The Development of English as a Second Language With and Without Specific Language Impairment: Clinical Implications
Bibliographic record
Abstract
PURPOSE: The purpose of this research forum article is to provide an overview of typical and atypical development of English as a second language (L2) and to present strategies for clinical assessment with English language learners (ELLs). METHOD: A review of studies examining the lexical, morphological, narrative, and verbal memory abilities of ELLs is organized around 3 topics: timeframe and characteristics of typical English L2 development, comparison of the English L2 development of children with and without specific language impairment (SLI), and strategies for more effective assessment with ELLs. RESULTS: ELLs take longer than 3 years to converge on monolingual norms and approach monolingual norms asynchronously across linguistic subdomains. Individual variation is predicted by age, first language, language learning aptitude, length of exposure to English in school, maternal education, and richness of the English environment outside school. ELLs with SLI acquire English more slowly than ELLs with typical development; their morphological and nonword repetition abilities differentiate them the most. Use of strategies such as parent questionnaires on first language development and ELL norm referencing can result in accurate discrimination of ELLs with SLI. CONCLUSIONS: Variability in the language abilities of ELLs presents challenges for clinical practice. Increased knowledge of English language learning development with and without SLI together with evidence-based alternative assessment strategies can assist in overcoming these challenges.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".