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Enregistrement W6986721812

Pısa 2006 Fen Başarı Testinin Madde Yanlılığının Kültür ve Dil Açısından İncelenmesi

2015· dissertation· en· W6986721812 sur OpenAlexaboutno aff

Notice bibliographique

RevueHacettepe University Institutional Repository (hacettepe.edu.tr) · 2015
Typedissertation
Langueen
DomaineEngineering
ThématiqueMilitary Technology and Strategies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésDifferential item functioningComparabilitySample (material)Test (biology)Item analysisSampling (signal processing)
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

In comparability investigations, the presence of differential item functioning (DIF) is considered to be an indication of possible bias. In this study, differential item functioning (DIF) analyses of Science items of PISA 2006 tests were carried out between different samplings in respect to language and culture.. Mantel Haenszel (MH), logistic regression(LR) and signed - unsigned area indexes methods were used for DIF
\ndetection analyses. The research group of this study consists of the Australia sample comprising 1124
\nstudents, the Canada sample comprising 1744 students; the England sample comprising
\n1008 students, the Turkey sample comprising 377 students; took the fifth booklets and the England sample comprising 1430 students, the Turkey sample comprising 380 students; took the first booklets. These countries were selected due to the differences in cultural relevance and linguistic are the possible main reasons for differential item functioning (DIF). In order to investigate the sources of DIF field specialist opinions were consulted. In the study, ın Canadien sampling, DIF was found in three items at B level and three items at negligible level according to the MH technique and in three item at negligible level according to LR technique, in five items according to each fo signed - unsigned area indexes methods. In Australia- England sampling DIF was found in one item at B level and C level, four items at negligible level according to the MH technique and in
\nfour items at negligible level according to LR technique, in two items according to each
\nfo signed - unsigned area indexes methods. In England- Turkey sampling for the first
\nbooklet; ten items included DIF according to MH results; five of them were at A level, two of them were at B level and three of them were at C level according to the MH technique those of the items eight of them favored English form, where two of them
\nfavored Turkish form. DIF was found in five items at negligible level and one item at B level according to LR technique, in six items according to each for signed-unsigned area indexes methods. In England-Turkey sampling for the fifth booklet; in two items at A 
\nlevel and in four items at B level and in five items at C level according to the MH technique and infour item at negligible level and two items at B level according to LR technique, in six items according to each fo signed-unsigned area indexes methods had
\nDIF. It is observed that as the linguistic and cultural differences increased between countries,
\nthe number of DIF items increased. The number of DIF items varied significantly according to the procedure used. The correlation coefficients for the same culturedifferent language between LR and MH were significant, Non‐signed area indexes and
\nSigned area indexes were significant at α =0,01. The correlation coefficients for the different culture-same language between LR and MH were significant at α =0,05, For the different culture and language LR and MH were significant at α =0,01for the first
\nbooklet and Non‐signed area indexes and Signed area indexes were significant at α=0,01for the fifth booklet. Generally; results of bias researchs indicated that the main possible reasons for DIF is due to differences in cultural relevance, linguistic differences and differences in curriculum.

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)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Études des sciences et des technologies, Intégrité de la recherche
Catégories consensuellesMéta-épidémiologie (sens strict), Intégrité de la recherche
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,432
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0010,002
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0020,001
Communication savante0,0000,001
Science ouverte0,0020,000
Intégrité de la recherche0,0020,003
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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 Opus0,009
Tête enseignante GPT0,197
Écart entre enseignants0,188 · 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeSans objet
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
Publié2015
Routes d'admission1
Résumé présentoui

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