English Language Learners' Nonword Repetition Performance: The Influence of Age, L2 Vocabulary Size, Length of L2 Exposure, and L1 Phonology
Bibliographic record
Abstract
PURPOSE: This study examined individual differences in English language learners' (ELLs) nonword repetition (NWR) accuracy, focusing on the effects of age, English vocabulary size, length of exposure to English, and first-language (L1) phonology. METHOD: Participants were 75 typically developing ELLs (mean age 5;8 [years;months]) whose exposure to English began on average at age 4;4. Children spoke either a Chinese language or South Asian language as an L1 and were given English standardized tests for NWR and receptive vocabulary. RESULTS: Although the majority of ELLs scored within or above the monolingual normal range (71%), 29% scored below. Mixed logistic regression modeling revealed that a larger English vocabulary, longer English exposure, South Asian L1, and older age all had significant and positive effects on ELLs' NWR accuracy. Error analyses revealed the following L1 effect: onset consonants were produced more accurately than codas overall, but this effect was stronger for the Chinese group whose L1s have a more limited coda inventory compared with English. CONCLUSION: ELLs' NWR performance is influenced by a number of factors. Consideration of these factors is important in deciding whether monolingual norm referencing is appropriate for ELL children.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".