The Effect of Age of First Exposure on Vocabulary, Mean Length of Utterance, Morphosyntactic Accuracy, and Semantic and Sentence-Level Patterns in the First 2 Years of French Second-Language Learning by Preschool- to Adolescent-Age Mandarin Speakers
Notice bibliographique
Résumé
BACKGROUND: Bilingual assessment is particularly difficult in the very first period of children's second language (L2) exposure. This exploratory, longitudinal study examined L2 learning after 1 and 2 years of L2 exposure by young immigrants and how it is affected by their age at first exposure to the L2 (AoE). METHOD: Participants were 18 immigrants ranging in age from 2;11 to 14;2 (years;months), all within their first year in Montreal at Time 1, enrolled in a French school or day care, and from a Mandarin first language background. Participants were tested again a year later. Measures included receptive and expressive French vocabulary tests and conversational language samples analyzed using traditional measures of mean length of utterance (MLU) and morphological accuracy as well as novel measures of semantic and sentence-level patterns. RESULTS: Performance was relatively high already at Time 1 and increased significantly at Time 2 in both vocabulary and MLU. At Time 2, vocabulary scores were below normative values, whereas MLU was within expected values relative to monolingual and simultaneous bilinguals for the majority of the participants. However, higher MLUs were accompanied by more instances of both semantic errors and creative semantic strategies. French performance was strongly related to AoE; with amount of exposure equivalent, older participants outperformed the younger ones on MLU and vocabulary. Semantic errors and creative uses were strongly predicted by AoE; however, morphological accuracy and number of agrammatical utterances were not. CONCLUSIONS: This initial period of French learning involved a rapid growth spurt for most of the participants. We argue that the pattern observed, particularly among the older children, constitutes an early stage of L2 learning characterized by long utterances that are also frequently hard to understand as speakers encounter challenges and use creative strategies in their attempt to convey meaning. Comparison with normative reference bases for monolinguals and bilinguals with greater cumulative L2 exposure who have similar MLUs should be done with much caution during this early period.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».