Early Precursors of Reading Development in Simultaneous Bilinguals: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Purpose: Literacy precursors are cognitive, linguistic, and oral-language skills that predict future reading skills in children as young as 4 years. Speech-language pathologists and educators utilize these precursors as assessment tools to identify children at risk for reading difficulties. Most current tools are developed based on monolinguals (predominantly in English), despite the significant percentage of bilinguals globally. As such, bilingual children are typically assessed on tools developed for monolinguals in research and clinical settings. Despite this common practice, there is a lack of comprehensive synthesis on whether these precursors are a reliable indicator of reading skills in bilingual children. Our article examines whether literacy precursors commonly used with monolinguals are associated with reading development in simultaneous bilinguals. Method: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses and Cochrane guidelines, our review includes four databases (Linguistics and Language Behavior Abstracts, Educational Resources Information Center, Modern Language Association, and PsycINFO), in addition to gray-literature and manual reference-list searches. To control for age of acquisition and language dominance variability, we included typically developing simultaneous bilinguals exposed to both languages before age 3 years ( N = 5,942). We analyzed reported statistical associations between code-related or oral-language precursors and reading outcome measures, using correlational meta-analyses. Results: The 41 reports, which met inclusion/exclusion criteria, were published between 1977 and 2022. The average age at assessment was 7;5 (years;months; range: 3;0–11;0), with children speaking over 21 bilingual language combinations. Our meta-analysis demonstrated significant within-language correlations and cross-language transfer effects for code-related (e.g., phonological awareness) and oral-language (e.g., vocabulary and morphological awareness) precursors. Semantic awareness, however, was not a reliable predictor in bilinguals. Conclusions: Phonological awareness and vocabulary measures—even if originally developed for monolingual children—can form a meaningful component of early literacy assessment in simultaneous bilingual children: These precursors may be used as assessment tools across heritage and societal languages in research and clinical practice. Future research suggestions within this domain are also discussed. Supplemental Material: https://doi.org/10.23641/asha.23880465
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".