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Is the Sky the Limit to Education Improvement? Comparing Your School to a Neighboring School Is No Longer Sufficient; You Must Compare Your School, Your District, and Your Country to the Best Performers in the World

2011· article· en· W324298873 sur OpenAlexaboutno aff
Andreas Schleicher

Notice bibliographique

RevuePhi Delta Kappan · 2011
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueEducation in Diverse Contexts
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPaceDisadvantagedEducation policyMathematics educationEconomic growthBest practicePolitical scienceEconomicsHigher educationPsychologyManagementGeography
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

In the global economy, where all work that can be automated, digitized, or outsourced can now be done anywhere by the people best qualified to do it, national standards are no longer the yardstick for measuring education success. Instead, the new metric is the best-performing education systems internationally. By showing what's possible in education, the best-performing systems can: * Demonstrate how to optimize policies and help countries consider alternatives to existing policies and practices. For example, the Program for International Student Assessment (PISA) shows Canadian 15-year-olds, on average, to be almost a school year ahead of American 15-year-olds in key subjects such as mathematics or science. PISA results also show that socio economically disadvantaged Canadians are much less at risk of poor educational performance than similar U.S. students. * Help countries set measurable goals by viewing what's been achieved by other systems and help identify policy levers and establish trajectories for reform. * Help gauge the pace of education progress and review the reality of education delivery. For example, Portugal, Poland, Israel, and Chile raised the performance of their 15-year-olds in PISA reading by the equivalent of between half and a full school year in less than a decade. * Support the political economy of education reform, which is a major issue in education where any payoff to reform almost inevitably accrues to successive governments if not generations. So, where does the U.S. stand in comparison with the principal industrialized countries, and what policy levers for education improvement emerge from international comparisons and transcend economic and cultural settings? U.S. IS LOSING ITS ADVANTAGE Among the 30 OECD countries with the largest expansion of college education over the last decades, most still see rising earnings differentials for college graduates, suggesting that having more knowledge workers doesn't necessarily lead to lower pay as is the case for low-skilled workers (OECD, 2010b). The other player in globalization is technological development, but this also depends on education. Tomorrow's knowledge workers and innovators require high levels of education, and a highly educated workforce is a prerequisite for adopting and absorbing new technologies and increasing productivity. Together, skills and technology have flattened the world. That means that all work that can be digitized, automated, and outsourced can now be done by the most effective and competitive individuals, enterprises, or countries--wherever they are. But no country has been able to capitalize on the opportunities of this flat world more than the U.S. The United States can draw on the most educated labor force among the principal industrialized nations, at least when measured by formal qualifications. However, this advantage accrues largely because of the first-mover advantage, which the U.S. gained after World War II by massively increasing enrollments. That advantage is now eroding quickly as more countries reach and surpass U.S. qualification levels. In fact, many countries are now close to ensuring that virtually all young adults have at least a high school diploma (OECD average 80%). OECD calls that a baseline qualification for reasonable earnings and employment prospects. Over time, this will translate into better workforce qualifications in these countries. In contrast, the U.S. stood still on this measure (76%). Among OECD countries, only Turkey, Mexico, Chile, Luxembourg, and Spain now have lower high school completion rates than the U.S. Two generations ago, South Korea had the economic output of Afghanistan today and ranked 24th in education output among today's OECD countries. Today, 98% of an age cohort obtain a high school diploma. In college education, the U.S. slipped from 2nd position in 1995 to 14th position in 2009--not because U. …

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,025
score de la tête « metaresearch » (Gemma)0,061
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,063
Score d'incertitude au seuil0,134

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0250,061
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0030,005
Études des sciences et des technologies0,0090,022
Communication savante0,0220,039
Science ouverte0,0030,012
Intégrité de la recherche0,0060,013
Charge utile insuffisante (le modèle a refusé de juger)0,0260,010

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 Opus0,096
Tête enseignante GPT0,348
Écart entre enseignants0,252 · 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; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

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
Publié2011
Routes d'admission1
Résumé présentoui

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