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Enregistrement W6889633344 · doi:10.25946/21454098

Examination of low scoring nine year old respondents in the IEA reading literacy study from English speaking countries

2022· dissertation· en· W6889633344 sur OpenAlexaboutno aff

Notice bibliographique

RevueCentral Queensland University · 2022
Typedissertation
Langueen
DomainePsychology
ThématiqueReading and Literacy Development
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésReading (process)LiteracySample (material)Rasch modelSet (abstract data type)Data collectionMultivariate analysisMandarin Chinese

Résumé

récupéré en direct d'OpenAlex

The reading literacy study, conducted in 1990/91 by the International Association for the Evaluation of Educational Achievement (IEA), measured the performance of 9 year old students from 27 countries across the world. Until now, no specific analyses of the low scoring students has been undertaken. The aim of this secondary analysis of lEA reading literacy data was to examine the following question: What factors operate to influence the identification of low scores in reading literacy within and between identifiable cultural categories? Low scoring students were included when their scores fell below 100 rasch points (approximately 2.5 years) below their respective country mean. English speaking countries included in the analysis, all of which have historical ties to England, were Canada, New Zealand, Trinidad and Tobago, and the United States. Low scoring sample sizes exceeded 12% of their respective total sample. Typical differences featured when the background qualities of students (i.e. sex, language background, wealth) in the low scoring and respective country samples were compared. To examine the reading factors influencing low scores, the models of reading proposed by the lEA were tested across and within low scoring country and international data sets. Through conducting principal components analyses (PCA), it was found that the text and skills based models proposed by the lEA were not supported. New models of reading for each data set were devised and saved for further multivariate analyses. The factors of the newly theorized reading literacy constructs are concerning with poor fitting data, though similar patterns are found across the data sets. These results indicate that the variables in the reading test examined other skills, knowledge and experiences. Procedures of MANCOVA or MANOVA were applied to each data set to facilitate identification of significant personal background factors (independent variables) on the saved component scores (dependent variables). The reading behaviour constructs (Reading in Class, Voluntary Reading, Home Literacy Interaction) devised by the lEA were included as covariates following respecification using PCA where appropriate. A socio-economic construct was devised for each country using PCA and was included as another covariate. Canada was the only country to have no significant covariates, and so, a straight MANOVA was applied. Socioeconomic status predicted student performance in all countries except Canada. Home Literacy Interaction predicted performance on one component in the United States and Internationally. Low scoring boys obtained higher scores than the girls on items with a mathematical component, and girls tended to obtain higher scores when information was presented in a narrative or literal form. Where significant differences feature, native English speaking students consistently out perform non-native speakers. Questions are raised about traditional cognitive views of reading comprehension and standardized testing. Evidence accumulated throughout the thesis lends credence for explanations of reading literacy favouring sociocultural viewpoints.

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 candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,446
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

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

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.

Tête enseignante Opus0,010
Tête enseignante GPT0,269
Écart entre enseignants0,258 · 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 tête enseignante, pas un consensus.

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

Citations0
Publié2022
Routes d'admission1
Résumé présentoui

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