’ instrumental motivation to learn mathematics ... 79
Figure III.3.16 Gender and socio-economic differences in students’ instrumental motivation to learn mathematics ... 80
Figure III.3.17 Relationship between instrumental motivation to learn mathematics and mathematics performance ... 81
Figure III.4.1 Mathematics self-beliefs, dispositions and participation in mathematics-related activities ... 88
Figure III.4.2 Students’ mathematics self-efficacy ... 89
Figure III.4.3 Gender and socio-economic differences in mathematics self-efficacy ... 90
Figure III.4.4a Change between 2003 and 2012 in the gender gap in mathematics self-efficacy ... 92
Figure III.4.4b Change between 2003 and 2012 in socio-economic disparities in mathematics self-efficacy ... 92
Figure III.4.5 Country-level association between mathematics performance and mathematics self-efficacy ... 93
Figure III.4.6 Relationship between mathematics self-efficacy and mathematics performance... 94
Figure III.4.7 Students’ mathematics self-concept ... 95
Figure III.4.8 Change between 2003 and 2012 in students’ mathematics self-concept ... 96
Figure III.4.9 Relationship between mathematics self-concept and mathematics performance ... 97
Figure III.4.10 Students’ mathematics anxiety ... 99
Figure III.4.11 Change between 2003 and 2012 in students’ anxiety towards mathematics... 99
Figure III.4.12 Gender and socio-economic differences in students’ mathematics anxiety ... 100
Figure III.4.13 Change between 2003 and 2012 in the gender gap in anxiety towards mathematics ... 101
Figure III.4.14 System-level association between mathematics performance and mathematics anxiety ... 102
Figure III.4.15 Relationship between mathematics anxiety and mathematics performance ... 103
Figure III.4.16 Students’ participation in activities related to mathematics ... 107
Figure III.4.17 Gender differences in students’ participation in mathematics-related activities ... 108
Figure III.4.18 Socio-economic differences in students’ participation in mathematics-related activities ... 109
Figure III.4.19 Relationship between mathematics anxiety and mathematics performance among the highest- and lowest-achieving students: The role of socio-economic and gender differences ... 110
Figure III.5.1 Concentration of students who arrive late for school ... 115
Figure III.5.2 Within- and between-school differences in mathematics self-efficacy ... 116
Figure III.5.3 Relative performance and student engagement, drive and self-beliefs ... 118
Figure III.5.4a Relationship between absolute and relative performance and mathematics self-concept ... 120
Figure III.5.4b Relationship between relative performance and mathematics self-concept and mean mathematics performance ... 121
Figure III.5.5a Relationship between absolute and relative performance and intrinsic motivation to learn mathematics ... 122
Figure III.5.5b Relationship between relative performance and intrinsic motivation to learn mathematics and mean mathematics performance ... 123
Figure III.5.6 Index of teachers’ use of cognitive-activation strategies ... 125
Figure III.5.7 Index of teacher-directed instruction ... 126
Figure III.5.8 Index of teachers’ student orientation ... 127
Figure III.5.9 Index of teachers’ use of formative assessments ... 128
Figure III.5.10 Within- and between-school differences in students’ experience with applied mathematics tasks ... 130
Figure III.5.11 Within- and between-school differences in students’ experience with pure mathematics tasks ... 131
Figure III.5.12 Within- and between-school differences in teachers’ use of student-oriented strategies ... 132
Figure III.5.13 Relationship between experience with pure mathematics problems and students’ lack of punctuality ... 133
Figure III.5.14 Relationship between students’ experience with pure and applied mathematics tasks and intrinsic motivation to learn mathematics ... 134
Figure III.5.15 Students’ confidence in solving an applied mathematics task as a function of frequency of experience with that task ... 135
Figure III.5.16 Students’ confidence in solving a pure mathematics task as a function of frequency of experience with that task ... 136
Figure III.5.17 Students’ confidence as a function of experience with different problems, OECD average ... 137
Figure III.5.18 Relationship between teachers’ use of cognitive-activation strategies and student perseverance ... 138
Figure III.5.19 Relationship between disciplinary climate and students’ skipping classes or days of school ... 140
Figure III.5.20 Relationship between disciplinary climate and students’ sense of belonging ... 141
Figure III.5.21 Relationship between teacher-student relations and students’ lack of punctuality ... 142
Figure III.5.22 Relationship between teacher-student relations and students’ sense of belonging ... 143
Figure III.5.23 Relationship between repeating a grade and skipping classes or days of school ... 144
Figure III.6.1 The role of families in education ... 151
Figure III.6.2 The home environment ... 152
Figure III.6.3 The home environment and its relationship with mathematics performance ... 154
Figure III.6.4 The relationship between parents regularly eating the main meal with their child and the likelihood that the child arrives late for school ... 155
Figure III.6.5 The relationship between parents regularly eating the main meal with their child and the child’s propensity to skip classes or days of school ... 155
Figure III.6.6 Students’ mathematics performance and parents’ work in STEM occupations ... 156
Figure III.6.7 Parents working in STEM occupations and students’ intrinsic motivation to learn mathematics ... 157
Figure III.6.8 Parental expectations for their child’s future ... 158
Figure III.6.9 Difference in mathematics performance associated with parents’ expectations for their child’s future ... 160
Figure III.6.10 Difference in student perseverance that is associated with parents’ expectations for their child’s future ... 161
Figure III.6.11 The association between parents’ expectations and students’ dispositions ... 162
Figure III.7.1 Relationship between the gender gap in mathematics performance and student disposition ... 167
Figure III.7.2 Relationship between socio-economic disparities in mathematics performance and student dispositions ... 168
Figure III.7.3 Gender gap among top performers in mathematics ... 169
Figure III.7.4 Gender gap among low performers in mathematics ... 170
Figure III.7.5 Socio-economic disparities in arriving late for school ... 172
Figure III.7.6 Gender gaps in arriving late for school ... 173
Figure III.7.7 Gender gaps in mathematics self-efficacy ... 174
Figure III.7.8 Socio-economic disparities in mathematics self-efficacy ... 175
Figure III.7.9 Impact of socio-economic status on the relationship between intrinsic motivation to learn mathematics and mathematics performance ... 176
Figure III.7.10 Gender gradient in the relationship between mathematics anxiety and mathematics performance ... 178
Figure III.7.11 Impact of socio-economic status on the relationship between mathematics anxiety and mathematics performance ... 180
Figure III.7.12 Role of mathematics self-efficacy in reducing the gender gap in mathematics performance among the highest-achieving students ... 181
Figure III.7.13 Role of mathematics anxiety in reducing the gender gap in mathematics performance among the highest-achieving students ... 182
TABLES
Table III.A Snapshot of students’ engagement, drive and self-beliefs ... 19
Table A1.1 Levels of parental education converted into years of schooling ... 195
Table A1.2 A multilevel model to estimate grade effects in mathematics accounting for some background variables ... 197
Table A1.3 Student questionnaire rotation design ... 200
Table A2.1 PISA target populations and samples ... 211
Table A2.2 Exclusions ... 213
Table A2.3 Response rates ... 215
Table A2.4a Percentage of students at each grade level ... 218
Table A2.4b Percentage of students at each grade level, by gender ... 219
Table A5.1 Link error for comparisons of performance between PISA 2012 and previous assessments ... 227
Table III.2.1a Mathematics performance, by the number of times students arrived late for school in the two weeks prior to the PISA test ... 232
Table III.2.1b Change between 2003 and 2012 in the number of times students arrived late for school ... 236
Table III.2.2a Mathematics performance, by the number of times students skipped some classes in the two weeks prior to the PISA test ... 241
Table III.2.2b Mathematics performance, by the number of times students skipped a day of school in the two weeks prior to the PISA test ... 245
Table III.2.2c Association between skipping classes or days of school and mathematics performance, by performance level ... 249
Table III.2.3a Students’ sense of belonging ... 251
Table III.2.3d Index of sense of belonging and mathematics performance, by national quarters of this index ... 252
Table III.2.3f Change between 2003 and 2012 in students’ sense of belonging ... 254
Table III.2.4a Students’ attitudes towards school: Learning outcomes ... 257
Table III.2.4d Index of attitudes towards school (learning outcomes) and mathematics performance, by national quarters of this index ... 258
Table III.2.4e Change between PISA 2003 and PISA 2012 in students’ attitudes towards school learning outcomes ... 260
Table III.2.5a Students’ attitudes towards school: Learning activities ... 262
Table III.2.5d Index of attitudes towards school (learning activities) and mathematics performance, by national quarters of this index ... 263
Table III.2.7a Effect sizes for gender differences in engagement with and at school ... 265
Table III.2.7b Effect sizes for socio-economic differences in engagement with and at school ... 266
Table III.2.7c Effect sizes for differences in immigrant background in engagement with and at school ... 267
Table III.2.9 Change between 2003 and 2012 in the association between students’ engagement with school and mathematics performance ... 268
Table III.3.1a Students and perseverance ... 269
Table III.3.1d Index of perseverance and mathematics performance, by national quarters of this index ... 270
Table III.3.2a Students’ openness to problem solving ... 272
Table III.3.2d Index of openness to problem solving and mathematics performance, by national quarters of this index ... 273
Table III.3.3a Students’ self-responsibility for failing in mathematics ... 275
Table III.3.3b Index of self-responsibility for failing in mathematics and mathematics performance, by national quarters of this index .... 278
Table III.3.3d Students’ perceived control of success in mathematics ... 280
Table III.3.3h Students’ perceived control of success in school ... 281
Table III.3.4a Students’ intrinsic motivation to learn mathematics ... 282
Table III.3.4d Index of intrinsic motivation to learn mathematics and mathematics performance, by national quarters of this index ... 283
Table III.3.4f Change between 2003 and 2012 in students’ intrinsic motivation to learn mathematics ... 285
Table III.3.5a Students’ instrumental motivation to learn mathematics ... 287
Table III.3.5d Index of instrumental motivation to learn mathematics and mathematics performance, by national quarters of this index ... 288
Table III.3.5f Change between 2003 and 2012 in students’ instrumental motivation to learn mathematics ... 290
Table III.3.7a Effect sizes for gender differences in drive and motivation ... 292
Table III.3.7b Effect sizes for socio-economic differences in drive and motivation ... 293
Table III.3.7c Effect sizes for differences by immigrant status in drive and motivation ... 294
Table III.3.8 Students’ drive and motivation, by proficiency level in mathematics ... 295
Table III.3.9 Change between 2003 and 2012 in the association between students’ drive and mathematics performance ... 297
Table III.4.1a Students’ self-efficacy in mathematics ... 298
Table III.4.1d Index of mathematics self-efficacy and mathematics performance, by national quarters of this index ... 299
Table III.4.1f Change between 2003 and 2012 in students’ self-efficacy in mathematics ... 301
Table III.4.2a Students’ self-concept in mathematics ... 304
Table III.4.2d Index of mathematics self-concept and mathematics performance, by national quarters of this index ... 305
Table III.4.2f Change between 2003 and 2012 in students’ mathematics self-concept ... 307
Table III.4.3a Students and mathematics anxiety ... 310
Table III.4.3b Students and mathematics anxiety, by gender ... 311
Table III.4.3c Students and mathematics anxiety, by socio-economic status ... 313
Table III.4.3d Index of mathematics anxiety and mathematics performance, by national quarters of this index ... 316
Table III.4.3f Change between 2003 and 2012 in students’ mathematics anxiety ... 318
Table III.4.4a Students and mathematics behaviours ... 321
Table III.4.4d Index of mathematics behaviours and mathematics performance, by national quarters of this index ... 322
Table III.4.5a Students and their intentions for mathematics ... 324
Table III.4.5b Index of mathematics intentions and mathematics performance, by national quarters of this index ... 325
Table III.4.6a Students and subjective norms in mathematics ... 327
Table III.4.6b Index of subjective norms in mathematics and mathematics performance, by national quarters of this index ... 328
Table III.4.7a Effect sizes for gender differences in mathematics self-beliefs and participation in mathematics activities ... 330
Table III.4.7b Effect sizes for socio-economic differences in mathematics self-beliefs and participation in mathematics activities... 331
Table III.4.7c Effect sizes for differences by immigrant background in mathematics self-beliefs and participation in mathematics activities ... 332
Table III.4.9 Change between 2003 and 2012 in the association between student’s mathematics self-beliefs and mathematics performance ... 333
Table III.4.10 Country-level correlations among the changes, between 2003 and 2012, in students’ engagement and dispositions towards mathematics measures... 334
Table III.5.1a Concentration of students arriving late for school in the two weeks prior to the PISA test ... 335
Table III.5.1b Social comparisons and arriving late for school ... 336
Table III.5.1c Trends in the concentration of students arriving late for school in the two weeks prior to the PISA test ... 337
Table III.5.1d Trends in the percentage of students arriving late for school in the two weeks prior to the PISA test, by school characteristics ... 338
Table III.5.2a The concentration of students who skipped classes or days of school in the two weeks prior to the PISA test ... 345
Table III.5.2b Social comparisons and skipping classes or days of school ... 346
Table III.5.3c Social comparisons and sense of belonging ... 347
Table III.5.4b Social comparisons and perseverance ... 348
Table III.5.5c Social comparisons and intrinsic motivation to learn mathematics ... 349
Table III.5.6c Social comparisons and instrumental motivation to learn mathematics ... 350
Table III.5.7c Social comparisons and mathematics self-efficacy ... 351
Table III.5.8c Social comparisons and mathematics self-concept ... 352
Table III.5.9c Social comparisons and mathematics anxiety ... 353
Table III.5.10a Index of experience with applied mathematics tasks and mathematics performance, by national quarters of this index ... 354
Table III.5.10c Index of experience with pure mathematics tasks and mathematics performance, by national quarters of this index... 356
Table III.5.10e Index of teachers’ use of cognitive-activation strategies and mathematics performance, by national quarters of this index ... 358
Table III.5.10g Index of teachers’ use of formative assessment and mathematics performance, by national quarters of this index ... 360
Table III.5.10j Index of teachers’ student orientation at school and mathematics performance, by national quarters of this index ... 362
Table III.5.10l Index of teacher-directed instruction at school and mathematics performance, by national quarters of this index ... 364
Table III.5.10n Index of disciplinary climate at school and mathematics performance, by national quarters of this index ... 366
Table III.5.10o Index of teacher-student relations at school and mathematics performance, by national quarters of this index ... 368
Table III.5.11 The relationship between experience with pure and applied mathematics problems and student engagement, drive, motivation and self-beliefs ... 370
Table III.5.14 Relationship between teachers’ use of cognitive-activation strategies and student dispositions ... 376
Table III.5.15 Relationship between teachers’ use of teacher-directed instruction and student dispositions ... 378
Table III.5.16 Relationship between teachers’ use of formative assessments and student dispositions ... 380
Table III.5.17 Relationship between teachers’ student orientation and student dispositions ... 382
Table III.5.18 Relationship between disciplinary climate and student dispositions ... 384
Table III.5.19 Relationship between teacher-student relations and student dispositions ... 386
Table III.5.22 Relationship between learning time in mathematics and student dispositions ... 388
Table III.5.23 Relationship between learning time in school and student dispositions ... 390
Table III.5.24 Relationship between the presence of ability grouping in school and student dispositions ... 392
Table III.5.25 Relationship between the presence of creative extracurricular activities at school and student dispositions ... 394
Table III.5.26 Relationship between the presence of extracurricular mathematics activities at school and student dispositions ... 396
Table III.5.27 Relationship between class size and student dispositions ... 398
Table III.5.28 Relationship between school size and student dispositions (per 100 students) ... 400
Table III.5.29 Relationship between school’s socio-economic composition and student dispositions ... 402
Table III.6.1a Students’ mathematics performance, by parents’ activities at home ... 404
Table III.6.1b Students’ mathematics performance, by family structure and labour-market situation ... 406
Table III.6.1c Students’ mathematics performance, by parents’ attitudes towards the child’s future ... 409
Table III.6.2a Students who arrived late for school in the two weeks prior to the PISA test, by parents’ activities at home ... 411
Table III.6.2b Students who arrived late for school in the two weeks prior to the PISA test, by family structure and labour- market situation ... 412
Table III.6.2c Students who arrived late for school in the two weeks prior to the PISA test, by parents’ attitudes towards the child’s future ... 414
Table III.6.6a Students’ perseverance, by parents’ activities at home ... 415
Table III.6.6b Students’ perseverance, by family structure and labour-market situation ... 416
Table III.6.6c Students’ perseverance, by parents’ attitudes towards the child’s future ... 418
Table III.6.8a Students’ intrinsic motivation to learn mathematics, by parents’ activities at home ... 419
Table III.6.8b Students’ intrinsic motivation to learn mathematics, by family structure and labour-market situation ... 420
Table III.6.8c Students’ intrinsic motivation to learn mathematics, by parents’ attitudes towards the child’s future ... 422
Table III.6.10a Students’ self-efficacy in mathematics, by parents’ activities at home ... 423
Table III.6.10b Students’ self-efficacy in mathematics, by family structure and labour-market situation ... 424
Table III.6.10c Students’ self-efficacy in mathematics, by parents’ attitudes towards the child’s future ... 426
Table III.6.13a The relationship between arriving late and parental attitudes towards the child’s future ... 427
Table III.6.13b The relationship between perseverance and parental attitudes towards the child’s future ... 427
Table III.6.13c The relationship between intrinsic motivation to learn mathematics and parental attitudes towards the child’s future ... 428
Table III.6.13d The relationship between mathematics self-efficacy and parental attitudes towards the child’s future ... 428
Table III.7.1a The gender gap in engagement with and at school ... 429
Table III.7.1b Socio-economic disparities in engagement with and at school ... 431
Table III.7.2a The gender gap in drive and motivation ... 433
Table III.7.2b Socio-economic disparities in drive and motivation ... 435
Table III.7.3a The gender gap in mathematics self-beliefs and engagement in mathematics activities ... 437
Table III.7.3b Socio-economic disparities in mathematics self-beliefs and engagement in mathematics activities ... 439
Table III.7.4 Association between students’ gender and socio-economic status and mathematics performance ... 441
Table III.7.6a Gender and socio-economic differences in the association between engagement with and at school and mathematics performance ... 442
Table III.7.6b Gender and socio-economic differences in the association between drive and motivation and mathematics performance ... 446
Table III.7.6c Gender and socio-economic differences in the association between mathematics self-beliefs and mathematics performance ... 449
Table III.7.7a Engagement with and at school among resilient students, disadvantaged low-achievers, advantaged low-achievers and advantaged high-achievers ... 453
Table III.7.7b Students’ drive and motivation among resilient students, disadvantaged low-achievers, advantaged low-achievers and advantaged high-achievers ... 455
Table III.7.7c Mathematics self-beliefs among resilient students, disadvantaged low-achievers, advantaged low-achievers and advantaged high-achievers ... 457
Table III.7.8 Change between 2003 and 2012 in the association between students’ attitudes and behaviours and mathematics performance, by gender ... 459
Table III.7.9 Change between 2003 and 2012 in the association between students’ attitudes and behaviours and mathematics performance, by socio-economic status ... 466
Table B2.III.1 Mathematics performance, by the number of times students arrived late for school in the two weeks prior to the PISA test and region ... 473
Table B2.III.2 Mathematics performance, by the number of times students skipped some classes in the two weeks prior to the PISA test and region ... 475
Table B2.III.3 Mathematics performance, by the number of times students skipped a day of school in the two weeks prior to the PISA test and region ... 477
Table B2.III.4 Index of sense of belonging and mathematics performance, by national quarters of this index and region ... 479
Table B2.III.8 Index of perseverance and mathematics performance, by national quarters of this index and region ... 483
Table B2.III.9 Index of openness to problem solving and mathematics performance, by national quarters of this index and region ... 487
Table B2.III.11 Index of intrinsic motivation to learn mathematics and mathematics performance, by national quarters of this index and region ... 491
Table B2.III.12 Index of instrumental motivation to learn mathematics and mathematics performance, by national quarters of this index and region ... 495
Table B2.III.13 Index of mathematics self-efficacy and mathematics performance, by national quarters of this index and region ... 499
Table B2.III.14 Index of mathematics self-concept and mathematics performance, by national quarters of this index and region ... 503
Table B2.III.15 Index of mathematics anxiety and mathematics performance, by national quarters of this index and region ... 507
Executive Summary
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Students’ engagement with school, the belief that they can achieve at high levels, and their ability and willingness to do what it takes to reach their goals not only play a central role in shaping students’ ability to master academic subjects, they are also valuable attributes that will enable students to lead full lives, meeting challenges and making the most of available opportunities along the way. In other words, much more is required of students – and adults – than just cognitive proficiency.
Four out of five students in OECD countries agree or strongly agree that they feel happy at school or that they feel like they belong at school.
Not all students are equally likely to report a strong sense of belonging: on average across OECD countries, for example, 78% of disadvantaged but 85% of advantaged students agree or strongly agree with the statement “I feel like I belong at school”.
Although the vast majority of students reported a strong sense of belonging, more than one in three students in OECD countries reported that they had arrived late for school in the two weeks prior to the PISA test;
and more than one in four students reported that they had skipped classes or days of school during the same period.
Lack of punctuality and truancy are negatively associated with student performance. On average across OECD countries, arriving late for school is associated with a 27-point lower score in mathematics, while skipping classes or days of school is associated with a 37-point lower score in mathematics – the equivalent of almost one full year of formal schooling.
Students who are more perseverant and more open to problem solving perform at higher levels in mathematics.
For example, students who feel they can handle a lot of information, are quick to understand things, seek explanations for things, can easily link facts together, and like to solve complex problems score 31 points higher in mathematics, on average, than those who are less open to problem solving. Among high achievers, the difference between the two groups of students is even greater – an average of 39 score points.
Across most countries and economies, socio-economically disadvantaged students not only score lower in mathematics, they also have lower levels of engagement, drive, motivation and self-beliefs. Resilient students, disadvantaged students who achieve at high levels, break this link.
Resilient students report much higher levels of perseverance, intrinsic and instrumental motivation to learn mathematics, mathematics self-efficacy, mathematics self-concept and lower levels of mathematics anxiety than disadvantaged students who perform at lower levels; in fact, they share many of the characteristics of advantaged high-achievers.
One way that a student’s negative self-belief can manifest itself is in anxiety towards mathematics. Some 30%
of students reported that they feel helpless when doing mathematics problems: 25% of boys, 35% of girls, 35% of disadvantaged students, and 24% of advantaged students reported feeling that way.
Mathematics anxiety is strongly associated with performance. On average across OECD countries, greater mathematics anxiety is associated with a 34-point lower score in mathematics – the equivalent of almost one year of school. Between 2003 and 2012, mathematics self-efficacy tended to increase in those countries that also showed reductions in the level of mathematics anxiety. This was true in Iceland and Portugal, for example, where steep drops in mathematics anxiety coincided with increases in students’ mathematics self-efficacy.
PISA results show that even when girls perform as well as boys in mathematics, they report less perseverance, less openness to problem solving, less intrinsic and instrumental motivation to learn mathematics, lower mathematics self-concept and higher levels of anxiety towards mathematics than boys, on average; they are also more likely than boys to attribute failure in mathematics to themselves rather than to external factors.
In most countries and economies, the average girl underperforms in mathematics compared with the average boy; and among the highest-achieving students, the gender gap in favour of boys is even wider. However, PISA reveals that the gender gap, even among the highest-achieving students, is considerably narrower when comparing boys and girls with similar levels of drive, motivation and mathematics self-beliefs.
In many countries, students’ motivation, self-belief and dispositions towards learning mathematics are positively associated not only with how well they perform in mathematics, but also with how much better these students perform compared to other students in their school.
In all countries except Belgium, Croatia, Finland, Korea and Romania, students’ intrinsic motivation to learn mathematics is positively associated with how much better students perform compared to other students in their schools; in Argentina, Austria, Chile, France, Germany, Liechtenstein, Peru and Slovenia, a student’s standing relative to others in the school is strongly associated with his or her self-beliefs in learning mathematics; and in Austria, Canada, the Czech Republic, France, Germany, Japan, Liechtenstein, the Netherlands and Slovenia, students who perform better compared to others in their school report significantly less mathematics anxiety.
Teacher-student relations are strongly associated with students’ engagement with and at school.
In all countries and economies except Hong Kong-China, Indonesia, Liechtenstein, Malaysia and Turkey, among students with equal mathematics performance and similar socio-economic status, students who attend schools with better teacher-student relations are less likely to report that they had arrived late during the two weeks prior to the PISA test. In addition, in all countries and economies, among students with equal performance and similar socio-economic status, those who attend schools with better teacher-student relations reported a stronger sense of belonging and greater intrinsic motivation to learn mathematics.
Parents’ expectations are strongly and positively associated not only with students’ mathematics performance but also with positive dispositions towards learning.
Across the 11 countries and economies that distributed a questionnaire to parents, students whose parents have high expectations for them – who expect them to earn a university degree and work in a professional or managerial capacity later on – tend to have more perseverance, greater intrinsic motivation to learn mathematics, and more confidence in their own ability to solve mathematics problems than students of similar socio-economic status and academic performance, but whose parents hold less ambitious expectations for them.
