Executive Summary

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Far too many students around the world are trapped in a vicious circle of poor performance and demotivation that leads only to more bad marks and further disengagement from school. Worse, poor performance at school has long-term consequences, both for the individual and for society as a whole. Students who perform poorly at age 15 face a high risk of dropping out of school altogether. When a large share of the population lacks basic skills, a country’s long-term economic growth is severely compromised.

Results from PISA 2012 show that more than one in four 15-year-old students in OECD countries did not attain a baseline level of proficiency in at least one of the three core subjects PISA assesses: reading, mathematics and science. In absolute numbers, this means that about 13 million 15-year-old students in the 64 countries and economies that participated in PISA 2012 were low performers in at least one subject.

Reducing the number of low-performing students is not only a goal in its own right but also an effective way to improve an education system’s overall performance – and equity, since low performers are disproportionately from socio-economically disadvantaged families. Brazil, Germany, Italy, Mexico, Poland, Portugal, the Rusian Federation, Tunisia and Turkey, for example, improved their performance in mathematics between 2003 and 2012 by reducing the share of low performers in this subject. What do these countries have in common? Not very much; as a group, they are about as socio-economically and culturally diverse as can be. But therein lies the lesson: all countries can improve their students’ performance, given the right policies and the will to implement them.

Multiple risk factors acting in concert

Analyses show that poor performance at age 15 is not the result of any single risk factor, but rather of a combination and accumulation of various barriers and disadvantages that affect students throughout their lives. Who is most likely to be a low performer in mathematics? On average across OECD countries, a socio-economically disadvantaged girl who lives in a single-parent family in a rural area, has an immigrant background, speaks a different language at home from the language of instruction, had not attended pre-primary school, had repeated a grade, and is enrolled in a vocational track has an 83% probability of being a low performer.

While these background factors can affect all students, among low performers the combination of risk factors is more detrimental to disadvantaged than to advantaged students. Indeed, all of the demographic characteristics considered in the report, as well as the lack of pre-primary education, increase the probability of low performance by a larger margin among disadvantaged than among advantaged students, on average across OECD countries. Only repeating a grade and enrolment in a vocational track have greater penalties for advantaged students. In other words, disadvantaged students tend not only to be encumbered with more risk factors, but those risk factors have a stronger impact on these students’ performance.

Less positive attitudes towards school and learning

Low performers tend to have less perseverance, motivation and self-confidence in mathematics than better-performing students, and they skip classes or days of school more. Students who have skipped school at least once in the two weeks prior to the PISA test are almost three times more likely to be low performers in mathematics than students who did not skip school.

Perhaps surprisingly, however, low performers in mathematics spend a similar amount of time as better-performing students in some mathematics activities, such as programming computers or taking part in mathematics competitions. They are more likely to participate in a mathematics club and play chess after school, perhaps because these activities are presented as recreational and are based on social interactions.

Less supportive teachers and schools

Students attending schools where teachers are more supportive and have better morale are less likely to be low performers, while students whose teachers have low expectations for them and are absent more often are more likely to be low performers in mathematics, even after accounting for the socio-economic status of students and schools.

In addition, in schools with larger concentrations of low performers, the quality of educational resources is lower, and the incidence of teacher shortage is higher, on average across OECD countries, even after accounting for students’ and schools’ socio-economic status. In countries and economies where educational resources are distributed more equitably across schools, there is less incidence of low performance in mathematics, and a larger share of top performers, even when comparing school systems whose educational resources are of similar quality.

Analysis also shows that the degree to which advantaged and disadvantaged students attend the same school (social inclusion) is more strongly related to smaller proportions of low performers in a school system than to larger proportions of top performers. These findings suggest that systems that distribute both educational resources and students more equitably across schools might benefit low performers without undermining better-performing students.

Policies that can help to break the cycle of disengagement and low performance

The first step for policy makers is to make tackling low performance a priority in their education policy agenda – and translate that priority into additional resources. Given the extent to which the profile of low performers varies across countries, tackling low performance requires a multi-pronged approach, tailored to national and local circumstances. An agenda to reduce the incidence of low performance can include several actions:

  • Dismantle the multiple barriers to learning.

  • Create demanding and supportive learning environments at school.

  • Provide remedial support as early as possible.

  • Encourage the involvement of parents and local communities.

  • Inspire students to make the most of available education opportunities.

  • Identify low performers and design a tailored policy strategy.

  • Provide targeted support to disadvantaged schools and/or families.

  • Offer special programmes for immigrant, minority-language and rural students.

  • Tackle gender stereotypes and assist single-parent families.

  • Reduce inequalities in access to early education and limit the use of student sorting.

http://dx.doi.org/10.1787/888933315931
Table 0.1. [Part 1/2] PERCENTAGE OF LOW PERFORMERS IN MATHEMATICS, READING AND SCIENCE
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Note: Values that are statistically significant are indicated in bold.

Countries/economies are ranked in ascending order of the percentage of low performing students in mathematics.

Source: OECD, PISA 2012 Database, Tables 1.1, 1.2, 1.9, 1.11 and 1.12.

Table 0.1. [Part 2/2] PERCENTAGE OF LOW PERFORMERS IN MATHEMATICS, READING AND SCIENCE
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Note: Values that are statistically significant are indicated in bold.

Countries/economies are ranked in ascending order of the percentage of low performing students in mathematics.

Source: OECD, PISA 2012 Database, Tables 1.1, 1.2, 1.9, 1.11 and 1.12.

 http://dx.doi.org/10.1787/888933315931

http://dx.doi.org/10.1787/888933315940
Table 0.2. [Part 1/2] OVERLAPPING OF LOW PERFORMANCE ACROSS SUBJECTS
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Countries/economies are ranked in descending order of the percentage of students who are above baseline in all subjects.

Source: OCD, PISA 2012 Database, Table 1.3.

Table 0.2. [Part 2/2] OVERLAPPING OF LOW PERFORMANCE ACROSS SUBJECTS
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Countries/economies are ranked in descending order of the percentage of students who are above baseline in all subjects.

Source: OCD, PISA 2012 Database, Table 1.3.

 http://dx.doi.org/10.1787/888933315940

http://dx.doi.org/10.1787/888933315951
Table 0.3. [Part 1/2] STUDENT BACKGROUND AND LOW PERFORMANCE
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Note: Values that are statistically significant are indicated in bold.

Countries/economies are ranked in ascending order of the difference in the percentage of low performers in mathematics between socio-economically advantaged and disadvantaged students.

Source: OECD, PISA 2012 Database, Tables 2.1, 2.3a, 2.6, 2.14, 2.16 and 2.18.

Table 0.3. [Part 2/2] STUDENT BACKGROUND AND LOW PERFORMANCE
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Note: Values that are statistically significant are indicated in bold.

1. This category includes students enrolled in pre-vocational, vocational and modular programmes. Countries/economies are ranked in ascending order of the difference in the percentage of low performers in mathematics between socio-economically advantaged and disadvantaged students.

Source: OECD, PISA 2012 Database, Tables 2.1, 2.3a, 2.6, 2.14, 2.16 and 2.18.

 http://dx.doi.org/10.1787/888933315951

http://dx.doi.org/10.1787/888933315961
Table 0.4. [Part 1/2] ENGAGEMENT, PERSEVERANCE AND SELF-CONFIDENCE AMONG LOW PERFORMERS IN MATHEMATICS
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Note: Values that are statistically significant are indicated in bold.

Countries/economies are ranked in descending order of the percentage of low performers in mathematics who had skipped school at least once in the two weeks prior to the PISA test.

Source: OECD, PISA 2012 Database, Tables 3.1, 3.3, 3.8, 3.12 and 3.15.

Table 0.4. [Part 2/2] ENGAGEMENT, PERSEVERANCE AND SELF-CONFIDENCE AMONG LOW PERFORMERS IN MATHEMATICS
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Note: Values that are statistically significant are indicated in bold.

Countries/economies are ranked in descending order of the percentage of low performers in mathematics who had skipped school at least once in the two weeks prior to the PISA test.

Source: OECD, PISA 2012 Database, Tables 3.1, 3.3, 3.8, 3.12 and 3.15.

 http://dx.doi.org/10.1787/888933315961

http://dx.doi.org/10.1787/888933315975
Table 0.5. [Part 1/3] HOW SCHOOL CHARACTERISTICS ARE RELATED TO LOW PERFORMANCE
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Note: Values that are statistically significant are indicated in bold.

Countries/economies are ranked in descending order of the difference in the percentage of low performers in mathematics in schools where teachers’ low expectations hinder learning a lot or to some extent and schools where teachers’ low expectations hinder learning very little or not at all.

Source: OECD, PISA 2012 Database, Tables 4.6, 4.8, 4.14, 4.16 and 4.20.

http://dx.doi.org/10.1787/888933315975
Table 0.5. [Part 2/3] HOW SCHOOL CHARACTERISTICS ARE RELATED TO LOW PERFORMANCE
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Note: Values that are statistically significant are indicated in bold.

Countries/economies are ranked in descending order of the difference in the percentage of low performers in mathematics in schools where teachers’ low expectations hinder learning a lot or to some extent and schools where teachers’ low expectations hinder learning very little or not at all.

Source: OECD, PISA 2012 Database, Tables 4.6, 4.8, 4.14, 4.16 and 4.20.

Table 0.5. [Part 3/3] HOW SCHOOL CHARACTERISTICS ARE RELATED TO LOW PERFORMANCE
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Note: Values that are statistically significant are indicated in bold.

Countries/economies are ranked in descending order of the difference in the percentage of low performers in mathematics in schools where teachers’ low expectations hinder learning a lot or to some extent and schools where teachers’ low expectations hinder learning very little or not at all.

Source: OECD, PISA 2012 Database, Tables 4.6, 4.8, 4.14, 4.16 and 4.20.

 http://dx.doi.org/10.1787/888933315975

Table 0.6. HOW THE CHARACTERISTICS OF EDUCATION SYSTEMS ARE RELATED TO LOW PERFORMANCE COUNTRY-LEVEL CORRELATIONS
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Note: Values that are statistically significant are indicated in bold.

Source: OECD, PISA 2012 Database, Tables 5.1, 5.2 and 5.3.

 http://dx.doi.org/10.1787/888933315983