copy the linklink copied! Chapter 4. How did countries perform in PISA 2018?

This chapter compares students’ mean scores and the variation in their performance in reading, mathematics and science across the countries and economies that participated in the PISA 2018 assessment. It also highlights differences in social and economic contexts across education systems.

    

PISA outcomes are reported in a variety of ways; but the easiest way to gain an understanding of the overall performance of a country or economy is through the mean performance of its students. Because countries’ and economies’ standing in comparison with other countries/economies that participated in PISA can differ across subjects, this chapter includes multiple comparisons of mean performance. Further comparisons can consider the proportion of students who achieve a certain level of performance (see Chapters 5, 6 and 7 in this volume), or the extent to which learning outcomes vary within countries (PISA 2018 Results report, Where All Students Can Succeed [OECD, 2019see the section on “variation in performance” below and Volume II of the [1]]). No single ranking does justice to the richness of information that PISA provides and, more important, to the variety of goals that education systems pursue. This chapter also highlights the statistical uncertainty in PISA results when comparing countries and economies.

When considering differences in performance across countries and economies, it is also important to consider differences in context – such as a country’s level of development or the proportion of 15-year-olds who are in school and eligible to sit the PISA test. These factors are discussed at the end of the chapter.

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What the data tell us
  • On average, students in Beijing, Shanghai, Jiangsu and Zhejiang (China) and Singapore outperformed students from all other countries in reading, mathematics and science.

  • Differences in performance between students within the same country are, in general, larger than between-country differences in performance. For example, in every country and economy, the performance gap between the highest-scoring 5 % of students and the lowest-scoring 5 % of students in reading is larger than the difference in mean performance between the highest-performing country and the lowest-performing country.

  • While an inadequately resourced education system cannot deliver good results, Estonia, with a level of expenditure on education that is about 30 % lower than the OECD average, is nevertheless one of the top-performing OECD countries in reading, mathematics and science.

copy the linklink copied! Mean performance in reading, mathematics and science

In 2018, the mean reading score amongst OECD countries was 487 points; the mean score in mathematics and science was 489 points. In reading, Beijing, Shanghai, Jiangsu and Zhejiang (China) (hereafter “B-S-J-Z [China]”) (555 points) and Singapore (549 points) scored significantly higher than all other countries/economies that participated in PISA 2018. In mathematics and science, the highest mean performance was achieved by students in B-S-J-Z (China) (591 points in mathematics and 590 points in science), and the second-highest mean performance by students in Singapore (569 points in mathematics and 551 points in science).

Table I.4.1, Table I.4.2, and Table I.4.3 show each country’s/economy’s mean score, and indicate for which pairs of countries/ economies the differences between the means are statistically significant. Indeed, when comparing mean performance across countries/economies, only those differences that are statistically significant should be considered (see Chapter 2). For each country/economy shown in the middle column, the countries/economies whose mean scores are not statistically significantly different are listed in the right column. For example, B-S-J-Z (China) scored higher than Singapore on the PISA mathematics and science scales, but in reading, the mean performance of B-S-J-Z (China) was not statistically significantly different from that of Singapore; or students in Germany performed better in science than students in France, but in reading and mathematics, their mean scores were not statistically significantly different.

In Table I.4.1, Table I.4.2, and Table I.4.3, countries and economies are divided into three broad groups: those whose mean scores are statistically around the OECD mean (highlighted in white); those whose mean scores are above the OECD mean (highlighted in blue); and those whose mean scores are below the OECD mean (highlighted in grey).1

Twenty countries and economies performed above the OECD average in all three domains (reading, mathematics and science). B-S-J-Z (China) and Singapore were the highest-performing education systems: in all three subjects, their mean scores lay more than 50 points above the average score across OECD countries. In reading, Estonia, Canada, Finland and Ireland were the highest-performing OECD countries (the mean performance of Korea was significantly below that of Estonia, but not below those of Canada, Finland and Ireland; and Poland’s score was below those of Estonia, Canada and Finland, but not below that of Ireland) (all countries/economies are listed in descending order of their mean scores).

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Table I.4.1. Comparing countries’ and economies’ performance in reading
Table I.4.1. Comparing countries’ and economies’ performance in reading
Table I.4.1. Comparing countries’ and economies’ performance in reading

1. Data did not meet the PISA technical standards but were accepted as largely comparable (see Annexes A2 and A4).

Source: OECD, PISA 2018 Database, Table I.B1.4.

 StatLink https://doi.org/10.1787/888934028235

In science, the highest-performing OECD countries were Japan and Estonia. In mathematics, the highest-performing OECD countries were Japan, Korea and Estonia. B-S-J-Z (China), Singapore, Estonia, Canada, Finland, Ireland, Japan and Korea scored above the OECD average in all three subjects, as did Macao (China), Hong Kong (China), Chinese Taipei, Sweden, New Zealand, the United Kingdom, Denmark, Germany, Slovenia, Belgium and France (in descending order of mean performance in reading).

Two countries (the United States and Australia) scored above the OECD average in reading and science, but not in mathematics; in the United States, performance in mathematics was significantly below the OECD average, while the performance of students in Australia was not statistically significantly different from the OECD average. Norway scored above the OECD average in reading and mathematics, but close to the OECD average in science. Three countries (the Czech Republic, the Netherlands and Switzerland) scored above the OECD average in mathematics and science, but close to the OECD average in reading. Some countries achieved above-average results in one subject only; this was the case of Austria, Iceland and Latvia in mathematics.

Eight countries whose mean scores lay below the OECD average (Argentina, Jordan, Lebanon, the Republic of Moldova, the Republic of North Macedonia, Romania, Saudi Arabia and Ukraine) conducted the PISA 2018 test using pen-and-paper forms, designed initially for the PISA 2012 or earlier assessments. Their results are reported on the same scale as those of the remaining countries, just as PISA 2018 results for all remaining countries/economies are reported on the same scale as past PISA results.2

The gap in performance between the highest- and lowest-performing OECD countries was 111 score points in reading; it was even larger in mathematics and science.3 But the difference between the highest-performing and lowest-performing education systems that took part in PISA 2018 was about twice as large (Table I.4.1, Table I.4.2, and Table I.4.3), and the gap in mean performance, across all education systems in the world, is likely to be even larger. Indeed, the developing countries that participated in PISA – either as part of PISA 2018 or, in 2017, as part of the PISA for Development initiative (see Chapter 11 and Ward [2018[2]]) – represent only a minority of all developing countries. They often participated with the clear understanding that their students were not learning at adequate levels, even when they were in school. By participating in a global assessment of learning outcomes, these developing countries demonstrated a strong commitment to develop an evidence base for future education reforms and to address the international “learning crisis” (World Bank, 2017[3]).

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Table I.4.2. Comparing countries’ and economies’ performance in mathematics
Table I.4.2. Comparing countries’ and economies’ performance in mathematics
Table I.4.2. Comparing countries’ and economies’ performance in mathematics

1. Data did not meet the PISA technical standards but were accepted as largely comparable (see Annexes A2 and A4).

Source: OECD, PISA 2018 Database, Table I.B1.5.

 StatLink https://doi.org/10.1787/888934028254

copy the linklink copied! Variation in performance within countries and economies

While differences in average performance across countries and economies are large, the gap that separates the highest-performing and lowest-performing students within any country is, typically, even larger. In reading, for example, the difference between the 95th percentile of performance (the score above which only 5 % of students scored) and the 5th percentile of performance (the score below which only 5 % of students scored) was more than 220 score points in all countries and economies; on average across OECD countries, 327 score points separated these extremes (Table I.B1.4). This difference corresponds, typically, to capacities that students develop over the equivalent of several years and grades.4

The largest differences between top-performing and low-achieving students were found in Israel, Lebanon, Malta and the United Arab Emirates, meaning that learning outcomes at age 15 in these countries are highly unequal (Table I.B1.4).

The smallest differences between high- and low-achieving students were, typically, found amongst countries and economies with the lowest mean scores. In Kosovo, Morocco and the Philippines, even the highest-performing students scored only around the OECD average. In these countries/economies, the 95th percentile of the reading distribution was close to the average score across OECD countries.

The standard deviation summarises the variation in performance amongst 15-year-old students within each country/economy across the entire distribution. The average standard deviation in reading performance within OECD countries was 99 score points. If the between-country variation was also considered ( “OECD total”), the standard deviation across all students in OECD countries was 105 score points. By this measure, the smallest variation in reading proficiency was found in Kosovo (68 score points); several other countries and economies whose mean performance was below the OECD average also have small variations in performance (Figure I.4.1). Amongst high-performing systems, B-S-J-Z (China) (87 score points) stood out for its relatively small variation in performance. This indicates that, more than in other high-performing systems, student performance in B-S-J-Z (China) is consistently high: there are smaller-than-average inequalities in learning outcomes. In contrast, Singapore, with mean performance similar to that of B-S-J-Z (China), had one of the widest variations in reading performance (109 score points; the variation in mathematics and in science was closer to the OECD average). This large variation in reading performance in Singapore may be related to the diversity of students’ linguistic backgrounds. As shown at the end of this chapter, 43 % of students in Singapore reported that they do not speak the test language at home (Figure I.4.11).5 (PISA 2018 Results [Volume II]: Where All Students Can Succeed [OECD, 2019Demographic and socio-economic factors related to variations in performance within countries/economies are more extensively analysed in [1]]).

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Table I.4.3. Comparing countries’ and economies’ performance in science
Table I.4.3. Comparing countries’ and economies’ performance in science
Table I.4.3. Comparing countries’ and economies’ performance in science

1. Data did not meet the PISA technical standards but were accepted as largely comparable (see Annexes A2 and A4).

Source: OECD, PISA 2018 Database, Table I.B1.6.

 StatLink https://doi.org/10.1787/888934028273

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Figure I.4.1. Average performance in reading and variation in performance
Figure I.4.1. Average performance in reading and variation in performance

Source: OECD, PISA 2018 Database, Table I.B1.4.

 StatLink https://doi.org/10.1787/888934028349

copy the linklink copied! Ranking countries’ and economies’ performance in PISA

The goal of PISA is to provide useful information to educators and policy makers concerning the strengths and weaknesses of their country’s education system, the progress made over time, and opportunities for improvement. When ranking countries, economies and education systems in PISA, it is important to consider the social and economic context in which education takes place. Moreover, many countries and economies score at similar levels; small differences that are not statistically significant or practically meaningful should not be overly emphasised.

Table I.4.4, Table I.4.5 and Table I.4.6 show, for each country and economy, an estimate of where its mean performance ranks amongst all other countries and economies that participate in PISA as well as, for OECD countries, amongst all OECD countries. Because mean-score estimates are derived from samples and are thus associated with statistical uncertainty, it is often not possible to determine an exact ranking for all countries and economies. However, it is possible to identify the range of possible rankings for the country’s/economy’s mean performance.6 This range of ranks can be wide, particularly for countries/economies whose mean scores are similar to those of many other countries/economies.7

Table I.4.4, Table I.4.5 and Table I.4.6 also include, for countries where the sampling design supports such reporting, the results of cities, regions, states or other subnational entities within the country.8 For these subnational entities (whose results are reported in Annex B2), a rank order was not estimated. Still, the mean score and its confidence interval allow for a comparison of performance with that of countries and economies. For example, Alberta (Canada) scored below top-performers B-S-J-Z (China) and Singapore, but close to Macao (China) in reading. These subnational results also highlight differences within countries that are often as large as between-country differences in performance. In reading, for example, more than 40 score points separated the mean performance of Alberta and the mean performance of New Brunswick in Canada, and even larger differences were observed between Astana and the Atyrau region of Kazakhstan.

copy the linklink copied! A context for countries’ performance in PISA

Comparing the performance of students across vastly diverse countries poses numerous challenges. In any classroom, students with varying abilities, attitudes and social backgrounds are required to respond to the same set of tasks when sitting a test. When comparing the performance of schools in an education system, the same test is used across schools that may differ significantly in the structure and sequencing of their curriculum, in their pedagogical emphasis, in the instructional methods applied, and in the demographic and social contexts of their student population. Comparing the performance of education systems across countries adds further layers of complexity because students are given tests in different languages, and because the social, economic and cultural context of the countries that are being compared are often very different.

However, while students within a country may learn in different contexts according to their home environment and the school they attend, their performance is measured against common standards. And when they become adults, they will all face common challenges and will often have to compete for the same jobs. Similarly, in a global society and economy, the success of education systems in preparing students for life is no longer measured against locally established benchmarks, but increasingly against benchmarks that are common to all education systems around the world. As difficult as international comparisons are, comparisons with the best-performing systems provide important information for educators, and PISA goes to considerable lengths to ensure that such comparisons are valid and fair (see also Annex A6).

This section discusses countries’ mean reading performance in PISA in the context of important economic, demographic and social factors that can influence the assessment results (results are similar for mathematics and science). It provides a context for interpreting the results that are presented above and in the following chapters.

PISA’s stringent sampling standards limit the possible exclusion of students and schools and the impact of non-response. These standards are applied to ensure that the results support conclusions that are valid for the PISA target population when comparing adjudicated countries, economies and subnational entities. Chapter 3 provides a definition of the PISA target population, which is the relevant population when comparing school systems.

But when interpreting PISA results with regard to the overall population of 15-year-olds, sample coverage must be assessed with respect to this wider population. Coverage Index 3, discussed in Chapter 3, provides an estimate of the share of the 15-year-old age cohort covered by PISA. In 2018, it varied from 46 % in Baku (Azerbaijan) and 53 % in Panama to close to 100 % in Germany, Hong Kong (China) and Slovenia. While the PISA results are representative of the target population in all adjudicated countries/ economies, they cannot be readily generalised to the entire population of 15-year-olds in countries where many young people of that age are not enrolled in lower or upper secondary school. The mean scores of 15-year-old students in countries with a low Coverage Index 3 are typically below average (Figure I.4.2); but the mean scores amongst all 15-year-olds may be even lower if the reading, mathematics and science competences of the 15-year-olds who were not eligible to sit the PISA test were, on average, below those of eligible 15-year-olds.9 The following chapters (Chapters 5 through 10) discuss several ways of accounting for the share of 15-year-olds who were not covered by the PISA sample when comparing results across countries and over time.

Variations in population coverage are not the only differences that must be borne in mind when comparing results across countries. As discussed in PISA 2018 Results (Volume II): Where All Students Can Succeed (OECD, 2019[1]), a family’s wealth is related to its children’s performance in school, but the strength of this relationship varies markedly across countries. Similarly, the relative prosperity of some countries allows them to spend more on education, while other countries find themselves constrained by a lower national income. It is therefore important to keep the national income of countries in mind when interpreting the performance of middle-income countries, such as Colombia, Moldova, Morocco and the Philippines, compared with high-income countries (defined by the World Bank as countries whose per capita income was above USD 12 375 in 2018).10

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Figure I.4.2. Reading performance and coverage of the population of 15-year-olds in the PISA sample
Figure I.4.2. Reading performance and coverage of the population of 15-year-olds in the PISA sample

Source: OECD, PISA 2018 Database, Tables I.B1.4 and I.A2.1.

 StatLink https://doi.org/10.1787/888934028368

Resources available and invested in education

Figure I.4.3 displays the relationship between national income, as measured by per capita GDP, and students’ average reading performance.11 The figure also shows a trend line that summarises this relationship. The relationship suggests that 44 % of the variation in countries’/economies’ mean scores is related to per capita GDP (33 % in OECD countries). Countries with higher national incomes thus tend to score higher in PISA, even if the chart provides no indications about the causal nature of this relationship. The figure also shows that, although their average performance lies below the OECD average, some countries, including Belarus, Croatia and Ukraine, performed better than other countries at similar levels of economic development.

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Figure I.4.3. Mean reading performance and per capita GDP
Figure I.4.3. Mean reading performance and per capita GDP

Source: OECD, PISA 2018 Database, Tables I.B1.4 and B3.1.4.

 StatLink https://doi.org/10.1787/888934028387

While per capita GDP reflects the potential resources available for education in each country, it does not directly measure the financial resources actually invested in education. Figure I.4.4 compares countries’ cumulative spending per student from the age of six up to the age of 15, with average student performance in reading.12

Figure I.4.4 shows a positive relationship between spending per student and mean reading performance. As expenditure on educational institutions per student increases, so does a country’s mean performance; but the rate of increase diminishes quickly. Expenditure per student accounts for 49 % of the variation in mean performance between countries/economies (39 % in OECD countries).13 Relatively low spending per student needs to be taken into account when interpreting the low performance of countries such as Indonesia and the Philippines. But above USD 50 000 per student (after accounting for purchasing power parities [PPP]), a level of cumulative expenditure reached by all OECD countries except Colombia, Mexico and Turkey, spending is much less related to performance. Indeed, Estonia, which spends around USD 64 000 per student (compared to an OECD average expenditure of about USD 89 000), was one of the top-performing OECD countries in reading, mathematics and science in PISA 2018. This shows that, while education needs to be adequately resourced, and is often under-resourced in developing countries, a high level of spending per student is not required to achieve excellence in education.

In most countries, students and their families do not bear the full costs of their primary and secondary education, and often do not pay directly for it, as compulsory education is typically paid for through taxes. But students and their families directly invest their time in education. PISA 2015 highlighted significant differences in the hours of instruction per week among 15-year-old students. Students in Beijing-Shanghai-Jiangsu-Guangdong (China) (hereafter “B-S-J-G [China]”), Chile, Costa Rica, Korea, Chinese Taipei, Thailand and Tunisia spent at least 30 hours per week in regular lessons (all subjects combined), while students in Brazil, Bulgaria, Finland, Lithuania, the Slovak Republic and Uruguay spent less than 25 hours per week. Even larger differences were found in the amount of time that students spent learning outside of regular lessons, i.e. doing homework, taking additional instruction or attending private study. All subjects combined, students in B-S-J-G (China), the Dominican Republic, Qatar, Tunisia and the United Arab Emirates reported that they studied at least 25 hours per week in addition to the required school schedule; in Finland, Germany, Iceland, Japan, the Netherlands, Sweden and Switzerland, they studied less than 15 hours per week outside of school (OECD, 2016, pp. 209-217[4]).

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Figure I.4.4. Reading performance and spending on education
Figure I.4.4. Reading performance and spending on education

Source: OECD, PISA 2018 Database, Tables I.B1.4 and B3.1.1.

 StatLink https://doi.org/10.1787/888934028406

Based on information about learning time collected in PISA 2015,14 Figure I.4.5 shows the widely varied combinations of total learning time and performance that can be observed across PISA countries and economies. Countries in the upper-left quadrant can be considered more efficient, in that students reach above-average levels of proficiency but devote less time to learning than 15-year-old students on average across OECD countries. This group includes Finland, Germany, Japan and Sweden. By contrast, in several high-performing countries and economies, including B-S-J-Z (China), Korea and Singapore, students reported spending more than 50 hours per week attending regular lessons or in additional learning activities.

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Figure I.4.5. Reading performance and total learning time per week
Figure I.4.5. Reading performance and total learning time per week

Notes: Learning time is based on reports by 15-year-old students in the same country/economy in response to the PISA 2015 questionnaire.

For Beijing-Shanghai-Jiangsu-Zhejiang (China) (labelled as B-J-S-Z [China] on the chart), data on learning time amongst students from Beijing-Shanghai-Jiangsu-Guangdong (China) were used.

Source: OECD, PISA 2018 Database, Table I.B1.4; and OECD, PISA 2015 Database, Figure II.6.23.

 StatLink https://doi.org/10.1787/888934028425

The cumulative nature of PISA results

It is not only current economic conditions that matter for education; past economic conditions, and the level of education of previous generations, also influence children’s learning outcomes. Indeed, education is a cumulative process: the outcomes of one year of schooling depend on what was learned during the previous year; and the influence of the school environment is compounded by that of the family environment and of the wider social environment in which a child grows up.

There is a close inter-relationship between a student’s performance in PISA and his or her parents’ level of education (as measured by their educational qualifications); and a similarly close inter-relationship can be expected between countries’ performance in PISA and adults’ level of education and skills. When it comes to educating their children, countries with more highly educated and skilled adults are at an advantage over countries where parents have less education, or where many adults have low literacy skills. Figure I.4.6 shows the relationship between mean reading performance and the percentage of 35-44 year-olds who have attained tertiary education. This group corresponds roughly to the age group of parents of the 15-year-olds assessed in PISA. According to this simple analysis, the share of tertiary-educated 35-44 year-olds accounts for 49 % of the variation between countries/ economies (N = 41) in 15-year-old students’ mean performance (42 % across OECD countries, N = 36). Figure I.4.7 shows the relationship between mean reading performance and the average literacy score of 35-54 year-olds in countries that participated in the Survey of Adult Skills, a product of the OECD Programme for the International Assessment of Adult Competencies (PIAAC).15 Adult literacy accounts for 58 % of the variation in mean performance between countries/economies (N = 35).

When interpreting the performance of 15-year-olds in PISA, it is also important to consider that the results reflect more than the quality of lower secondary schooling (which these students have typically just completed, or are about to complete) or the quality of the upper secondary schools that they may be attending (which, in some cases, they have attended for less than a year). They also reflect the quality of learning in earlier stages of schooling, and the cognitive, emotional and social competences students had acquired before they even entered school.

A clear way of showing this is to compare the mean reading performance of 15-year-olds in PISA with the average reading performance achieved towards the end of primary school by students from a similar birth cohort who participated in the Progress in International Reading Literacy Study (PIRLS) in 2011. Some 42 countries, economies and subnational entities that participated in PISA 2018 also participated in PIRLS 2011, a study developed by the International Association for the Evaluation of Educational Achievement (Mullis et al., 2012[5]). Figure I.4.8 shows a strong correlation between the results of the reading test for 4th-grade students in PIRLS 2011 and the results of the PISA 2018 reading assessment amongst 15-year-old students (variations in PIRLS results can account for about 72 % of the variation in PISA reading results across countries and economies). Despite this clear relationship, countries that scored at similar levels in PIRLS – such as the Russian Federation and Singapore, which were amongst the highest-performing countries – can have very different mean scores in PISA. Differences between PISA and PIRLS in countries’ relative standing may reflect the influence of the intervening grades on performance, but could also be related to differences in what is measured and in who is assessed.16

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Figure I.4.6. Reading performance in PISA and educational attainment amongst 35-44 year-olds
Figure I.4.6. Reading performance in PISA and educational attainment amongst 35-44 year-olds

Source: OECD, PISA 2018 Database, Table I.B1.4; OECD (2019[6]), Education at a Glance 2019: OECD Indicators, OECD Publishing, Paris, https://doi.org/10.1787/f8d7880d-en.

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Figure I.4.7. Reading performance in PISA and literacy amongst 35-54 year-olds
Figure I.4.7. Reading performance in PISA and literacy amongst 35-54 year-olds

Note: Different countries and regions participated in the Survey of Adult Skills (PIAAC) in different years. In all countries and regions, results for 35-54 year-olds are approximated by the results of adults born between 1964 and 1983. No adjustment was made to account for changes in the skills of these adults, or for changes in the composition of these cohorts, between the year in which the Survey of Adult Skills was conducted and 2018. PISA results for the Flemish community (Belgium) are related to PIAAC results for Flanders (Belgium). PIAAC results for Ecuador are related to the country’s results in the PISA for Development assessment (2017). For the United States, PIAAC data refer to 2017.

Source: OECD, PISA 2018 Database, Table I.B1.4; OECD, Survey of Adult Skills (PIAAC) (2011-12, 2014-15, 2017).

 StatLink https://doi.org/10.1787/888934028463

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Figure I.4.8. Reading performance in PISA and 4th-graders’ performance in PIRLS 2011
Figure I.4.8. Reading performance in PISA and 4th-graders’ performance in PIRLS 2011

Notes: Only countries and economies with available data are shown.

For Morocco, 6th-grade achievement was used rather than 4th-grade achievement.

Source: OECD, PISA 2018 Database, Table I.B1.4 and Mullis, I. et al. (2012[5]), PIRLS 2011 International Results in Reading, https://timssandpirls.bc.edu/pirls2011/downloads/P11_IR_FullBook.pdf.

 StatLink https://doi.org/10.1787/888934028482

The challenges of student and language diversity

The challenges education systems face cannot be reduced to differences in the overall resources available for schooling or in the extent to which families and society at large support students’ acquisition of core skills. Student diversity, related, for example, to socio-economic inequality and students not speaking the language of instruction at home, must also be considered. The challenge for teachers and education systems is to overcome inequalities and at the same time exploit the benefits of diversity in the classroom (OECD, 2010[7]; OECD, 2019[8]).

Figure I.4.9 shows how the standard deviation of reading performance, described earlier, relates to a measure of socio-economic heterogeneity within the country (the standard deviation of the PISA index of economic, social and cultural status); see Chapter 2 in PISA 2018 Results (Volume II): Where All Students Can Succeed (OECD, 2019[1]). There is no strong relationship across countries and economies between the magnitude of socio-economic inequalities and the extent to which learning outcomes vary (this also holds after accounting for mean performance in reading). However, some countries (including Brazil, Lebanon and Luxembourg) have comparatively large variations in socio-economic conditions amongst their students, and also larger variations in learning outcomes amongst their students than that observed in countries with similar overall performance or at similar levels of economic development.

How well students read in the language of instruction is influenced by whether they commonly speak that language at home and, more generally, outside of school, and whether specific support is available for bilingual students and for non-native language learners.17 Specific policies may also be required to help integrate students with an immigrant background into host societies (OECD, 2019[8]); also see PISA 2018 Results (Volume II): Where All Students Can Succeed (OECD, 2019[1]), Chapters 9 and 10. But even when such policies are in place, the performance of students who immigrated to the country in which they were assessed can be only partially attributed to their host country’s education system.

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Figure I.4.9. Variation in reading performance and in students’ socio-economic status
Figure I.4.9. Variation in reading performance and in students’ socio-economic status

Source: OECD, PISA 2018 Database, Tables I.B1.4 and II.B1.2.1.

 StatLink https://doi.org/10.1787/888934028501

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Figure I.4.10. First-generation immigrant students
Based on students’ reports
Figure I.4.10. First-generation immigrant students

Note: Only countries and economies where the percentage of first-generation immigrant students is higher than 3 % are shown.

Countries and economies are ranked in descending order of the percentage of first-generation immigrant students.

Source: OECD, PISA 2018 Database, Table II.B1.9.9.

 StatLink https://doi.org/10.1787/888934028520

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Figure I.4.11. Students who do not speak the language of instruction at home
Based on students’ reports about what language they speak at home most of the time
Figure I.4.11. Students who do not speak the language of instruction at home

Countries and economies are ranked in descending order of the percentage of students who speak, most of the time, a language different from the language of instruction at home.

Source: OECD, PISA 2018 Database, Table II.B1.9.2.

 StatLink https://doi.org/10.1787/888934028539

Figure I.4.10 and Figure I.4.11 show the countries where immigration and linguistic diversity are most pronounced.18 In 2018, more than one in five students in Qatar (40 %), the United Arab Emirates (33 %), Macao (China) (26 %) and Luxembourg (25 %) were first-generation immigrants, meaning that they were born outside of the country/economy and their parents were also born outside of the country/economy. In Canada, Singapore, New Zealand, Australia, Hong Kong (China) and Switzerland (in descending order of that share), more than 10 % of students were first-generation immigrants. However, some of these immigrants may have already spoken the language of instruction when they arrived. Immigrant students’ performance and characteristics are the topic of Chapters 9 and 10 in PISA 2018 Results (Volume II): Where All Students Can Succeed (OECD, 2019[1]).

On the other hand, great linguistic diversity may exist even in countries that have relatively small shares of immigrant students. More than 80 % of students in Lebanon, the Philippines, Brunei Darussalam, Morocco, Luxembourg and Malta (in descending order of that share), and between 41 % and 53 % of students in Indonesia, Singapore and the United Arab Emirates reported that, most of the time, they speak a different language at home from the language of instruction.

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Table I.4.4. Reading performance at national and subnational levels

Reading scale

Mean score

95 % confidence interval

Range of ranks

OECD countries

All countries/economies

Countries/economies assessing students on computers

Upper rank

Lower rank

Upper rank

Lower rank

Upper rank

Lower rank

B-S-J-Z (China)

555

550 - 561

1

2

1

2

Singapore

549

546 - 553

1

2

1

2

Alberta (Canada)

532

523 - 540

Macao (China)

525

523 - 528

3

5

3

5

Hong Kong (China)1

524

519 - 530

3

7

3

7

Ontario (Canada)

524

517 - 531

Estonia

523

519 - 527

1

3

3

7

3

7

Canada

520

517 - 524

1

4

4

8

4

8

Finland

520

516 - 525

1

5

4

9

4

9

Québec (Canada)

519

513 - 526

British Columbia (Canada)

519

511 - 528

Ireland

518

514 - 522

1

5

5

9

5

9

Nova Scotia (Canada)

516

508 - 523

Korea

514

508 - 520

2

7

6

11

6

11

Newfoundland and Labrador (Canada)

512

503 - 520

Poland

512

507 - 517

4

8

8

12

8

12

Sweden

506

500 - 512

6

14

10

19

10

19

New Zealand

506

502 - 510

6

12

10

17

10

17

United States1

505

498 - 512

6

15

10

20

10

20

England (United Kingdom)

505

499 - 511

Scotland (United Kingdom)

504

498 - 510

United Kingdom

504

499 - 509

7

15

11

20

11

20

Japan

504

499 - 509

7

15

11

20

11

20

Australia

503

499 - 506

8

14

12

19

12

19

Chinese Taipei

503

497 - 508

11

20

11

20

Prince Edward Island (Canada)

503

486 - 519

Flemish Community (Belgium)

502

495 - 509

Denmark

501

498 - 505

9

15

13

20

13

20

Northern Ireland (United Kingdom)

501

493 - 509

Norway

499

495 - 504

10

17

14

22

14

22

Saskatchewan (Canada)

499

493 - 505

Germany

498

492 - 504

10

19

14

24

14

24

Trento (Italy)

496

491 - 501

Bolzano (Italy)

495

489 - 502

Slovenia

495

493 - 498

14

18

19

23

19

23

Manitoba (Canada)

494

488 - 501

Belgium

493

488 - 497

15

20

20

26

20

26

France

493

488 - 497

15

21

20

26

20

26

Portugal1

492

487 - 497

15

21

20

26

20

26

Czech Republic

490

485 - 495

16

22

21

27

21

27

New Brunswick (Canada)

489

482 - 496

Moscow region (Russia)

486

477 - 495

Netherlands1

485

480 - 490

20

24

24

30

24

30

Reading scale

Mean score

95 % confidence interval

Range of ranks

OECD countries

All countries/economies

Countries/economies assessing students on computers

Upper rank

Lower rank

Upper rank

Lower rank

Upper rank

Lower rank

Austria

484

479 - 490

20

24

24

30

24

30

Switzerland

484

478 - 490

19

25

24

31

24

31

Wales (United Kingdom)

483

476 - 491

German-speaking Community (Belgium)

483

474 - 492

Toscana (Italy)

482

475 - 490

French Community (Belgium)

481

475 - 487

Croatia

479

474 - 484

27

36

27

36

Latvia

479

476 - 482

23

27

28

34

28

34

Russia

479

472 - 485

26

36

26

36

Italy

476

472 - 481

23

29

29

37

29

37

Hungary

476

472 - 480

24

29

29

37

29

37

Lithuania

476

473 - 479

24

28

29

36

30

36

Iceland

474

471 - 477

25

29

31

38

31

37

Belarus

474

469 - 479

30

38

30

38

Israel

470

463 - 478

25

31

31

40

31

39

Luxembourg

470

468 - 472

29

31

36

39

36

39

Ukraine

466

459 - 473

36

41

Turkey

466

461 - 470

30

32

38

41

38

40

Republic of Tatarstan (Russia)

463

456 - 469

Sardegna (Italy)

462

454 - 470

Slovak Republic

458

454 - 462

32

34

40

43

40

42

Greece

457

450 - 465

31

34

40

43

39

42

Bogotá (Colombia)

455

444 - 465

CABA (Argentina)

454

443 - 464

Chile

452

447 - 457

33

34

42

44

41

43

Malta

448

445 - 452

43

44

42

43

Serbia

439

433 - 446

45

46

44

45

South (Brazil)

432

420 - 444

United Arab Emirates

432

427 - 436

45

48

44

47

Romania

428

418 - 438

45

55

Astana (Kazakhstan)

428

413 - 442

Córdoba (Argentina)

427

418 - 436

Uruguay

427

422 - 433

46

52

45

49

Costa Rica

426

420 - 433

46

54

45

50

Middle-West (Brazil)

425

407 - 443

Almaty (Kazakhstan)

424

409 - 440

Cyprus

424

422 - 427

48

53

46

50

Moldova

424

419 - 429

47

54

Southeast (Brazil)

424

418 - 430

Karagandy region (Kazakhstan)

422

409 - 436

Montenegro

421

419 - 423

50

55

48

51

Mexico

420

415 - 426

35

36

49

57

47

52

Bulgaria

420

412 - 428

48

58

46

53

Reading scale

Mean score

95 % confidence interval

Range of ranks

OECD countries

All countries/economies

Countries/economies assessing students on computers

Upper rank

Lower rank

Upper rank

Lower rank

Upper rank

Lower rank

Jordan

419

413 - 425

49

57

Kostanay region (Kazakhstan)

417

407 - 427

Malaysia

415

409 - 421

53

58

50

54

DI Yogyakarta (Indonesia)

414

402 - 425

PBA (Argentina)

413

402 - 424

Brazil

413

409 - 417

55

59

51

54

North-Kazakhstan region (Kazakhstan)

413

403 - 422

DKI Jakarta (Indonesia)

412

399 - 426

Colombia

412

406 - 419

35

36

54

61

51

57

Brunei Darussalam

408

406 - 410

58

61

54

57

Qatar

407

406 - 409

59

62

55

58

Albania

405

402 - 409

59

64

55

59

East-Kazakhstan region (Kazakhstan)

405

392 - 418

Bosnia and Herzegovina

403

397 - 409

59

65

55

59

Argentina

402

396 - 407

60

66

Peru

401

395 - 406

61

66

57

60

Saudi Arabia

399

393 - 405

61

66

Akmola region (Kazakhstan)

395

386 - 404

Thailand

393

387 - 399

64

69

59

62

North Macedonia

393

391 - 395

66

68

North (Brazil)

392

379 - 406

Pavlodar region (Kazakhstan)

391

378 - 403

Baku (Azerbaijan)

389

384 - 394

66

69

60

62

Northeast (Brazil)

389

381 - 397

Tucumán (Argentina)

389

379 - 399

Kazakhstan

387

384 - 390

68

69

61

62

Aktobe region (Kazakhstan)

381

372 - 389

Georgia

380

376 - 384

70

71

63

64

West-Kazakhstan region (Kazakhstan)

378

369 - 388

Panama

377

371 - 383

70

72

63

65

Indonesia

371

366 - 376

71

72

64

65

Zhambyl region (Kazakhstan)

369

362 - 376

South-Kazakhstan region (Kazakhstan)

368

361 - 375

Kyzyl-Orda region (Kazakhstan)

366

361 - 372

Mangistau region (Kazakhstan)

361

349 - 372

Almaty region (Kazakhstan)

360

351 - 369

Morocco

359

353 - 366

73

74

66

67

Lebanon

353

345 - 362

73

75

Kosovo

353

351 - 355

74

75

66

67

Atyrau region (Kazakhstan)

344

335 - 352

Dominican Republic

342

336 - 347

76

77

68

69

Philippines

340

333 - 346

76

77

68

69

1. Data did not meet the PISA technical standards but were accepted as largely comparable (see Annexes A2 and A4).

Notes: OECD countries are shown in bold black. Partner countries, economies and subnational entities that are not included in national results are shown in bold blue.

Regions are shown in black italics (OECD countries) or blue italics (partner countries).

Range-of-rank estimates are computed based on mean and standard-error-of-the-mean estimates for each country/economy, and take into account multiple comparisons amongst countries and economies at similar levels of performance. For an explanation of the method, see Annex A3.

Countries and economies are ranked in descending order of mean reading performance.

Source: OECD, PISA 2018 Database.

 StatLink https://doi.org/10.1787/888934028292

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Table I.4.5. Mathematics performance at national and subnational levels

Mathematics scale

Mean score

95 % confidence interval

Range of ranks

OECD countries

All countries/economies

Countries/economies assessing students on computers

Upper rank

Lower rank

Upper rank

Lower rank

Upper rank

Lower rank

B-S-J-Z (China)

591

586 - 596

1

1

1

1

Singapore

569

566 - 572

2

2

2

2

Macao (China)

558

555 - 561

3

4

3

4

Hong Kong (China)1

551

545 - 557

3

4

3

4

Québec (Canada)

532

525 - 539

Chinese Taipei

531

525 - 537

5

7

5

7

Japan

527

522 - 532

1

3

5

8

5

8

Korea

526

520 - 532

1

4

5

9

5

9

Estonia

523

520 - 527

1

4

6

9

6

9

Bolzano (Italy)

521

515 - 528

Netherlands1

519

514 - 524

2

6

7

11

7

11

Trento (Italy)

518

513 - 523

Flemish Community (Belgium)

518

511 - 524

Poland

516

511 - 521

4

8

9

13

9

13

Switzerland

515

510 - 521

4

9

9

14

9

14

Ontario (Canada)

513

504 - 521

Canada

512

507 - 517

5

11

10

16

10

16

Alberta (Canada)

511

501 - 521

Denmark

509

506 - 513

6

11

11

16

11

16

Slovenia

509

506 - 512

7

11

12

16

12

16

Belgium

508

504 - 513

7

13

12

18

12

18

Finland

507

503 - 511

7

13

12

18

12

18

German-speaking Community (Belgium)

505

495 - 515

British Columbia (Canada)

504

494 - 515

England (United Kingdom)

504

498 - 510

Navarre (Spain)

503

486 - 519

Castile and León (Spain)

502

493 - 512

Sweden

502

497 - 508

10

19

15

24

15

24

United Kingdom

502

497 - 507

10

19

15

24

15

24

Norway

501

497 - 505

11

19

16

24

16

24

Germany

500

495 - 505

11

21

16

26

16

26

Ireland

500

495 - 504

12

21

17

26

17

26

Czech Republic

499

495 - 504

12

21

17

26

17

26

Basque Country (Spain)

499

492 - 506

Austria

499

493 - 505

12

23

17

28

17

28

Cantabria (Spain)

499

484 - 514

Galicia (Spain)

498

490 - 507

La Rioja (Spain)

497

478 - 517

Aragon (Spain)

497

485 - 508

Latvia

496

492 - 500

15

23

20

28

20

28

Toscana (Italy)

496

487 - 504

France

495

491 - 500

15

24

20

29

20

29

Iceland

495

491 - 499

16

24

21

29

21

29

French Community (Belgium)

495

490 - 501

New Zealand

494

491 - 498

18

24

22

29

22

29

Nova Scotia (Canada)

494

482 - 507

Portugal1

492

487 - 498

18

26

23

31

23

31

Northern Ireland (United Kingdom)

492

484 - 500

Australia

491

488 - 495

20

25

25

31

25

31

Mathematics scale

Mean score

95 % confidence interval

Range of ranks

OECD countries

All countries/economies

Countries/economies assessing students on computers

Upper rank

Lower rank

Upper rank

Lower rank

Upper rank

Lower rank

New Brunswick (Canada)

491

480 - 502

Asturias (Spain)

491

481 - 500

Catalonia (Spain)

490

482 - 498

Scotland (United Kingdom)

489

481 - 497

Newfoundland and Labrador (Canada)

488

476 - 501

Russia

488

482 - 494

27

35

27

35

Wales (United Kingdom)

487

479 - 495

Italy

487

481 - 492

23

29

28

35

28

35

Prince Edward Island (Canada)

487

465 - 508

Slovak Republic

486

481 - 491

23

29

28

35

28

35

Madrid (Spain)

486

479 - 492

Saskatchewan (Canada)

485

475 - 495

Luxembourg

483

481 - 486

25

29

31

36

31

36

Balearic Islands (Spain)

483

472 - 493

Manitoba (Canada)

482

474 - 489

Spain

481

479 - 484

26

31

32

37

32

37

Lithuania

481

477 - 485

26

31

32

37

32

37

Hungary

481

477 - 486

26

31

31

37

31

37

Castile-La Mancha (Spain)

479

469 - 489

United States1

478

472 - 485

27

31

32

39

32

39

Murcia (Spain)

474

462 - 485

Comunidad Valenciana (Spain)

473

465 - 482

Belarus

472

467 - 477

37

40

37

40

Malta

472

468 - 475

37

39

37

39

Extremadura (Spain)

470

457 - 482

Andalusia (Spain)

467

459 - 476

Sardegna (Italy)

467

459 - 475

Croatia

464

459 - 469

39

41

40

41

Israel

463

456 - 470

32

32

39

42

39

41

Canary Islands (Spain)

460

452 - 469

Zhambyl region (Kazakhstan)

456

444 - 467

Turkey

454

449 - 458

33

34

42

46

42

45

Ukraine

453

446 - 460

41

46

Greece

451

445 - 457

33

34

42

46

42

45

Cyprus

451

448 - 453

42

46

42

45

Astana (Kazakhstan)

450

435 - 466

Almaty (Kazakhstan)

448

434 - 463

Serbia

448

442 - 454

42

47

42

46

Kostanay region (Kazakhstan)

448

435 - 461

Karagandy region (Kazakhstan)

446

431 - 460

Malaysia

440

435 - 446

46

50

45

49

Pavlodar region (Kazakhstan)

438

426 - 449

Albania

437

432 - 442

47

51

46

49

East-Kazakhstan region (Kazakhstan)

437

423 - 451

Bulgaria

436

429 - 444

47

53

46

51

United Arab Emirates

435

431 - 439

47

51

46

50

CABA (Argentina)

434

425 - 444

North-Kazakhstan region (Kazakhstan)

433

422 - 443

Melilla (Spain)

432

411 - 452

Mathematics scale

Mean score

95 % confidence interval

Range of ranks

OECD countries

All countries/economies

Countries/economies assessing students on computers

Upper rank

Lower rank

Upper rank

Lower rank

Upper rank

Lower rank

Brunei Darussalam

430

428 - 432

50

53

49

51

Romania

430

420 - 440

47

56

DI Yogyakarta (Indonesia)

430

417 - 442

Montenegro

430

427 - 432

50

53

49

51

Bogotá (Colombia)

430

420 - 439

Kazakhstan

423

419 - 427

53

57

52

54

DKI Jakarta (Indonesia)

421

406 - 436

Moldova

421

416 - 425

54

59

Aktobe region (Kazakhstan)

420

408 - 432

Baku (Azerbaijan)

420

414 - 425

54

60

52

57

Kyzyl-Orda region (Kazakhstan)

419

403 - 436

Thailand

419

412 - 425

53

60

52

57

West-Kazakhstan region (Kazakhstan)

418

405 - 430

Uruguay

418

413 - 423

54

60

52

57

Chile

417

413 - 422

35

35

55

60

53

57

Qatar

414

412 - 417

58

61

55

58

Ceuta (Spain)

411

387 - 435

Akmola region (Kazakhstan)

411

399 - 424

Mexico

409

404 - 414

36

36

60

63

57

60

Bosnia and Herzegovina

406

400 - 412

61

65

58

61

Costa Rica

402

396 - 409

61

66

58

62

South-Kazakhstan region (Kazakhstan)

401

390 - 412

South (Brazil)

401

391 - 412

Córdoba (Argentina)

400

392 - 409

Peru

400

395 - 405

62

67

59

62

Jordan

400

393 - 406

62

68

Almaty region (Kazakhstan)

399

389 - 409

Georgia

398

392 - 403

63

68

60

63

Middle-West (Brazil)

396

379 - 412

North Macedonia

394

391 - 398

65

69

Lebanon

393

386 - 401

63

69

Southeast (Brazil)

392

386 - 398

Colombia

391

385 - 397

37

37

66

70

62

64

Mangistau region (Kazakhstan)

391

373 - 409

PBA (Argentina)

387

377 - 397

Brazil

384

380 - 388

69

72

64

65

Atyrau region (Kazakhstan)

382

368 - 396

Argentina

379

374 - 385

70

73

Indonesia

379

373 - 385

70

73

64

65

Saudi Arabia

373

367 - 379

71

74

Morocco

368

361 - 374

73

75

66

67

North (Brazil)

366

352 - 380

Kosovo

366

363 - 369

74

75

66

67

Tucumán (Argentina)

364

354 - 374

Northeast (Brazil)

363

356 - 371

Panama

353

348 - 358

76

77

68

69

Philippines

353

346 - 359

76

77

68

69

Dominican Republic

325

320 - 330

78

78

70

70

1. Data did not meet the PISA technical standards but were accepted as largely comparable (see Annexes A2 and A4).

Notes: OECD countries are shown in bold black. Partner countries, economies and subnational entities that are not included in national results are shown in bold blue.

Regions are shown in black italics (OECD countries) or blue italics (partner countries).

Range-of-rank estimates are computed based on mean and standard-error-of-the-mean estimates for each country/economy, and take into account multiple comparisons amongst countries and economies at similar levels of performance. For an explanation of the method, see Annex A3.

Countries and economies are ranked in descending order of mean mathematics performance.

Source: OECD, PISA 2018 Database.

 StatLink https://doi.org/10.1787/888934028311

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Table I.4.6. Science performance at national and subnational levels

Science scale

Mean score

95 % confidence interval

Range of ranks

OECD countries

All countries/economies

Countries/economies assessing students on computers

Upper rank

Lower rank

Upper rank

Lower rank

Upper rank

Lower rank

B-S-J-Z (China)

590

585 - 596

1

1

1

1

Singapore

551

548 - 554

2

2

2

2

Macao (China)

544

541 - 546

3

3

3

3

Alberta (Canada)

534

525 - 542

Estonia

530

526 - 534

1

2

4

5

4

5

Japan

529

524 - 534

1

3

4

6

4

6

Finland

522

517 - 527

2

5

5

9

5

9

Québec (Canada)

522

514 - 529

Korea

519

514 - 525

3

5

6

10

6

10

Ontario (Canada)

519

511 - 526

Canada

518

514 - 522

3

5

6

10

6

10

Hong Kong (China)1

517

512 - 522

6

11

6

11

British Columbia (Canada)

517

506 - 527

Chinese Taipei

516

510 - 521

6

11

6

11

Poland

511

506 - 516

5

9

9

14

9

14

Galicia (Spain)

510

503 - 518

Flemish Community (Belgium)

510

503 - 516

New Zealand

508

504 - 513

6

10

10

15

10

15

Nova Scotia (Canada)

508

499 - 517

England (United Kingdom)

507

501 - 513

Slovenia

507

505 - 509

6

11

11

16

11

16

Newfoundland and Labrador (Canada)

506

494 - 519

United Kingdom

505

500 - 510

6

14

11

19

11

19

Netherlands1

503

498 - 509

7

16

12

21

12

21

Germany

503

497 - 509

7

16

12

21

12

21

Australia

503

499 - 506

8

15

13

20

13

20

United States1

502

496 - 509

7

18

12

23

12

23

Prince Edward Island (Canada)

502

484 - 519

Castile and León (Spain)

501

491 - 511

Saskatchewan (Canada)

501

493 - 508

Sweden

499

493 - 505

9

19

14

24

14

24

Belgium

499

494 - 503

11

19

16

24

16

24

Bolzano (Italy)

498

490 - 506

Czech Republic

497

492 - 502

12

21

17

26

17

26

Asturias (Spain)

496

487 - 505

Ireland

496

492 - 500

13

21

18

26

18

26

Cantabria (Spain)

495

477 - 513

Switzerland

495

489 - 501

13

23

18

28

18

28

Trento (Italy)

495

491 - 499

Aragon (Spain)

493

483 - 504

France

493

489 - 497

16

23

21

28

21

28

Denmark

493

489 - 496

16

23

21

28

21

28

New Brunswick (Canada)

492

481 - 504

Navarre (Spain)

492

480 - 504

Portugal1

492

486 - 497

16

24

21

29

21

29

Northern Ireland (United Kingdom)

491

482 - 500

Norway

490

486 - 495

18

24

23

29

23

29

Scotland (United Kingdom)

490

482 - 498

Austria

490

484 - 495

18

25

23

30

23

30

Science scale

Mean score

95 % confidence interval

Range of ranks

OECD countries

All countries/economies

Countries/economies assessing students on computers

Upper rank

Lower rank

Upper rank

Lower rank

Upper rank

Lower rank

Manitoba (Canada)

489

482 - 497

Catalonia (Spain)

489

479 - 498

Wales (United Kingdom)

488

481 - 496

Basque Country (Spain)

487

479 - 496

Latvia

487

484 - 491

21

25

26

30

26

30

Madrid (Spain)

487

481 - 493

La Rioja (Spain)

487

471 - 502

French Community (Belgium)

485

479 - 490

Castile-La Mancha (Spain)

484

473 - 496

German-speaking Community (Belgium)

483

469 - 498

Spain

483

480 - 486

24

27

29

32

29

32

Balearic Islands (Spain)

482

472 - 492

Lithuania

482

479 - 485

25

27

30

33

30

33

Hungary

481

476 - 485

24

28

29

34

29

34

Murcia (Spain)

479

468 - 490

Russia

478

472 - 483

30

37

30

36

Comunidad Valenciana (Spain)

478

469 - 486

Luxembourg

477

474 - 479

27

29

32

36

32

36

Iceland

475

472 - 479

28

30

33

37

33

37

Toscana (Italy)

475

467 - 483

Extremadura (Spain)

473

462 - 485

Croatia

472

467 - 478

33

40

33

39

Belarus

471

466 - 476

34

40

34

39

Andalusia (Spain)

471

462 - 480

Canary Islands (Spain)

470

461 - 478

Ukraine

469

463 - 475

35

42

Turkey

468

464 - 472

30

32

36

41

36

40

Italy

468

463 - 473

30

33

36

42

36

41

Slovak Republic

464

460 - 469

30

33

39

42

38

41

Israel

462

455 - 469

30

33

38

43

38

42

Malta

457

453 - 460

42

44

41

43

CABA (Argentina)

455

444 - 465

Sardegna (Italy)

452

444 - 460

Greece

452

445 - 458

34

35

43

45

42

44

Bogotá (Colombia)

451

441 - 460

Chile

444

439 - 448

35

35

44

47

43

46

Serbia

440

434 - 446

45

49

44

48

DI Yogyakarta (Indonesia)

439

429 - 449

Cyprus

439

436 - 442

45

48

44

47

Melilla (Spain)

439

424 - 454

Malaysia

438

432 - 443

45

50

44

48

United Arab Emirates

434

430 - 438

47

52

47

50

Brunei Darussalam

431

429 - 433

49

53

48

50

Almaty (Kazakhstan)

431

414 - 447

Jordan

429

424 - 435

49

56

Moldova

428

424 - 433

49

55

Astana (Kazakhstan)

428

413 - 443

DKI Jakarta (Indonesia)

428

415 - 441

Karagandy region (Kazakhstan)

428

414 - 442

Science scale

Mean score

95 % confidence interval

Range of ranks

OECD countries

All countries/economies

Countries/economies assessing students on computers

Upper rank

Lower rank

Upper rank

Lower rank

Upper rank

Lower rank

Córdoba (Argentina)

427

418 - 437

Kostanay region (Kazakhstan)

426

415 - 438

Thailand

426

420 - 432

50

58

49

54

Uruguay

426

421 - 431

51

57

49

53

Romania

426

417 - 435

49

60

Bulgaria

424

417 - 431

50

59

49

55

South (Brazil)

419

408 - 431

Mexico

419

414 - 424

36

37

55

62

51

57

North-Kazakhstan region (Kazakhstan)

419

409 - 429

Qatar

419

417 - 421

56

60

52

56

Albania

417

413 - 421

57

63

53

58

Costa Rica

416

409 - 422

56

63

52

58

Middle-West (Brazil)

415

399 - 431

Ceuta (Spain)

415

402 - 428

Montenegro

415

413 - 418

58

63

54

58

Southeast (Brazil)

414

408 - 419

PBA (Argentina)

413

403 - 424

East-Kazakhstan region (Kazakhstan)

413

402 - 424

Colombia

413

407 - 419

36

37

58

64

54

59

Pavlodar region (Kazakhstan)

413

401 - 425

North Macedonia

413

410 - 416

60

63

Peru

404

399 - 409

63

67

58

61

Argentina

404

398 - 410

63

68

Brazil

404

400 - 408

64

67

59

61

Akmola region (Kazakhstan)

401

391 - 411

Bosnia and Herzegovina

398

393 - 404

65

70

60

64

Baku (Azerbaijan)

398

393 - 402

66

70

60

64

Zhambyl region (Kazakhstan)

397

389 - 406

Kazakhstan

397

394 - 400

67

70

61

64

Indonesia

396

391 - 401

67

70

61

64

West-Kazakhstan region (Kazakhstan)

391

381 - 401

Tucumán (Argentina)

391

381 - 401

Aktobe region (Kazakhstan)

389

379 - 399

Saudi Arabia

386

381 - 392

71

73

North (Brazil)

384

373 - 396

Lebanon

384

377 - 391

71

74

Georgia

383

378 - 387

71

74

65

66

Northeast (Brazil)

383

375 - 390

Almaty region (Kazakhstan)

380

371 - 390

Morocco

377

371 - 382

73

74

65

66

Kyzyl-Orda region (Kazakhstan)

374

365 - 384

South-Kazakhstan region (Kazakhstan)

373

366 - 380

Kosovo

365

363 - 367

75

76

67

68

Panama

365

359 - 370

75

77

67

69

Mangistau region (Kazakhstan)

365

355 - 374

Atyrau region (Kazakhstan)

361

350 - 371

Philippines

357

351 - 363

76

77

68

69

Dominican Republic

336

331 - 341

78

78

70

70

1. Data did not meet the PISA technical standards but were accepted as largely comparable (see Annexes A2 and A4).

Notes: OECD countries are shown in bold black. Partner countries, economies and subnational entities that are not included in national results are shown in bold blue.

Regions are shown in black italics (OECD countries) or blue italics (partner countries).

Range-of-rank estimates are computed based on mean and standard-error-of-the-mean estimates for each country/economy, and take into account multiple comparisons amongst countries and economies at similar levels of performance. For an explanation of the method, see Annex A3.

Countries and economies are ranked in descending order of mean mathematics performance.

Source: OECD, PISA 2018 Database.

 StatLink https://doi.org/10.1787/888934028330

References

[11] Bialystok, E. (2011), “Reshaping the mind: The benefits of bilingualism.”, Canadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale, Vol. 65/4, pp. 229-235, http://dx.doi.org/10.1037/a0025406.

[10] Bialystok, E. et al. (2009), “Bilingual Minds”, Psychological Science in the Public Interest, Vol. 10/3, pp. 89-129, http://dx.doi.org/10.1177/1529100610387084.

[5] Mullis, I. et al. (2012), PIRLS 2011 International Results in Reading, TIMSS & PIRLS International Study Center and International Association for the Evaluation of Educational Achievement (IEA), https://timssandpirls.bc.edu/pirls2011/downloads/P11_IR_FullBook.pdf (accessed on 3 July 2019).

[6] OECD (2019), Education at a Glance 2019: OECD Indicators, OECD Publishing, Paris, https://doi.org/10.1787/f8d7880d-en.

[1] OECD (2019), PISA 2018 Results (Volume II): Where All Students Can Succeed, PISA, OECD Publishing, Paris, https://doi.org/10.1787/b5fd1b8f-en.

[8] OECD (2019), The Road to Integration: Education and Migration, OECD Reviews of Migrant Education, OECD Publishing, Paris, https://dx.doi.org/10.1787/d8ceec5d-en.

[4] OECD (2016), PISA 2015 Results (Volume II): Policies and Practices for Successful Schools, PISA, OECD Publishing, Paris, https://dx.doi.org/10.1787/9789264267510-en.

[7] OECD (2010), Educating Teachers for Diversity: Meeting the Challenge, Educational Research and Innovation, OECD Publishing, Paris, https://dx.doi.org/10.1787/9789264079731-en.

[2] Ward, M. (2018), “PISA for Development: Results in Focus”, PISA in Focus, No. 91, OECD Publishing, Paris, https://dx.doi.org/10.1787/c094b186-en.

[9] Worden, J. (2012), “Bilingual education policy and language learning in Estonia and Singapore”, in Languages in a Global World: Learning for Better Cultural Understanding, OECD Publishing, Paris, https://dx.doi.org/10.1787/9789264123557-11-en.

[3] World Bank (2017), World Development Report 2018: Learning to Realize Education’s Promise, The World Bank, http://dx.doi.org/10.1596/978-1-4648-1096-1.

Notes

← 1. Because the membership of the OECD has changed over time, the three categories (around, above and below the OECD mean) are not comparable to the corresponding categories used in earlier PISA reports.

← 2. See Annex A5 for a discussion of how the scales are linked, and of the comparability of results between paper- and computer-based assessments.

← 3. While score points in reading, mathematics and science are not comparable, differences in scores can be compared through a standardised effect-size metric, such as Cohen’s d.

← 4. In reading, 220 points is approximately equal to the distance between the mid-point of Proficiency Level 5 – a level at which students can comprehend lengthy texts, deal with concepts that are abstract or counterintuitive, and establish distinctions between fact and opinion, based on implicit cues pertaining to the content or source of the information – and the mid-point of Proficiency Level 2 – a level at which students are capable of identifying the main idea in a text of moderate length, of finding information based on explicit though sometimes complex criteria, and of reflecting on the purpose and form of texts only when explicitly directed to do so, but have difficulty with reading tasks that do not contain explicit cues or that do contain distractors and competing information (see Chapter 5 for more detailed descriptions of what students can do at different levels of the reading scale).

← 5. In reading, students in Singapore who reported that they do not speak English at home scored 54 points (S.E.: 3.3 points) below students who reported that they speak English at home; in mathematics, the difference was only 32 points (S.E.: 2.9 points).

← 6. In this report, the range of ranks is defined as the 97.5 % confidence interval for the rank statistic. This means that there is at least a 97.5 % probability that the interval defined by the upper and lower ranks, and computed based on PISA samples, contains the true rank of the country/economy (see Annex A3).

← 7. The lowest rank of country/economy A is not merely given by the number of countries/economies whose mean scores are above those of country/economy A in Table I.4.1, Table I.4.2, and Table I.4.3, and whose names are not listed amongst the non-significant differences compared to country/economy A in those tables. For more details about the methodology behind the computation of a confidence interval for the rank, see Annex A3.

← 8. In addition to adjudicated subnational entities, whose data were carefully reviewed against technical and scientific standards, the table also includes any subnational entity that constituted one or more explicit sampling strata and that achieved, through deliberate over-sampling or sometimes, due to its large size within the country, a sample of at least 25 participating schools and 875 assessed students. It also includes some subnational entities that conducted a census, and where the country requested that results be reported at the subnational level. For non-adjudicated entities, response rates were not assessed separately from those of the country as a whole, and results must be interpreted with caution.

← 9. If the distribution of performance amongst the eligible 15-year-olds (first-order) stochastically dominates that of the non-eligible 15-year-olds, then the mean and all percentiles of the PISA target population represent an upper bound on the percentiles of the population encompassing all 15-year-olds.

← 10. See https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups (accessed on 23 August 2019).

← 11. The GDP values represent per capita GDP in 2018 at current prices, expressed in USD. The conversion from local currencies to equivalent USD accounts for differences in purchasing power across countries and economies.

← 12. Spending per student is approximated by multiplying the expenditure per student on educational institutions in 2018 (from public and private sources), at each level of education, by the theoretical duration of education at the respective level, up to the age of 15. Cumulative expenditure for a given country is approximated as follows: let n0, n1 and n2 be the typical number of years spent by a student from the age of 6 up to the age of 15 in primary, lower secondary and upper secondary education. Let E0, E1 and E2 be the annual expenditure per student in USD converted using purchasing power parity in primary, lower secondary and upper secondary education, respectively. The cumulative expenditure is then calculated by multiplying current annual expenditure for each level of education by the typical duration of study in that level, using the following formula: CE=n0E0+n1E1+n2 E2.

← 13. The countries and economies included in each analysis may vary due to data availability. The percentage of variation in mean reading performance accounted for by each variable cannot therefore be directly compared.

← 14. The indicator of total learning time computed based on 2015 data is used as a proxy for the time investment of PISA 2018 students, because PISA 2018 did not collect data on out-of-school learning time.

← 15. Different countries participated in the Survey of Adult Skills (PIAAC) in different years. In all countries, results for 35-54 year-olds are approximated by the results of adults born between 1964 and 1983. No adjustment is made to account for changes in the skills of these adults, or for changes in the composition of these cohorts, between the year in which the survey was conducted and 2018. PISA results for the Flemish Community of Belgium are related to PIAAC results for Flanders (Belgium). PIAAC results for Ecuador are related to the country’s results in the PISA for Development assessment (2017). For the United States, PIAAC data refer to 2017.

← 16. PISA and PIRLS assess different constructs and different samples. For example, PIRLS uses a grade-based definition of the target population, while PISA uses an age-based definition. Dropout between the end of primary school and the age of 15 may reduce the comparability of samples across assessments. Also note that the cohort that was assessed in PIRLS 2011 differs by 1 or 2 years, in most cases, from the cohort assessed in PISA 2018. In addition, cohort composition could have changed in some countries and economies due to migration. It is beyond the scope of this chapter to analyse these differences in detail.

← 17. As noted in Worden (2012[9]), bilingualism and multilingualism can have multiple benefits for students and should be encouraged. Bilingualism, in particular, is associated with enhanced executive control (Bialystok, 2011[11]). Despite the many advantages of bilingualism, it has been shown that bilingual children, on average, know significantly fewer words in each language than comparable monolingual children (Bialystok et al., 2009[10]). Several high-performing countries in PISA have large shares of bilingual students, including Singapore, one of the highest-performing countries in all subjects, and Switzerland, which scores around the OECD average in reading, but above the OECD average in mathematics.

← 18. International PISA data cannot describe all aspects of ethnic diversity. For example, in Australia, New Zealand or in the Americas, PISA measures of linguistic diversity and immigrant status do not necessarily cover indigenous populations, which use the language of instruction in everyday life.

Disclaimer

This work is published under the responsibility of the Secretary-General of the OECD. The opinions expressed and arguments employed herein do not necessarily reflect the official views of OECD member countries.

This document, as well as any data and 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.

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 OECD 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.

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