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Enregistrement W2051436187 · doi:10.1111/1529-1006.003

Class Size and Student Achievement

2001· article· en· W2051436187 sur OpenAlexaff
Ronald G. Ehrenberg, Dominic J. Brewer, Adam Gamoran, J. Douglas Willms

Notice bibliographique

RevueGothic.net · 2001
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueSchool Choice and Performance
Établissements canadiensUniversity of New Brunswick
Organismes subventionnairesnon disponible
Mots-clésClass (philosophy)EarningsMathematics educationPsychologySpace (punctuation)CognitionClass sizeComputer scienceArtificial intelligenceEconomics

Résumé

récupéré en direct d'OpenAlex

Schooling has multiple purposes. In the long run, higher levels of schooling are associated with higher earnings and economic mobility, better health, lower mortality rates, and greater democratic participation. For these reasons, most societies require children to attend school for a specified number of years or until they reach a certain age. Many of the benefits of schooling occur in part because students learn some new knowledge or skills that enhance their ability to communicate, solve problems, and make decisions. Much of the debate over schooling is essentially about how to maximize the amount of student learning, typically as measured by various assessment instruments such as standardized achievement tests. From a societal viewpoint, since resources—most notably, time—are required for learning, and are scarce, the amount of learning needs to be maximized at least cost. Learning is complex, involving cognitive processes that are not completely understood. Typically, school systems have established a primary mode of learning that involves groups of students of about the same age interacting with a single individual leading activities in a confined physical space, directed toward learning a particular topic—in other words, students are placed in classes. The number of other students in the class can vary. At the extreme, there can be one or more adults facilitating learning—teachers—with one or two students. At the other, a student may be one of a few hundred being taught by a single instructor (or, with new Internet technology, one of millions). The number of students in a class has the potential to affect how much is learned in a number of different ways. For example, it could affect how students interact with each other—the level of social engagement. This may result, for example, in more or less noise and disruptive behavior, which in turn affect the kinds of activities the teacher is able to promote. It could affect how much time the teacher is able to focus on individual students and their specific needs rather than on the group as a whole. Since it is easier to focus on one individual in a smaller group, the smaller the class size, the more likely individual attention can be given, in theory at least. The class size could also affect the teacher’s allocation of time and, hence, effectiveness, in other ways, too—for example, how much material can be covered. Teachers may choose different methods of teaching and assessment when they have smaller classes. For example, they may assign more writing, or provide more feedback on students’ written work, or use open-ended assessments, or encourage more discussions, all activities that may be more feasible with a smaller number of students. Exposure to a particular learning environment may affect learning over the time period of exposure, or it may have longer term or delayed effects (e.g., by increasing self-esteem or cognitive developments that have lasting effects). For these reasons, changes to the class size are considered a potential means of changing how much students learn. Not only is class size potentially one of the key variables in the “production” of learning or knowledge, it is one of the simplest variables for policymakers to manipulate. However, the amount of student learning is dependent on many different factors. Some are related to the classroom and school environment in which the class takes place, but others are related to the student’s own background and motivation and broader community influences. When we ask whether class size matters for achievement, it is essential to ask also, how class size matters. This is important for three reasons. First, if we can observe not only achievement differences, but also the mechanisms through which the differences are produced, this will increase our confidence that the differences are real, and not an artifact of some unmeasured or inadequately controlled condition. Second, the effects of class size may vary in different circumstances, and identifying how class size affects achievement will help us to understand why the effects of class size are variable. Third, the potential benefits of class-size reduction may be greater than what we observe. For example, suppose class-size reductions aid achievement, but only when teachers modify instructional practices to take advantage of the smaller classes. If a few teachers make such modifications, but most do not, then understanding how class size affects achievement in some cases will help reveal its potential effects, even if the potential is generally unrealized.

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)0,002
score de la tête « metaresearch » (Gemma)0,018
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,046
Score d'incertitude au seuil0,153

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,018
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,002
Études des sciences et des technologies0,0010,001
Communication savante0,0030,002
Science ouverte0,0010,002
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0460,007

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 Opus0,026
Tête enseignante GPT0,337
Écart entre enseignants0,311 · 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'étudeObservationnel
Domainenon disponible
GenreEmpirique

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

En bref

Citations384
Publié2001
Routes d'admission1
Résumé présentoui

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