Should Bilingual Children Learn Reading in Two Languages at the Same Time or in Sequence?
Bibliographic record
Abstract
Is it best to learn reading in two languages simultaneously or sequentially? We observed second- and third-grade children in two-way dual-language learning contexts: (a) 50:50 or Simultaneous dual-language (two languages within same developmental period) and (b) 90:10 or Sequential dual-language (one language, followed gradually by the other). They were compared to matched monolingual English-only children in single-language English schools. Bilinguals (home language was Spanish only, English-only, or Spanish and English in dual-language schools), were tested in both languages, and monolingual children were tested in English using standardized reading and language tasks. Bilinguals in 50:50 programs performed better than bilinguals in 90:10 programs on English Irregular Words and Passage Comprehension tasks, suggesting language and reading facilitation for underlying grammatical class and linguistic structure analyses. By contrast, bilinguals in 90:10 programs performed better than bilinguals in the 50:50 programs on English Phonological Awareness and Reading Decoding tasks, suggesting language and reading facilitation for surface phonological regularity analysis. Notably, children from English-only homes in dual-language learning contexts performed equally well, or better than, children from monolingual English-only homes in single-language learning contexts. Overall, the findings provide tantalizing evidence that dual-language learning during the same developmental period may provide bilingual reading advantages.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".