The impact of online learning during the pandemic on language and reading performance in English–French bilingual children
Notice bibliographique
Résumé
Background The COVID‐19 pandemic created a unique learning experience, characterised by school closures and a shift to online learning. Research suggests that online learning during the pandemic negatively impacted the reading development of elementary school children. However, little is known about the challenges of learning a second language (L2) remotely. Therefore, this study investigates the impact of online learning during the pandemic on language and reading development among French immersion (FI) students who learn French as an L2. Methods A total of 137 Grade 1 and Grade 2 students from two cohorts were included in the study. The in‐person cohort consisted of 72 students who attended school in person and were tested in person before the pandemic. The online cohort consisted of 65 students who received virtual instruction during the pandemic and were tested online. Measures of vocabulary, word reading accuracy and fluency, and phonological awareness were administered in English and French to both cohorts. Analyses of covariance (ANCOVAs) were carried out to assess the effects of cohort and grade on the measures, with guardian education as a covariate. Results Students in the in‐person cohort performed significantly better on French vocabulary and English word reading accuracy than students online. The cohort effect was not significant for other French and English measures. Grade 2 students significantly outperformed Grade 1 students in both English and French vocabulary and word reading. Conclusions The current results suggest that online learning may have had a moderately negative effect on French vocabulary but no impact on French phonological awareness or word reading. FI students' English skills were also largely unaffected. Therefore, FI students made progress on their language and literacy skills through online learning during the pandemic. The findings point to the importance of enhancing L2 vocabulary input during online 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 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 | 0,007 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| 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 | 0,002 |
| 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 ».