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Record W4400705441 · doi:10.1787/79f720fe-en

Snapshot of school environment conductive to creative thinking

2024· other· en· W4400705441 on OpenAlexaboutno aff

Bibliographic record

VenueProgramme for international student assessment/Internationale Schulleistungsstudie · 2024
Typeother
Languageen
FieldPsychology
TopicCreativity in Education and Neuroscience
Canadian institutionsnot available
Fundersnot available
KeywordsChinaEquity (law)GeographySocioeconomic statusCoronavirus disease 2019 (COVID-19)Political scienceEconomic growthDemographySociologyEconomicsPopulationMedicine

Abstract

fetched live from OpenAlex

In 2022, as countries were still dealing with the lingering impacts of the COVID-19 pandemic, nearly 700 000 students from 81 OECD Member and partner economies, representing 29 million across the world, took the Programme for International Student Assessment (PISA) test.It makes 2022 PISA the first large-scale study to collect data on student performance, well-being, and equity before and after the COVID-19 disruptions.The report finds that in spite of the challenging circumstances, 31 countries and economies managed to at least maintain their performance in mathematics since PISA 2018.Among these, Australia*, Japan, Korea, Singapore, and Switzerland maintained or further raised already high levels of student performance, with scores ranging from 487 to 575 points (OECD average 472).These systems showed common features including shorter school closures, fewer obstacles to remote learning, and continuing teachers' and parental support, which can further offer insights and indications of broader best practices to address future crises.Many countries also made significant progress towards universal secondary education, key to enabling equality of opportunity and full participation in the economy.Among them, Cambodia, Colombia, Costa Rica, Indonesia, Morocco, Paraguay and Romania have rapidly expanded education to previously marginalised populations over the past decade.Ten countries and economies saw a large share of all 15-year-olds gain basic proficiency in maths, reading and science and achieve high levels of socio-economic fairness: Canada*, Denmark*, Finland, Hong Kong (China)*, Ireland*, Japan, Korea, Latvia*, Macao (China) and the United Kingdom*.While socioeconomic status remains a significant predictor of performance in these and other OECD countries and economies, education in these countries can be considered highly equitable.At the same time, on average, the PISA 2022 assessment saw an unprecedented drop in performance across the OECD.Compared to 2018, mean performance fell by ten score points in reading and by almost 15 score points in mathematics, which is equivalent to three-quarters of a year's worth of learning.The decline in mathematics performance is three times greater than any previous consecutive change.In fact, one in four 15-year-olds is now considered a low performer in mathematics, reading, and science on average across OECD countries.This means they can struggle to do tasks such as use basic algorithms or interpret simple texts.This trend is more pronounced in 18 countries and economies, where more than 60% of 15-year-olds are falling behind.Yet the decline can only partially be attributed to the COVID-19 pandemic.Scores in reading and science had already been falling prior to the pandemic.For example, negative trends in maths performance were already apparent prior to 2018 in Belgium, Canada*, Czechia, Finland, France, Hungary, Iceland, the Netherlands*, New Zealand*, and the Slovak Republic.The relationship between pandemic-induced school closures, often cited as the main cause of performance decline is not so direct.Across the OECD, around half of the students experienced closures for more than three months.However, PISA results show no clear difference in performance trends between education systems with limited school closures such as Iceland, Sweden and Chinese Taipei and systems that experienced longer school closures, such as Brazil, Ireland* and Jamaica*.PISA 2022 RESULTS (VOLUME III) © OECD 2024School closures also drove a global conversion to digitally enabled remote learning, adding to long-term challenges that had already emerged, such as the use of technology in classrooms.How education systems grapple with technological change and whether policymakers find the right balance between risks and opportunities, will be a defining feature of effective education systems.According to our results, on average across OECD countries, around three-quarters of students reported being confident using various technologies, including learning-management systems, school learning platforms and video communication programs.Students who spent up to one hour per day on digital devices for learning activities in school scored 14 points higher in mathematics than students who spent no time, and this positive relationship is observed in over half (46 countries and economies) of all systems with available data.Yet technology used for leisure rather than instruction, such as mobile phones, often seems to be associated with poorer results.Students who reported that they become distracted by other students who are using digital devices in at least some mathematics lessons scored 15 points lower than students who reported that this never or almost never happens, after accounting for students' and schools' socio-economic profile.PISA data show that teacher support is particularly important in times of disruption, including by providing extra pedagogical and motivational support to students.The availability of teachers to help students in need had the strongest relationship to mathematics performance across the OECD, compared to other experiences linked to COVID-19 school closure.Mathematics scores were 15 points higher on average where students agreed they had good access to teacher help.These students were also more confident than their peers to learn autonomously and remotely.Despite this, only one in five students overall reported that they received extra help from teachers in some lessons in 2022.Around eight percent never or almost never received additional support.Overall, education systems with positive trends in parental engagement in student learning between 2018 and 2022 showed greater stability or improvement in mathematics performance.This was particularly true for disadvantaged students.These figures, which consider students' and schools' socio-economic profile, show that the level of active support that parents offer their children might have a decisive effect.Yet parental involvement in students' learning at school decreased substantially between 2018 and 2022.On average across OECD countries, the share of students in schools where most parents independently initiated discussions about their child's progress with a teacher dropped by ten percentage points.Finally, we see a positive relationship between investment in education and average performance up to a threshold of USD 75 000 in cumulative spending per student from ages 6 to 15.For many OECD countries that spend more per student, there is no relationship between extra investment and student performance.Countries like Korea and Singapore have demonstrated that it is possible to establish a top-tier education system even when starting from a relatively low income level, by prioritising the quality of teaching over the size of classes and funding mechanisms that align resources with needs.To strengthen the role of education in empowering young people to succeed and ensuring merit-based equality of opportunity, the resilience of our education systems will be critical not only to improve learning outcomes measured through PISA, but to their long-term effectiveness.I'm pleased to share the 2022 PISA report with you, to provide policymakers across OECD Members and partner economies with evidence-based policy advice to design resilient and effective education systems that will help give our children and adolescents the best possible future.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.102
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0240.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.089
GPT teacher head0.451
Teacher spread0.362 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2024
Admission routes1
Has abstractyes

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