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Enregistrement W4400705441 · doi:10.1787/79f720fe-en

Snapshot of school environment conductive to creative thinking

2024· other· en· W4400705441 sur OpenAlexaboutno aff

Notice bibliographique

RevueProgramme for international student assessment/Internationale Schulleistungsstudie · 2024
Typeother
Langueen
DomainePsychology
ThématiqueCreativity in Education and Neuroscience
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésChinaEquity (law)GeographySocioeconomic statusCoronavirus disease 2019 (COVID-19)Political scienceEconomic growthDemographySociologyEconomicsPopulationMedicine

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesCharge utile insuffisante (le modèle a refusé de juger)
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Autre · Signal consensuel: Autre
Score de désaccord entre enseignants0,102
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0020,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0240,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,089
Tête enseignante GPT0,451
Écart entre enseignants0,362 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeSans objet
Domainenon disponible
GenreAutre

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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