Are first- and second-language factors related in predicting second-language reading comprehension? A study of Spanish-speaking children acquiring English as a second language from first to second grade.
Bibliographic record
Abstract
First-language (L1) and 2nd-language (L2) oral language skills and L2 word reading were used as predictors to test the simple view of reading as a model of 2nd-language reading comprehension. The simple view of reading states that reading comprehension is related to decoding and oral language comprehension skills. One hundred thirty-one Spanish-speaking English learners (ELs) were tested in 1st grade and many were followed into 2nd grade, including a full sample of 79. Structural equation modeling confirmed that a 5-factor measurement model had the best fit, suggesting that L1 and L2 phonological awareness should be viewed as separate but related constructs and that L1 and L2 oral language proficiency, measured by vocabulary and grammatical awareness, were separate constructs. The structural model indicated that for this group of ELs, who were educated in English, English oral language proficiency and word reading were the strongest predictors of English reading comprehension. Other models that deleted 1 of these crucial components resulted in significantly poorer fit. Therefore, the results support the validity of the simple view of reading as a model for the development of reading comprehension in young ELs. Implications for theory and practice, specifically assessment of ELs, are discussed.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".