Impacts of the COVID-19 disruption on the language and literacy development of monolingual and heritage bilingual children in the United States
Bibliographic record
Abstract
Children who speak one language at home and a different language at school may be at higher risk of falling behind in their academic achievement when schooling is disrupted. The present study examined the effects of COVID-19-related school disruptions on English language and literacy development among monolingual and bilingual children in the US. All children attended English-only schools that implemented varied forms of virtual and hybrid schooling during the pandemic. Pre-COVID-19 and during-COVID-19 examinations were conducted with 237 children (M(SD)age = 7.78 (1.54) at Time 1) from relatively high SES homes, including 95 monolinguals, 75 Spanish-English and 67 Chinese-English bilinguals. The findings revealed different impacts of COVID-19 school disruptions on the present bilingual and monolingual participants. Specifically, between Time 1 and Time 2, monolingual children made age-appropriate improvements in all literacy measurements. Relative to monolinguals, both bilingual groups showed greater gains in vocabulary but lower gains in reading comprehension. Moreover, across groups, children’s independent reading practices during COVID-19 were positively associated with children’s literacy growth during the pandemic-related schooling disruptions. Taken together, these findings inform theoretical perspectives on learning to read in linguistically diverse children experiencing COVID-19-related schooling disruptions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".