The impact of online learning during the pandemic on language and reading performance in English–French bilingual children
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".