Grammatical Morphology in Children Learning English as a Second Language
Bibliographic record
Abstract
PURPOSE: This study was conducted to examine whether the expressive language characteristics of typically developing (TD) children learning English as a second language (ESL) have similarities to the characteristics of the English that is spoken by monolingual children with specific language impairment (SLI), and whether this could result in the erroneous assessment of TD English-language learners (ELLs) as language impaired. METHOD: Twenty-four TD language-minority children who had been learning ESL for an average of 9.5 months participated in the study. The children's accuracy and error types in production of the following grammatical morphemes were examined in spontaneous and elicited speech: third person singular [-s], past tense [-ed], irregular past tense, BE as a copula and auxiliary verb, DO as an auxiliary verb, progressive [-ing], prepositions in and on, plural [-s], and determiners a and the. The elicitation probes were part of a recently developed standardized test for identifying language impairment, the Test of Early Grammatical Impairment (TEGI; M. Rice & K. Wexler, 2001). RESULTS: The ELLs' accuracy rates and error patterns with the grammatical morphemes were similar to those that have been reported for same-age monolingual children with SLI, in both spontaneous and elicited speech. In addition, the ELL's elicitation probe scores were compared to the criterion scores and group means from the sample of monolingual children used to develop the TEGI and their performance on the TEGI was in the range of the clinical population even though there is no reason to suspect that any of these children is language impaired. Both analyses point to the possibility that TD ELLs could be mistaken as language impaired. CLINICAL IMPLICATIONS: The results provide information that can be used to set appropriate expectations of error patterns and rate of grammatical development in the early stages of ESL learning. The results also emphasize how the use of English standardized tests with nonnative English-speakers is not a good practice, and suggestions are given for points to consider when assessing ELLs.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".