Programme for International Student Assessment

The Programme for International Student Assessment (PISA) is a worldwide study by the Organisation for Economic Co-operation and Development (OECD) in member and non-member nations intended to evaluate educational systems by measuring 15-year-old school pupils' scholastic performance on mathematics, science, and reading.<ref>{{#invoke:citation/CS1|citation |CitationClass=web }}</ref> It was first performed in 2000 and then repeated every three years. Its aim is to provide comparable data with a view to enabling countries to improve their education policies and outcomes. It measures problem solving and cognition.<ref>Template:Cite book</ref>

The results of the 2022 data collection were released in December 2023.<ref>{{#invoke:citation/CS1|citation |CitationClass=web }}</ref>

Influence and impact

PISA, and similar international standardised assessments of educational attainment are increasingly used in the process of education policymaking at both national and international levels.<ref name="Rey">Template:Cite journal</ref>

PISA was conceived to set in a wider context the information provided by national monitoring of education system performance through regular assessments within a common, internationally agreed framework; by investigating relationships between student learning and other factors they can "offer insights into sources of variation in performances within and between countries".<ref name="McGaw">Template:Cite journal</ref>

Until the 1990s, few European countries used national tests. In the 1990s, ten countries / regions introduced standardised assessment, and since the early 2000s, ten more followed suit. By 2009, only five European education systems had no national student assessments.<ref name="Rey"/>

The impact of these international standardised assessments in the field of educational policy has been significant, in terms of the creation of new knowledge, changes in assessment policy, and external influence over national educational policy more broadly.<ref name=":1">Template:Cite journal</ref><ref name=":2">Template:Cite journal</ref><ref>Template:Cite journal</ref>

Creation of new knowledge

Data from international standardised assessments can be useful in research on causal factors within or across education systems.<ref name="Rey"/> Mons notes that the databases generated by large-scale international assessments have made it possible to carry out inventories and comparisons of education systems on an unprecedented scale<ref group="note">40 countries participated back then, and 81 countries and economies participated in the 2022 data collection.</ref> on themes ranging from the conditions for learning mathematics and reading, to institutional autonomy and admissions policies.<ref name="Mons">Template:Cite journal</ref> They allow typologies to be developed that can be used for comparative statistical analyses of education performance indicators, thereby identifying the consequences of different policy choices. They have generated new knowledge about education: PISA findings have challenged deeply embedded educational practices, such as the early tracking of students into vocational or academic pathways.<ref name="Breaks">Template:Cite journal</ref>

Barroso and de Carvalho find that PISA provides a common reference connecting academic research in education and the political realm of public policy, operating as a mediator between different strands of knowledge from the realm of education and public policy.<ref name="Barroso">Template:Cite journal</ref> However, although the key findings from comparative assessments are widely shared in the research community<ref name="Rey"/> the knowledge they create does not necessarily fit with government reform agendas; this leads to some inappropriate uses of assessment data.

Changes in national assessment policy

Emerging research suggests that international standardised assessments are having an impact on national assessment policy and practice. PISA is being integrated into national policies and practices on assessment, evaluation, curriculum standards and performance targets; its assessment frameworks and instruments are being used as best-practice models for improving national assessments; many countries have explicitly incorporated and emphasise PISA-like competencies in revised national standards and curricula; others use PISA data to complement national data and validate national results against an international benchmark.<ref name="Breaks"/>

External influence over national educational policy

PISA may influence national education policy choices in a variety of ways. Participation in international assessments like PISA has been linked to significant education policy changes and outcomes, such as higher student enrollments and education reforms.<ref name=":1" /> However, critics have argued that participation could lead to undesirable outcomes, such as higher repetition rates and narrowing of curricula.<ref name=":2" /> The impact of PISA may also vary according to the specific country context.<ref>Template:Cite journal</ref>

Policy-makers in most participating countries see PISA as an important indicator of system performance; PISA reports can define policy problems and set the agenda for national policy debate; policymakers seem to accept PISA as a valid and reliable instrument for internationally benchmarking system performance and changes over time; most countries—irrespective of whether they performed above, at, or below the average PISA score—have begun policy reforms in response to PISA reports.<ref name="Breaks"/>

Against this, impact on national education systems varies markedly. For example, in Germany, the results of the first PISA assessment caused the so-called 'PISA shock': a questioning of previously accepted educational policies; in a state marked by jealously guarded regional policy differences, it led ultimately to an agreement by all Länder to introduce common national standards and even an institutionalised structure to ensure that they were observed.<ref name="Ertl">Template:Cite journal</ref> In Hungary, by comparison, which shared similar conditions to Germany, PISA results have not led to significant changes in educational policy.<ref name="Bajomi">{{#invoke:citation/CS1|citation |CitationClass=web }}</ref>

Because many countries have set national performance targets based on their relative rank or absolute PISA score, PISA assessments have increased the influence of their (non-elected) commissioning body, the OECD, as an international education monitor and policy actor, which implies an important degree of 'policy transfer' from the international to the national level; PISA in particular is having "an influential normative effect on the direction of national education policies".<ref name="Breaks"/> Thus, it is argued that the use of international standardised assessments has led to a shift towards international, external accountability for national system performance; Rey contends that PISA surveys, portrayed as objective, third-party diagnoses of education systems, actually serve to promote specific orientations on educational issues.<ref name="Rey"/>

National policy actors refer to high-performing PISA countries to "help legitimise and justify their intended reform agenda within contested national policy debates".<ref name="Break">Steiner-Khamsi (2003), cited by Template:Cite journal</ref> PISA data can be "used to fuel long-standing debates around pre-existing conflicts or rivalries between different policy options, such as in the French Community of Belgium".<ref name="Mang">Template:Cite journal</ref> In such instances, PISA assessment data are used selectively: in public discourse governments often only use superficial features of PISA surveys such as country rankings and not the more detailed analyses. Rey (2010:145, citing Greger, 2008) notes that often the real results of PISA assessments are ignored as policymakers selectively refer to data in order to legitimise policies introduced for other reasons.<ref name="Greger">Template:Cite journal cited in Template:Harvnb</ref>

In addition, PISA's international comparisons can be used to justify reforms with which the data themselves have no connection; in Portugal, for example, PISA data were used to justify new arrangements for teacher assessment (based on inferences that were not justified by the assessments and data themselves); they also fed the government's discourse about the issue of pupils repeating a year, (which, according to research, fails to improve student results).<ref name="Alfons">Template:Cite journal</ref> In Finland, the country's PISA results (that are in other countries deemed to be excellent) were used by Ministers to promote new policies for 'gifted' students.<ref name="Raut">Template:Cite journal</ref> Such uses and interpretations often assume causal relationships that cannot legitimately be based upon PISA data which would normally require fuller investigation through qualitative in-depth studies and longitudinal surveys based on mixed quantitative and qualitative methods,<ref name="Egel">Template:Cite journal</ref> which politicians are often reluctant to fund.

Recent decades have witnessed an expansion in the uses of PISA and similar assessments, from assessing students' learning, to connecting "the educational realm (their traditional remit) with the political realm".<ref>Template:Cite book cited in Template:Harvnb</ref> This raises the question of whether PISA data are sufficiently robust to bear the weight of the major policy decisions that are being based upon them, for, according to Breakspear, PISA data have "come to increasingly shape, define and evaluate the key goals of the national / federal education system".<ref name="Breaks"/> This implies that those who set the PISA tests – e.g. in choosing the content to be assessed and not assessed – are in a position of considerable power to set the terms of the education debate, and to orient educational reform in many countries around the globe.<ref name="Breaks"/>

Framework

PISA stands in a tradition of international school studies, undertaken since the late 1950s by the International Association for the Evaluation of Educational Achievement (IEA). Much of PISA's methodology follows the example of the Trends in International Mathematics and Science Study (TIMSS, started in 1995), which in turn was much influenced by the U.S. National Assessment of Educational Progress (NAEP). The reading component of PISA is inspired by the IEA's Progress in International Reading Literacy Study (PIRLS).

PISA aims to test literacy of students in three competence fields: reading, mathematics, science on an indefinite scale.<ref>{{#invoke:citation/CS1|citation |CitationClass=web }}</ref>

The PISA mathematics literacy test asks students to apply their mathematical knowledge to solve problems set in real-world contexts. To solve the problems students must activate a number of mathematical competencies as well as a broad range of mathematical content knowledge. TIMSS, on the other hand, measures more traditional classroom content such as an understanding of fractions and decimals and the relationship between them (curriculum attainment). PISA claims to measure education's application to real-life problems and lifelong learning (workforce knowledge).

In the reading test, "OECD/PISA does not measure the extent to which 15-year-old students are fluent readers or how competent they are at word recognition tasks or spelling." Instead, they should be able to "construct, extend and reflect on the meaning of what they have read across a wide range of continuous and non-continuous texts."<ref>{{#invoke:citation/CS1|citation |CitationClass=web }}</ref>

PISA also assesses students in innovative domains. In 2012 and 2015 in addition to reading, mathematics and science, they were tested in collaborative problem solving. In 2018 the additional innovative domain was global competence.

Implementation

PISA is sponsored, governed, and coordinated by the OECD, but paid for by participating countries.Template:Citation needed

Method of testing

Sampling

The students tested by PISA are aged between 15 years and 3 months and 16 years and 2 months at the beginning of the assessment period. The school year pupils are in is not taken into consideration. Only students at school are tested, not home-schoolers. In PISA 2006, however, several countries also used a grade-based sample of students. This made it possible to study how age and school year interact.

To fulfill OECD requirements, each country must draw a sample of at least 5,000 students. In small countries like Iceland and Luxembourg, where there are fewer than 5,000 students per year, an entire age cohort is tested. Some countries used much larger samples than required to allow comparisons between regions.

Test

File:Pisatest.jpg

PISA test documents on a school table (Neues Gymnasium, Oldenburg, Germany, 2006)

Each student takes a two-hour computer based test. Part of the test is multiple-choice and part involves fuller answers. There are six and a half hours of assessment material, but each student is not tested on all the parts. Following the cognitive test, participating students spend nearly one more hour answering a questionnaire on their background including learning habits, motivation, and family. School directors fill in a questionnaire describing school demographics, funding, etc. In 2012 the participants were, for the first time in the history of large-scale testing and assessments, offered a new type of problem, i.e. interactive (complex) problems requiring exploration of a novel virtual device.<ref>Template:Cite journal</ref><ref>{{#invoke:citation/CS1|citation |CitationClass=web }} // Translated from Russian. Reference to the original Russian text: Template:Cite journal</ref>

In selected countries, PISA started experimentation with computer adaptive testing.

National add-ons

Countries are allowed to combine PISA with complementary national tests.

Germany does this in a very extensive way: On the day following the international test, students take a national test called Template:Ill (E=Ergänzung=complement). Test items of PISA-E are closer to TIMSS than to PISA. While only about 5,000 German students participate in the international and the national test, another 45,000 take the national test only. This large sample is needed to allow an analysis by federal states. Following a clash about the interpretation of 2006 results, the OECD warned Germany that it might withdraw the right to use the "PISA" label for national tests.<ref>{{#invoke:citation/CS1|citation |CitationClass=web }}</ref>

Data scaling

From the beginning, PISA has been designed with one particular method of data analysis in mind. Since students work on different test booklets, raw scores must be 'scaled' to allow meaningful comparisons. Scores are thus scaled so that the OECD average in each domain (mathematics, reading and science) is 500 and the standard deviation is 100.<ref name=stanat2002pisa>Template:Citation</ref> This is true only for the initial PISA cycle when the scale was first introduced, though, subsequent cycles are linked to the previous cycles through IRT scale linking methods.<ref name = Mazzeo2013etal>Template:Citation</ref>

This generation of proficiency estimates is done using a latent regression extension of the Rasch model, a model of item response theory (IRT), also known as conditioning model or population model. The proficiency estimates are provided in the form of so-called plausible values, which allow unbiased estimates of differences between groups. The latent regression, together with the use of a Gaussian prior probability distribution of student competencies allows estimation of the proficiency distributions of groups of participating students.<ref name="vonDavierM2013etal">Template:Citation</ref> The scaling and conditioning procedures are described in nearly identical terms in the Technical Reports of PISA 2000, 2003, 2006. NAEP and TIMSS use similar scaling methods.

Ranking results

All PISA results are tabulated by country; recent PISA cycles have separate provincial or regional results for some countries. Most public attention concentrates on just one outcome: the mean scores of countries and their rankings of countries against one another. In the official reports, however, country-by-country rankings are given not as simple league tables but as cross tables indicating for each pair of countries whether or not mean score differences are statistically significant (unlikely to be due to random fluctuations in student sampling or in item functioning). In favorable cases, a difference of 9 points is sufficient to be considered significant.Template:Citation needed

PISA never combines mathematics, science and reading domain scores into an overall score. However, commentators have sometimes combined test results from all three domains into an overall country ranking. Such meta-analysis is not endorsed by the OECD, although official summaries sometimes use scores from a testing cycle's principal domain as a proxy for overall student ability.

PISA 2022 ranking summary

The results of PISA 2022 were presented on 5 December 2023, which included data for around 700,000 participating students in 81 countries and economies, with Singapore emerging as the top performer in all categories.<ref name=":3">{{#invoke:citation/CS1|citation |CitationClass=web }}</ref>

Both Lebanon and the Chinese provinces/municipalities of Beijing, Shanghai, Jiangsu and Zhejiang participated this edition, but their results were not published as they were not able to fully collect data because of COVID restrictions.<ref>{{#invoke:citation/CS1|citation |CitationClass=web }}</ref>

Because of the Russian full-scale invasion of Ukraine, only 18 of 27 Ukrainian regions had their data collected, thus the results are not representative of the following regions: Dnipropetrovsk Oblast, Donetsk Oblast, Kharkiv Oblast, Luhansk Oblast, Zaporizhzhia Oblast, Kherson Oblast, Mykolaiv Oblast, the Autonomous Republic of Crimea and the city of Sevastopol.<ref>Template:Cite book</ref>

Mathematics<ref name=":3" />	Science<ref name=":3" />	Reading<ref name=":3" />
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Rankings comparison 2000–2022

Mathematics
Country	2022<ref name="d84fig">{{#invoke:citation/CS1|citation		CitationClass=web }}</ref>	2018<ref name="EDU-2019-4228-EN-T001">Template:Cite document</ref>		2015		2012		2009		2006		2003		2000
	Score	Rank	Score	Rank	Score	Rank	Score	Rank	Score	Rank	Score	Rank	Score	Rank	Score	Rank
International Average (OECD)	472	—	489	—	490	—	494	—	495	—	494	—	499	—	492	—
Template:Flagcountry	368	68	437	48	413	57	394	54	377	53	—	—	—	—	381	33
Template:Flagcountry	—	—	—	—	360	72	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	378	66	379	71	409	58	—	—	—	—	—	—	—	—	388	30
Template:Flagcountry	487	17	491	29	494	25	504	17	514	13	520	12	524	10	533	6
Template:Flagcountry	487	16	499	23	497	20	506	16	496	22	505	17	506	18	503	12
Template:Flagcountry B-S-J-GTemplate:Efn	—	—	—	—	531	6	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry B-S-J-ZTemplate:Efn	—	—	591	1	—	—	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry Baku	397	56	420	56	—	—	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	—	—	472	38	—	—	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	489	12	508	15	507	15	515	13	515	12	520	11	529	7	520	8
Template:Flagcountry	—	—	406	62	—	—	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	379	65	384	70	377	68	389	55	386	51	370	50	356	39	334	35
Template:Flagcountry	442	40	430	51	—	—	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	417	49	436	49	441	47	439	43	428	41	413	43	—	—	430	28
Template:Flagcountry CABATemplate:Efn	—	—	—	—	456	43	418	49	—	—	—	—	—	—	—	—
Template:Flagcountry	336	81	—	—	—	—	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	497	9	512	12	516	10	518	11	527	8	527	7	532	6	533	6
Template:Flagcountry	412	52	417	59	423	50	423	47	421	44	411	44	—	—	384	32
Template:Flagcountry	547	3	531	5	542	4	560	3	543	4	549	1	—	—	—	—
Template:Flagcountry	383	64	391	69	390	64	376	58	381	52	370	49	—	—	—	—
Template:Flagcountry	385	63	402	63	400	62	407	53	—	—	—	—	—	—	—	—
Template:Flagcountry	463	36	464	40	464	41	471	38	460	38	467	34	—	—	—	—
Template:Flagcountry	418	48	451	45	437	48	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	487	18	499	22	492	28	499	22	493	25	510	15	516	12	498	14
Template:Flagcountry	489	13	509	13	511	12	500	20	503	17	513	14	514	14	514	10
Template:Flagcountry	339	79	325	78	328	73	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	343	78	—	—	—	—	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	510	7	523	8	520	9	521	9	512	15	515	13	—	—	—	—
Template:Flagcountry	484	20	507	16	511	13	519	10	541	5	548	2	544	2	536	5
Template:Flagcountry	474	26	495	25	493	26	495	23	497	20	496	22	511	15	517	9
Template:Flagcountry	390	60	398	66	404	60	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	475	25	500	20	506	16	514	14	513	14	504	19	503	19	490	16
Template:Flagcountry	430	44	451	44	454	44	453	40	466	37	459	37	445	32	447	24
Template:Flagcountry	344	77	—	—	—	—	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	540	4	551	4	548	2	561	2	555	2	547	3	550	1	560	1
Template:Flagcountry	473	28	481	36	477	37	477	37	490	27	491	26	490	25	488	17
Template:Flagcountry	459	37	495	26	488	31	493	25	507	16	506	16	515	13	514	10
Template:Flagcountry	366	70	379	72	386	66	375	60	371	55	391	47	360	37	367	34
Template:Flagcountry	492	11	500	21	504	18	501	18	487	30	501	21	503	20	503	12
Template:Flagcountry	458	38	463	41	470	39	466	39	447	39	442	38	—	—	433	26
Template:Flagcountry	471	30	487	31	490	30	485	30	483	33	462	36	466	31	457	22
Template:Flagcountry	377	67	—	—	—	—	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	536	5	527	6	532	5	536	6	529	7	523	9	534	5	557	2
Template:Flagcountry	361	73	400	65	380	67	386	57	387	50	384	48	—	—	—	—
Template:Flagcountry	425	46	423	54	460	42	432	45	405	48	—	—	—	—	—	—
Template:Flagcountry	527	6	526	7	524	7	554	4	546	3	547	4	542	3	547	3
Template:Flagcountry	355	75	366	75	362	71	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	483	21	496	24	482	34	491	26	482	34	486	30	483	27	463	21
Template:Flagcountry	—	—	393	68	396	63	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	475	24	481	35	478	36	479	35	477	35	486	29	—	—	—	—
Template:Flagcountry	—	—	483	33	486	33	490	27	489	28	490	27	493	23	446	25
Template:Flagcountry	552	2	558	3	544	3	538	5	525	10	525	8	527	8	—	—
Template:Flagcountry	409	54	440	47	446	45	421	48	—	—	—	—	—	—	—	—
Template:Flagcountry	466	33	472	39	479	35	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	395	57	409	61	408	59	413	50	419	46	406	45	385	36	387	31
Template:Flagcountry	414	50	421	55	420	52	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	425	47	—	—	—	—	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	406	55	430	53	418	54	410	51	403	49	399	46	—	—	—	—
Template:Flagcountry	365	71	368	74	—	—	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	493	10	519	9	512	11	523	8	526	9	531	5	538	4	—	—
Template:Flagcountry	479	23	494	27	495	21	500	21	519	11	522	10	523	11	537	4
Template:Flagcountry	389	62	394	67	371	69	—	—	—	—	—	—	—	—	381	33
Template:Flagcountry	468	32	501	19	502	19	489	28	498	19	490	28	495	22	499	13
Template:Flagcountry	366	69	—	—	—	—	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	357	74	353	76	—	—	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	338	80	—	—	—	—	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	391	59	400	64	387	65	368	61	365	57	—	—	—	—	292	36
Template:Flagcountry	355	76	353	77	—	—	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	489	15	516	10	504	17	518	12	495	23	495	24	490	24	470	20
Template:Flagcountry	472	29	492	28	492	29	487	29	487	31	466	35	466	30	454	23
Template:Flagcountry	414	51	414	60	402	61	376	59	368	56	318	52	—	—	—	—
Template:Flagcountry	428	45	430	52	444	46	445	42	427	42	415	42	—	—	426	29
Template:Flagcountry	—	—	488	30	494	23	482	32	468	36	476	32	468	29	478	18
Template:Flagcountry	440	42	448	46	—	—	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	389	61	373	73	—	—	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	575	1	569	2	564	1	573	1	562	1	—	—	—	—	—	—
Template:Flagcountry	464	35	486	32	475	38	482	33	497	21	492	25	498	21	—	—
Template:Flagcountry	485	19	509	14	510	14	501	19	501	18	504	18	—	—	—	—
Template:Flagcountry	473	27	481	34	486	32	484	31	483	32	480	31	485	26	476	19
Template:Flagcountry	482	22	502	17	494	24	478	36	494	24	502	20	509	16	510	11
Template:Flagcountry	508	8	515	11	521	8	531	7	534	6	530	6	527	9	529	7
Template:Flagcountry	394	58	419	57	415	56	427	46	419	45	417	41	417	35	432	27
Template:Flagcountry	—	—	—	—	417	55	—	—	414	47	—	—	—	—	—	—
Template:Flagcountry	—	—	—	—	367	70	388	56	371	54	365	51	359	38	—	—
Template:Flagcountry	453	39	454	42	420	51	448	41	445	40	424	40	423	33	—	—
Template:FlagcountryTemplate:Efn	441	41	453	43	—	—	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	431	43	435	50	427	49	434	44	—	—	—	—	—	—	—	—
Template:Flagcountry	489	14	502	18	492	27	494	24	492	26	495	23	508	17	529	7
Template:Flagcountry	465	34	478	37	470	40	481	34	487	29	474	33	483	28	493	15
Template:Flagcountry	409	53	418	58	418	53	409	52	427	43	427	39	422	34	—	—
Template:Flagcountry	364	72	—	—	—	—	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	469	31	—	—	495	22	511	15	—	—	—	—	—	—	—	—

Science
Country	2022<ref name="d84fig" />		2018<ref name="EDU-2019-4228-EN-T001" />		2015		2012		2009		2006
	Score	Rank	Score	Rank	Score	Rank	Score	Rank	Score	Rank	Score	Rank
International Average (OECD)	485	—	489	—	493	—	501	—	501	—	498	—
Template:Flagcountry	376	70	417	59	427	54	397	58	391	54	—	—
Template:Flagcountry	—	—	—	—	376	72	—	—	—	—	—	—
Template:Flagcountry	406	60	404	65	432	52	—	—	—	—	—	—
Template:Flagcountry	507	10	503	17	510	14	521	14	527	9	527	8
Template:Flagcountry	491	23	490	28	495	26	506	21	494	28	511	17
Template:Flagcountry B-S-J-GTemplate:Efn	—	—	—	—	518	10	—	—	—	—	—	—
Template:Flagcountry B-S-J-ZTemplate:Efn	—	—	590	1	—	—	—	—	—	—	—	—
Template:Flagcountry Baku	380	68	398	68	—	—	—	—	—	—	—	—
Template:Flagcountry	—	—	471	37	—	—	—	—	—	—	—	—
Template:Flagcountry	491	24	499	20	502	20	505	22	507	19	510	18
Template:Flagcountry	—	—	398	67	—	—	—	—	—	—	—	—
Template:Flagcountry	403	62	404	66	401	66	402	55	405	49	390	49
Template:Flagcountry	446	42	431	50	—	—	—	—	—	—	—	—
Template:Flagcountry	421	50	424	56	446	46	446	43	439	42	434	40
Template:Flagcountry CABATemplate:Efn	—	—	—	—	475	38	425	49	—	—	—	—
Template:Flagcountry	347	81	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	515	8	518	8	528	7	525	9	529	7	534	3
Template:Flagcountry	444	43	444	45	447	45	445	44	447	41	438	39
Template:Flagcountry	537	4	516	10	532	4	523	11	520	11	532	4
Template:Flagcountry	411	54	413	62	416	60	399	56	402	50	388	50
Template:Flagcountry	411	55	416	60	420	58	429	47	—	—	—	—
Template:Flagcountry	483	31	472	36	475	37	491	32	486	35	493	25
Template:Flagcountry	411	56	439	47	433	51	—	—	—	—	—	—
Template:Flagcountry	498	18	497	21	493	29	508	20	500	22	513	14
Template:Flagcountry	494	20	493	25	502	21	498	25	499	24	496	23
Template:Flagcountry	360	77	336	78	332	73	—	—	—	—	—	—
Template:Flagcountry	373	72	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	526	6	530	4	534	3	541	5	528	8	531	5
Template:Flagcountry	511	9	522	6	531	5	545	4	554	1	563	1
Template:Flagcountry	487	26	493	24	495	27	499	24	498	25	495	24
Template:Flagcountry	384	66	383	73	411	63	—	—	—	—	—	—
Template:Flagcountry	492	22	503	16	509	16	524	10	520	12	516	12
Template:Flagcountry	441	44	452	44	455	44	467	40	470	38	473	37
Template:Flagcountry	373	73	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	520	7	517	9	523	9	555	1	549	2	542	2
Template:Flagcountry	486	27	481	32	477	35	494	30	503	20	504	20
Template:Flagcountry	447	41	475	35	473	39	478	37	496	26	491	26
Template:Flagcountry	383	67	396	70	403	65	382	60	383	55	393	48
Template:Flagcountry	504	12	496	22	503	19	522	13	508	18	508	19
Template:Flagcountry	465	37	462	42	467	40	470	39	455	39	454	38
Template:Flagcountry	477	33	468	40	481	34	494	31	489	33	475	35
Template:Flagcountry	403	63	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	547	2	529	5	538	2	547	3	539	4	531	6
Template:Flagcountry	375	71	429	51	409	64	409	54	415	47	422	43
Template:Flagcountry	423	49	397	69	456	43	425	48	400	53	—	—
Template:Flagcountry	528	5	519	7	516	11	538	6	538	5	522	10
Template:Flagcountry	357	78	365	75	378	71	—	—	—	—	—	—
Template:Flagcountry	494	19	487	29	490	31	502	23	494	29	490	27
Template:Flagcountry	—	—	384	72	386	68	—	—	—	—	—	—
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Template:Flagcountry	—	—	477	34	483	33	491	33	484	36	486	33
Template:Flagcountry	543	3	544	3	529	6	521	15	511	16	511	16
Template:Flagcountry	416	52	438	48	443	47	420	50	—	—	—	—
Template:Flagcountry	466	36	457	43	465	41	—	—	—	—	—	—
Template:Flagcountry	410	57	419	57	416	61	415	52	416	46	410	47
Template:Flagcountry	417	51	428	52	428	53	—	—	—	—	—	—
Template:Flagcountry	412	53	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	403	61	415	61	411	62	410	53	401	51	412	46
Template:Flagcountry	365	76	377	74	—	—	—	—	—	—	—	—
Template:Flagcountry	488	25	503	15	509	17	522	12	522	10	525	9
Template:Flagcountry	504	11	508	12	513	12	516	16	532	6	530	7
Template:Flagcountry	380	69	413	63	384	70	—	—	—	—	—	—
Template:Flagcountry	478	32	490	27	498	24	495	29	500	23	487	32
Template:Flagcountry	369	74	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	388	65	365	76	—	—	—	—	—	—	—	—
Template:Flagcountry	368	75	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	408	59	404	64	397	67	373	61	369	57	—	—
Template:Flagcountry	356	79	357	77	—	—	—	—	—	—	—	—
Template:Flagcountry	499	17	511	11	501	22	526	8	508	17	498	22
Template:Flagcountry	484	30	492	26	501	23	489	34	493	30	474	36
Template:Flagcountry	432	46	419	58	418	59	384	59	379	56	349	52
Template:Flagcountry	428	48	426	55	435	50	439	46	428	43	418	45
Template:Flagcountry	—	—	478	33	487	32	486	35	478	37	479	34
Template:Flagcountry	447	40	440	46	—	—	—	—	—	—	—	—
Template:Flagcountry	390	64	386	71	—	—	—	—	—	—	—	—
Template:Flagcountry	561	1	551	2	556	1	551	2	542	3	—	—
Template:Flagcountry	462	38	464	41	461	42	471	38	490	32	488	29
Template:Flagcountry	500	14	507	13	513	13	514	18	512	15	519	11
Template:Flagcountry	485	28	483	30	493	30	496	27	488	34	488	30
Template:Flagcountry	494	21	499	19	493	28	485	36	495	27	503	21
Template:Flagcountry	503	13	495	23	506	18	515	17	517	13	512	15
Template:Flagcountry	409	58	426	53	421	57	444	45	425	45	421	44
Template:Flagcountry	—	—	—	—	425	56	—	—	410	48	—	—
Template:Flagcountry	—	—	—	—	386	69	398	57	401	52	386	51
Template:Flagcountry	476	34	468	39	425	55	463	41	454	40	424	42
Template:FlagcountryTemplate:Efn	450	39	469	38	—	—	—	—	—	—	—	—
Template:Flagcountry	432	47	434	49	437	48	448	42	—	—	—	—
Template:Flagcountry	500	15	505	14	509	15	514	19	514	14	515	13
Template:Flagcountry	499	16	502	18	496	25	497	26	502	21	489	28
Template:Flagcountry	435	45	426	54	435	49	416	51	427	44	428	41
Template:Flagcountry	355	80	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	472	35	—	—	525	8	528	7	—	—	—	—

Reading
Country	2022<ref name="d84fig" />		2018<ref name="EDU-2019-4228-EN-T001" />		2015		2012		2009		2006		2003		2000
	Score	Rank	Score	Rank	Score	Rank	Score	Rank	Score	Rank	Score	Rank	Score	Rank	Score	Rank
International Average (OECD)	476	—	487	—	493	—	496	—	493	—	489	—	494	—	493	—
Template:Flagcountry	358	73	405	61	405	63	394	58	385	55	—	—	—	—	349	39
Template:Flagcountry	—	—	—	—	350	71	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	401	58	402	63	425	56	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	498	12	503	16	503	16	512	12	515	8	513	7	525	4	528	4
Template:Flagcountry	480	21	484	27	485	33	490	26	470	37	490	21	491	22	492	19
Template:Flagcountry B-S-J-GTemplate:Efn	—	—	—	—	494	27	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry B-S-J-ZTemplate:Efn	—	—	555	1	—	—	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry Baku	365	69	389	68	—	—	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	—	—	474	36	—	—	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	479	23	493	22	499	20	509	16	506	10	501	11	507	11	507	11
Template:Flagcountry	—	—	403	62	—	—	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	410	52	413	57	407	62	407	52	412	49	393	47	403	36	396	36
Template:Flagcountry	429	44	408	59	—	—	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	404	57	420	54	432	49	436	47	429	42	402	43	—	—	430	32
Template:Flagcountry CABATemplate:Efn	—	—	—	—	475	38	429	48	—	—	—	—	—	—	—	—
Template:Flagcountry	329	81	—	—	—	—	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	507	8	520	6	527	3	523	7	524	5	527	4	528	3	534	2
Template:Flagcountry	448	37	452	43	459	42	441	43	449	41	442	37	—	—	410	35
Template:Flagcountry	515	5	503	17	497	23	523	8	495	21	496	15	—	—	—	—
Template:Flagcountry	409	54	412	58	425	57	403	54	413	48	385	49	—	—	—	—
Template:Flagcountry	415	50	426	49	427	52	441	45	—	—	—	—	—	—	—	—
Template:Flagcountry	475	26	479	29	487	31	485	33	476	34	477	29	—	—	—	—
Template:Flagcountry	381	63	424	50	443	45	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	489	17	490	25	487	30	493	24	478	32	483	25	489	24	492	20
Template:Flagcountry	489	15	501	18	500	18	496	23	495	22	494	18	492	19	497	16
Template:Flagcountry	351	74	342	76	358	69	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	365	70	—	—	—	—	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	511	6	523	5	519	6	516	10	501	12	501	12	—	—	—	—
Template:Flagcountry	490	14	520	7	526	4	524	5	536	2	547	2	543	1	546	1
Template:Flagcountry	474	29	493	23	499	19	505	19	496	20	488	22	496	17	505	14
Template:Flagcountry	374	67	380	70	401	65	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	480	22	498	20	509	11	508	18	497	18	495	17	491	21	484	22
Template:Flagcountry	438	41	457	42	467	41	477	38	483	30	460	35	472	30	474	25
Template:Flagcountry	374	66	—	—	—	—	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	500	11	524	4	527	2	545	1	533	3	536	3	510	9	525	6
Template:Flagcountry	473	31	476	33	470	40	488	28	494	24	482	26	482	25	480	23
Template:Flagcountry	436	42	474	35	482	35	483	35	500	15	484	23	492	20	507	12
Template:Flagcountry	359	71	371	72	397	67	396	57	402	53	393	46	382	38	371	38
Template:Flagcountry	516	2	518	8	521	5	523	6	496	19	517	6	515	6	527	5
Template:Flagcountry	474	30	470	37	479	37	486	32	474	35	439	39	—	—	452	29
Template:Flagcountry	482	20	476	32	485	34	490	25	486	27	469	32	476	29	487	21
Template:Flagcountry	410	53	—	—	—	—	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	516	3	504	15	516	8	538	3	520	7	498	14	498	14	522	9
Template:Flagcountry	342	78	419	55	408	61	399	55	405	51	401	44	—	—	—	—
Template:Flagcountry	386	61	387	69	427	54	393	59	390	54	—	—	—	—	—	—
Template:Flagcountry	515	4	514	9	517	7	536	4	539	1	556	1	534	2	525	7
Template:Flagcountry	342	77	353	75	347	72	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	475	27	479	30	488	29	489	27	484	28	479	27	491	23	458	28
Template:Flagcountry	—	—	353	74	347	73	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	472	32	476	34	472	39	477	37	468	38	470	31	—	—	—	—
Template:Flagcountry	—	—	470	38	481	36	488	30	472	36	479	28	479	27	441	30
Template:Flagcountry	510	7	525	3	509	12	509	15	487	26	492	20	498	15	—	—
Template:Flagcountry	388	60	415	56	431	50	398	56	—	—	—	—	—	—	—	—
Template:Flagcountry	445	39	448	44	447	44	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	415	49	420	53	423	58	424	49	425	44	410	42	400	37	422	34
Template:Flagcountry	411	51	424	51	416	59	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	378	65	—	—	—	—	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	405	56	421	52	427	55	422	50	408	50	392	48	—	—	—	—
Template:Flagcountry	339	79	359	73	—	—	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	459	35	485	26	503	15	511	13	508	9	507	10	513	8	—	—
Template:Flagcountry	501	10	506	12	509	10	512	11	521	6	521	5	522	5	529	3
Template:Flagcountry	359	72	393	67	352	70	—	—	—	—	—	—	—	—	373	37
Template:Flagcountry	477	25	499	19	513	9	504	20	503	11	484	24	500	12	505	13
Template:Flagcountry	349	75	—	—	—	—	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	392	59	377	71	—	—	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	373	68	—	—	—	—	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	408	55	401	64	398	66	384	61	370	57	—	—	—	—	327	40
Template:Flagcountry	347	76	340	77	—	—	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	489	16	512	10	506	13	518	9	500	14	508	8	497	16	479	24
Template:Flagcountry	477	24	492	24	498	21	488	31	489	25	472	30	478	28	470	26
Template:Flagcountry	419	47	407	60	402	64	388	60	372	56	312	51	—	—	—	—
Template:Flagcountry	428	45	428	47	434	47	438	46	424	45	396	45	—	—	428	33
Template:Flagcountry	—	—	479	31	495	26	475	40	459	40	440	38	442	32	462	27
Template:Flagcountry	440	40	439	45	—	—	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	383	62	399	65	—	—	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	543	1	549	2	535	1	542	2	526	4	—	—	—	—	—	—
Template:Flagcountry	447	38	458	41	453	43	463	41	477	33	466	33	469	31	—	—
Template:Flagcountry	469	33	495	21	505	14	481	36	483	29	494	19	—	—	—	—
Template:Flagcountry	474	28	—	—	496	25	488	29	481	31	461	34	481	26	493	18
Template:Flagcountry	487	18	506	11	500	17	483	34	497	17	507	9	514	7	516	10
Template:Flagcountry	483	19	484	28	492	28	509	14	501	13	499	13	499	13	494	17
Template:Flagcountry	379	64	393	66	409	60	441	44	421	46	417	40	420	35	431	31
Template:Flagcountry	—	—	—	—	427	53	—	—	416	47	—	—	—	—	—	—
Template:Flagcountry	—	—	—	—	361	68	404	53	404	52	380	50	375	39	—	—
Template:Flagcountry	456	36	466	40	428	51	475	39	464	39	447	36	441	33	—	—
Template:FlagcountryTemplate:Efn	428	46	466	39	—	—	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	417	48	432	46	434	48	442	42	—	—	—	—	—	—	—	—
Template:Flagcountry	494	13	504	14	498	22	499	21	494	23	495	16	507	10	523	8
Template:Flagcountry	504	9	505	13	497	24	498	22	500	16	—	—	495	18	504	15
Template:Flagcountry	430	43	427	48	437	46	411	51	426	43	413	41	434	34	—	—
Template:Flagcountry	336	80	—	—	—	—	—	—	—	—	—	—	—	—	—	—
Template:Flagcountry	462	34	—	—	487	32	508	17	—	—	—	—	—	—	—	—

Previous years

{{#invoke:Labelled list hatnote|labelledList|Main article|Main articles|Main page|Main pages}}

Period	Focus	OECD countries	Partner countries	Participating students	Notes
2000	Reading	28	4 + 11	265,000	The Netherlands disqualified from data analysis. 11 additional non-OECD countries took the test in 2002.
2003	Mathematics	30	11	275,000	UK disqualified from data analysis, due to its low response rate.<ref>Template:Cite journal</ref> Also included test in problem solving.
2006	Science	30	27	400,000	Reading scores for US disqualified from analysis due to misprint in testing materials.<ref>Template:Citation</ref>
2009<ref name=PISA2009>Template:Citation</ref>	Reading	34	41 + 10	470,000	10 additional non-OECD countries took the test in 2010.<ref name=PISA2009Plus>Template:Citation</ref><ref>Template:Citation</ref>
2012<ref name=PISA2012>Template:Citation</ref>	Mathematics	35	37	510,000
2015<ref name=PISA2015>Template:Citation</ref>	Science	34	31	509,000
2018<ref name=PISA2018>Template:Citation</ref>	Reading	37	42	600,000
2022	Mathematics	37	44	690,000

Reception

China

China's participation in the 2012 test was limited to Shanghai, Hong Kong, and Macau as separate entities. In 2012, Shanghai participated for the second time, again topping the rankings in all three subjects, as well as improving scores in the subjects compared to the 2009 tests. Shanghai's score of 613 in mathematics was 113 points above the average score, putting the performance of Shanghai pupils about 3 school years ahead of pupils in average countries. Educational experts debated to what degree this result reflected the quality of the general educational system in China, pointing out that Shanghai has greater wealth and better-paid teachers than the rest of China.<ref>Template:Cite news</ref> Hong Kong placed second in reading and science and third in maths.

Andreas Schleicher, PISA division head and co-ordinator, stated that PISA tests administered in rural China have produced some results approaching the OECD average. Citing further as-yet-unpublished OECD research, he said, "We have actually done Pisa in 12 of the provinces in China. Even in some of the very poor areas you get performance close to the OECD average."<ref name="ft-1">Template:Citation</ref> Schleicher believes that China has also expanded school access and has moved away from learning by rote,<ref>Template:Citation</ref> performing well in both rote-based and broader assessments.<ref name="ft-1" />

In 2018 the Chinese provinces that participated were Beijing, Shanghai, Jiangsu and Zhejiang. In 2015, the participating provinces were Jiangsu, Guangdong, Beijing, and Shanghai.<ref>Template:Cite news</ref> The 2015 Beijing-Shanghai-Jiangsu-Guangdong cohort scored a median 518 in science in 2015, while the 2012 Shanghai cohort scored a median 580.

Critics of PISA counter that in Shanghai and other Chinese cities, most children of migrant workers can only attend city schools up to the ninth grade, and must return to their parents' hometowns for high school due to hukou restrictions, thus skewing the composition of the city's high school students in favor of wealthier local families. A population chart of Shanghai reproduced in The New York Times shows a steep drop off in the number of 15-year-olds residing there.<ref>Template:Cite news For Schleicher's initial response to these criticisms see his post, {{#invoke:citation/CS1|citation |CitationClass=web }}</ref> According to Schleicher, 27% of Shanghai's 15-year-olds are excluded from its school system (and hence from testing). As a result, the percentage of Shanghai's 15-year-olds tested by PISA was 73%, lower than the 89% tested in the US.<ref>{{#invoke:citation/CS1|citation |CitationClass=web }}</ref> Following the 2015 testing, OECD published in depth studies on the education systems of a selected few countries including China.<ref>{{#invoke:citation/CS1|citation |CitationClass=web }}</ref>

In 2014, Liz Truss, the British Parliamentary Under-Secretary of State at the Department for Education, led a fact-finding visit to schools and teacher-training centres in Shanghai.<ref>Template:Cite news</ref> Britain increased exchanges with Chinese teachers and schools to find out how to improve quality. In 2014, 60 teachers from Shanghai were invited to the UK to help share their teaching methods, support pupils who are struggling, and help to train other teachers.<ref>Template:Cite news</ref> In 2016, Britain invited 120 Chinese teachers, planning to adopt Chinese styles of teaching in 8,000 aided schools.<ref>Template:Cite news</ref> By 2019, approximately 5,000 of Britain's 16,000 primary schools had adopted the Shanghai's teaching methods.<ref>{{#invoke:citation/CS1|citation |CitationClass=web }}</ref> The performance of British schools in PISA improved after adopting China's teaching styles.<ref>Template:Cite news</ref><ref>{{#invoke:citation/CS1|citation |CitationClass=web }}</ref>

Finland

Finland, which received several top positions in the first tests, fell in all three subjects in 2012, but remained the best performing country overall in Europe, achieving their best result in science with 545 points (5th) and worst in mathematics with 519 (12th) in which the country was outperformed by four other European countries. The drop in mathematics was 25 points since 2003, the last time mathematics was the focus of the tests. For the first time Finnish girls outperformed boys in mathematics narrowly. It was also the first time pupils in Finnish-speaking schools did not perform better than pupils in Swedish-speaking schools. Former minister of Education and Science Krista Kiuru expressed concern for the overall drop, as well as the fact that the number of low-performers had increased from 7% to 12%.<ref>{{#invoke:citation/CS1|citation |CitationClass=web }}</ref>

India

India participated in the 2009 round of testing but pulled out of the 2012 PISA testing, with the Indian government attributing its action to the unfairness of PISA testing to Indian students.<ref>{{#invoke:citation/CS1|citation |CitationClass=web }}</ref> India had ranked 72nd out of 73 countries tested in 2009.<ref name=dri1>{{#invoke:citation/CS1|citation |CitationClass=web }}</ref> The Indian Express reported, "The ministry (of education) has concluded that there was a socio-cultural disconnect between the questions and Indian students. The ministry will write to the OECD and drive home the need to factor in India's "socio-cultural milieu". India's participation in the next PISA cycle will hinge on this".<ref>{{#invoke:citation/CS1|citation |CitationClass=web }}</ref> The Indian Express also noted that "Considering that over 70 nations participate in PISA, it is uncertain whether an exception would be made for India".

India did not participate in the 2012, 2015 and 2018 PISA rounds.<ref>{{#invoke:citation/CS1|citation |CitationClass=web }}</ref>

A Kendriya Vidyalaya Sangathan (KVS) committee as well as a group of secretaries on education constituted by the Prime Minister of India Narendra Modi recommended that India should participate in PISA. Accordingly, in February 2017, the Ministry of Human Resource Development under Prakash Javadekar decided to end the boycott and participate in PISA from 2020. To address the socio-cultural disconnect between the test questions and students, it was reported that the OECD will update some questions. For example, the word avocado in a question may be replaced with a more popular Indian fruit such as mango.<ref>Template:Cite news</ref>

India did not participate in the 2022 PISA rounds citing due to COVID-19 pandemic disruption.<ref>{{#invoke:citation/CS1|citation |CitationClass=web }}</ref>

Malaysia

In 2015, the results from Malaysia were found by the OECD to have not met the maximum response rate.<ref>{{#invoke:citation/CS1|citation |CitationClass=web }}</ref> Opposition politician Ong Kian Ming said the education ministry tried to oversample high-performing students in rich schools.<ref>{{#invoke:citation/CS1|citation |CitationClass=web }}</ref><ref>{{#invoke:citation/CS1|citation |CitationClass=web }}</ref>

Sweden

Sweden's result dropped in all three subjects in the 2012 test, which was a continuation of a trend from 2006 and 2009. It saw the sharpest fall in mathematics performance with a drop in score from 509 in 2003 to 478 in 2012. The score in reading showed a drop from 516 in 2000 to 483 in 2012. The country performed below the OECD average in all three subjects.<ref>{{#invoke:citation/CS1|citation |CitationClass=web }}</ref> The leader of the opposition, Social Democrat Stefan Löfven, described the situation as a national crisis.<ref name=sl>{{#invoke:citation/CS1|citation |CitationClass=web }}</ref> Along with the party's spokesperson on education, Ibrahim Baylan, he pointed to the downward trend in reading as most severe.<ref name=sl/>

In 2020, Swedish newspaper Expressen revealed that Sweden had inflated their score in PISA 2018 by not conforming to OECD standards. According to professor Magnus Henrekson a large number of foreign-born students had not been tested.<ref>{{#invoke:citation/CS1|citation |CitationClass=web }}</ref>

United Kingdom

In the 2012 test, as in 2009, the result was slightly above average for the United Kingdom, with the science ranking being highest (20).<ref name=adams>Template:Citation</ref> England, Wales, Scotland and Northern Ireland also participated as separated entities, showing the worst result for Wales which in mathematics was 43rd of the 65 countries and economies. Minister of Education in Wales Huw Lewis expressed disappointment in the results, said that there were no "quick fixes", but hoped that several educational reforms that have been implemented in the last few years would give better results in the next round of tests.<ref>{{#invoke:citation/CS1|citation |CitationClass=web }}</ref> The United Kingdom had a greater gap between high- and low-scoring students than the average. There was little difference between public and private schools when adjusted for socio-economic background of students. The gender difference in favour of girls was less than in most other countries, as was the difference between natives and immigrants.<ref name=adams/>

Writing in the Daily Telegraph, Ambrose Evans-Pritchard warned against putting too much emphasis on the UK's international ranking, arguing that an overfocus on scholarly performances in East Asia might have contributed to the area's low birthrate, which he argued could harm the economic performance in the future more than a good PISA score would outweigh.<ref>{{#invoke:citation/CS1|citation |CitationClass=web }}</ref>

In 2013, the Times Educational Supplement (TES) published an article, "Is PISA Fundamentally Flawed?" by William Stewart, detailing serious critiques of PISA's conceptual foundations and methods advanced by statisticians at major universities.<ref name="Stewart 2013">{{#invoke:citation/CS1|citation |CitationClass=web }}</ref>

In the article, Professor Harvey Goldstein of the University of Bristol was quoted as saying that when the OECD tries to rule out questions suspected of bias, it can have the effect of "smoothing out" key differences between countries. "That is leaving out many of the important things," he warned. "They simply don't get commented on. What you are looking at is something that happens to be common. But (is it) worth looking at? PISA results are taken at face value as providing some sort of common standard across countries. But as soon as you begin to unpick it, I think that all falls apart."

Queen's University Belfast mathematician Dr. Hugh Morrison stated that he found the statistical model underlying PISA to contain a fundamental, insoluble mathematical error that renders Pisa rankings "valueless".<ref>{{#invoke:citation/CS1|citation |CitationClass=web }}</ref> Goldstein remarked that Dr. Morrison's objection highlights "an important technical issue" if not a "profound conceptual error". However, Goldstein cautioned that PISA has been "used inappropriately", contending that some of the blame for this "lies with PISA itself. I think it tends to say too much for what it can do and it tends not to publicise the negative or the weaker aspects." Professors Morrison and Goldstein expressed dismay at the OECD's response to criticism. Morrison said that when he first published his criticisms of PISA in 2004 and also personally queried several of the OECD's "senior people" about them, his points were met with "absolute silence" and have yet to be addressed. "I was amazed at how unforthcoming they were," he told TES. "That makes me suspicious." "Pisa steadfastly ignored many of these issues," he says. "I am still concerned."<ref name="Stewart 2013" />

Professor Svend Kreiner, of the University of Copenhagen, agreed: "One of the problems that everybody has with PISA is that they don't want to discuss things with people criticising or asking questions concerning the results. They didn't want to talk to me at all. I am sure it is because they can't defend themselves.<ref name="Stewart 2013"/>

United States

Since 2012 a few states have participated in the PISA tests as separate entities. Only the 2012 and 2015 results are available on a state basis. Puerto Rico participated in 2015 as a separate US entity as well.

2012 US State results

Mathematics	Science	Reading
Template:Flagcountry 514 Template:Flagcountry 506 Template:Flagicon US Average 481 Template:Flagcountry 467	Template:Flagcountry 527 Template:Flagcountry 521 Template:Flagicon US Average 497 Template:Flagcountry 485	Template:Flagcountry 527 Template:Flagcountry 521 Template:Flagicon US Average 498 Template:Flagcountry 492

2015 US State results

Mathematics	Science	Reading
Template:Flagcountry 500 Template:Flagcountry 471 Template:Flagicon US Average 470 Template:Flagcountry 378	Template:Flagcountry 529 Template:Flagcountry 502 Template:Flagicon US Average 496 Template:Flagcountry 403	Template:Flagcountry 527 Template:Flagcountry 500 Template:Flagicon US Average 497 Template:Flagcountry 410

PISA results for the United States by race and ethnicity

Mathematics
Race	2018<ref name=":0"/>	2015	2012	2009	2006	2003
	Score	Score	Score	Score	Score	Score
Asian	539	498	549	524	494	506
White	503	499	506	515	502	512
US Average	478	470	481	487	474	483
More than one race	474	475	492	487	482	502
Hispanic	452	446	455	453	436	443
Other	—	423	436	460	446	446
Black	419	419	421	423	404	417

Science
Race	2018<ref name=":0"/>	2015	2012	2009	2006
	Score	Score	Score	Score	Score
Asian	551	525	546	536	499
White	529	531	528	532	523
US Average	502	496	497	502	489
More than one race	502	503	511	503	501
Hispanic	478	470	462	464	439
Other	—	462	439	465	453
Black	440	433	439	435	409

Reading
Race	citation	CitationClass=web }}</ref>	2015	2012	2009	2006	2003	2000
	Score	Score	Score	Score	Score	Score	Score
Asian	556	527	550	541	—	513	546
White	531	526	519	525	—	525	538
US Average	505	497	498	500	—	495	504
More than one race	501	498	517	502	—	515	—
Hispanic	481	478	478	466	—	453	449
Black	448	443	443	441	—	430	445
Other	—	440	438	462	—	456	455

Research on possible causes of PISA disparities in different countries

Although PISA and TIMSS officials and researchers themselves generally refrain from hypothesizing about the large and stable differences in student achievement between countries, since 2000, literature on the differences in PISA and TIMSS results and their possible causes has emerged.<ref name="Hanushek, Eric A. 2011">Template:Cite book</ref> Data from PISA have furnished several researchers, notably Eric Hanushek, Ludger Wößmann, Heiner Rindermann, and Stephen J. Ceci, with material for books and articles about the relationship between student achievement and economic development,<ref>Template:Citation</ref> democratization, and health;<ref>Template:Citation</ref> as well as the roles of such single educational factors as high-stakes exams,<ref>Template:Cite journal</ref> the presence or absence of private schools and the effects and timing of ability tracking.<ref>Template:Citation</ref>

It is important to be careful with the interpretation of this data due to PISA has also attributed low educational achievements to school poverty or the presence of a high number of immigrants students, but this is hoax and a superficial analysis of the data, as has been demonstrated in some studies.  Now, is known that which actually determines educational achievement are those actions which are implemented in schools<ref>Garcia Yeste, C., Morlà-Folch, T., Lopez de Aguileta, G., Natividad Sancho, L., Lopez de Aguileta, A., & Munté-Pascual, A. (2025). Achieving the same educational opportunities for all: overcoming hoax interpretations of the PISA results. International Journal of Adolescence and Youth, 30(1). https://doi.org/10.1080/02673843.2025.2459330 </ref>

Comments on accuracy

David Spiegelhalter of Cambridge wrote: "Pisa does present the uncertainty in the scores and ranks - for example the United Kingdom rank in the 65 countries is said to be between 23 and 31. It's unwise for countries to base education policy on their Pisa results, as Germany, Norway and Denmark did after doing badly in 2001."<ref>Template:Cite news</ref>

According to Forbes, an American media outlet, in an opinion article, some countries such as China, Hong Kong, Macau, and Argentina select PISA samples from only the best-educated areas or from their top-performing students, slanting the results.<ref>{{#invoke:citation/CS1|citation |CitationClass=web }}</ref>

According to an open letter to Andreas Schleicher, director of PISA, various academics and educators argued that "OECD and Pisa tests are damaging education worldwide".<ref>Template:Cite news</ref>

According to O Estado de São Paulo, Brazil shows a great disparity when classifying the results between public and private schools, where public schools would rank worse than Peru, while private schools would rank better than Finland.<ref>{{#invoke:citation/CS1|citation |CitationClass=web }}</ref>

See also

• Gender gaps in mathematics and reading in PISA 2009
• Progress in International Reading Literacy Study (PIRLS)
• Programme for the International Assessment of Adult Competencies (PIAAC)
• Teaching And Learning International Survey (TALIS)
• Trends in International Mathematics and Science Study (TIMSS)

External links

• OECD/PISA website
  • Template:Cite book
  • {{#invoke:citation/CS1|citation

|CitationClass=web }}

• OECD's Education GPS: Interactive data from PISA 2015
• PISA Data Explorer
• Gunda Tire: "Estonians believe in education, and this belief has been essential for centuries"—Interview of Gunda Tire, OECD PISA National Project Manager, for Caucasian Journal
• QEdu – PISA scores and percentage of adequate learning by country

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