MétaCan
Menu
Retour à la cohorte
Enregistrement W4223899626 · doi:10.1111/1460-6984.12723

Dynamic assessment of multilingual children's word learning

2022· article· en· W4223899626 sur OpenAlexaff
Andrea A. N. MacLeod, Amy M. Glaspey

Notice bibliographique

RevueInternational Journal of Language & Communication Disorders · 2022
Typearticle
Langueen
DomainePsychology
ThématiqueEducational and Psychological Assessments
Établissements canadiensUniversity of Alberta
Organismes subventionnairesnon disponible
Mots-clésVocabularyTask (project management)Dynamic assessmentPsychologyLanguage developmentVocabulary developmentLanguage acquisitionWord (group theory)Standardized testCognitive psychologyDevelopmental psychologyLinguisticsMathematics education

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Teachers and clinicians may struggle to provide early identification to support multilingual children's language development. Dynamic assessments are a promising approach to identify and support children's language development. AIMS: We developed and studied a novel word learning task that is dynamic and language neutral. It makes use of multilingual children's abilities to apply language transfer, fast mapping and socially embedded language to the learning of new words. METHODS & PROCEDURES: A total of 26 children attending kindergarten in French participated in this study. Within this group, 13 different home languages were spoken. Children took part in a dynamic assessment task of their word learning that consisted of a test-teach-retest task. Children's scores on this task were compared with their language abilities reported by their parents, amount of language exposure and scores on standardized tests of vocabulary. All tasks were delivered in French. OUTCOMES & RESULTS: Children had higher accuracy for known words as compared with new words in the task, which may suggest transfer of knowledge from their first language. They also showed increased accuracy in identifying and naming the new words across the three trials, suggesting fast mapping of these new vocabulary items. Finally, the scores on the dynamic task correlated to children's vocabulary scores on the standardized tests, but not parent report of language development, or the amount of exposure to the language of school. CONCLUSIONS & IMPLICATIONS: This novel dynamic assessment task taps into the process of vocabulary learning, but is less influenced by prior language knowledge. Together, these findings provide insight into early word learning by young multilingual children and proposes a conceptual model for identifying strategies to support second language acquisition. WHAT THIS PAPER ADDS: What is already known on the subject Many barriers exist with regards to assessing the language abilities of multilingual children when a clinician aims to assess their language abilities in both languages. An alternative approach is to measure children's language processing abilities. What this paper adds to existing knowledge A novel dynamic and multilingual task was developed and implemented in this study. This task builds on children's word learning abilities that include cross-language transfer, fast-mapping, and socially imbedded learning. This multilingual task was found to tap into vocabulary learning but was not influenced by prior language knowledge. What are the potential or actual clinical implications of this work? Applying a task that focuses on language processing abilities is a promising strategy to capture language abilities in multilingual children. In addition, the dynamic nature of this tasks allows a clinician to identify scaffolding strategies that best support children's word learning.

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: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,340
Score d'incertitude au seuil0,994

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 ouverte0,0010,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0070,000

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 Opus0,014
Tête enseignante GPT0,416
Écart entre enseignants0,402 · 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

Citations8
Publié2022
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueInternational Journal of Language & Communication DisordersMême sujetEducational and Psychological AssessmentsTravaux en français237 207