Children's Second Language Acquisition of English Complex Syntax: The Role of Age, Input, and Cognitive Factors
Bibliographic record
Abstract
ABSTRACT The goal of this study was to determine (a) the similarities and dissimilarities between child L2 and L1 acquisition of complex sentences and (b) the individual difference factors predicting L2 children's acquisition of complex sentences. We analyzed language samples from 187 English L2 children with diverse L1s (Agemean= 5;10 [years;months]; English exposuremean= 17 months). Children used various types of complex sentences at all levels of L2 exposure, including sentences with relative clauses, which are late-acquired by L1 learners. Mixed logistic regression modeling revealed that longer exposure to English in school, richer English environments outside school, larger L2 vocabulary, superior verbal memory and visual analytic reasoning contributed to greater use of complex sentences. L1 typology did not impact complex sentence use in the L2. Overall, L2 children used more complex sentences within a few months of English L2 exposure than what is reported for L1 children aged 2;0–4;0, revealing an advantage for an older age of acquisition. The predictive role of input and cognitive factors, as well as vocabulary, in children's use of complex sentences is more consistent with constructivist than generativist accounts of L2 syntactic acquisition.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".