Notice bibliographique
Résumé
Background Findings from several international studies have shown that there is a significant relationship between literacy skills and socioeconomic status (SES). Research has also shown that schools differ considerably in their student outcomes, even after taking account of students’ ability and family background. The context or learning environment of a school or classroom is an important determinant of the rate at which children learn. The literature has traditionally used school composition, particularly the mean SES of the school, as a proxy for context. Focus of Study This study examines the relationships among school composition, several aspects of school and classroom context, and students’ literacy skills in science. Population The study uses data from the 2006 PISA (Programme for International Student Assessment) for 57 countries. PISA assesses the knowledge and life skills of 15-year-old youth as they approach the end of their compulsory period of schooling. Research Design Secondary analyses of the data describe the socioeconomic gradient (the relationship between a student outcome and SES) and the school profile (the relationship between average school performance and school composition) using data for the United States as an example. The analyses demonstrate two important relationships between school composition and the socioeconomic gradient and distinguish between two types of segregation, referred to as horizontal and vertical segregation. The analyses discern the extent to which school composition and classroom and school context separately and jointly account for variation in student achievement. Findings The results show that school composition is correlated with several aspects of school and classroom context and that these factors are associated with students’ science literacy. Literacy performance is associated with the extent to which school systems are segregated “horizontally,” based on the distribution among schools of students from differing SES backgrounds, and “vertically,” due mainly to mechanisms that select students into different types of schools. Conclusions An understanding of socioeconomic gradients and school profiles for a school system is critical to discerning whether reform efforts should be directed mainly at improving the performance of particular schools or at striving to alter policies and practices within all schools. Both horizontal and vertical segregation are associated with lower student outcomes; therefore, we require a better understanding of the mechanisms through which students are allocated to schools. When the correlation of school composition with a particular contextual variable is strong, it calls for policies aimed at increasing inclusion or differentially allocating school and classroom resources among schools serving students of differing status.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,001 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».