Toward a culture of reading: four perspectives
Notice bibliographique
Résumé
Other Factors and Student AchievementPoverty, and other health problems (poor nutrition, low birth weight, substandard housing, high violence, and substance abuse) associated with poverty, can depress achievement (Kober, 2001).The College Board (1999) reported in 1990 African-American children were three times as likely to be raised in low-income families as compared to White and Asian children.Hispanic children were twice as likely to be impoverished.Although a portion of the achievement gap can be explained by socioeconomic background, when these differences are factored out there is still an achievement gap that must be due to other factors (Kober, 2001).Gender differences must also be considered when examining achievement.Although gender differences in mathematics achievement have been declining, there is still a gap in upper-level mathematics course enrollment by females resulting in fewer females choosing professions in math, science, and technology (Dick & Rallis, 1991).Women are underrepresented in the fields of mathematics, science, and engineering contributing to the significant gap in earning ability between males and females.And, although women make up 44% of the work force, they make up only 15 % of those working in the fields of math, science, and technology (National Science Foundation, 1989).Johnson (2000) examined factors influencing advanced math coursework and students" attitudes toward math and found peer relationships have different effects on female and male decisions regarding mathematics.To address the influences of SES and gender differences on achievement, the present study controlled for these factors when examining the effects of peer and parent encouragement on math scores. MethodsUsing the National Education Longitudinal Study: 1988/2000 (NELS) dataset, the present study uses General Linear Modeling (GLM) to examine the effects of peer influence on high school mathematics achievement.The study considers parental encouragement, socio-economic level, race, and sex to investigate differential effects.In addition, the study used a longitudinal data analysis to assess the long-term effects of peer and parent influence relationships.Specifically, the following research questions were addressed: Does peer encouragement/ discouragement to take algebra show a significant difference in mathematics achievement in the eighth, tenth and twelfth grades?How does the relationship vary across ethnicity when controlled for SES and gender?Does encouragement of peers and parents have a lasting effect on mathematics achievement (8 th through 12 th grades)?How does the relationship vary across ethnicity when controlled for SES and gender?This study uses three measures from the NELS survey, in which 1988 schools and students were selected using a two-stage stratified probability design with over 24,000 students in more than 1,000 schools sampled.This study performs both cross-sectional and longitudinal analyses using General Linear Modeling (GLM) and Hierarchical Linear Modeling (HLM).
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 | 0,006 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,010 | 0,032 |
| Communication savante | 0,017 | 0,008 |
| Science ouverte | 0,001 | 0,007 |
| Intégrité de la recherche | 0,002 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
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 ».