Reading comprehension and strategy use: Comparing bilingual children to their monolingual peers and to bilingual adults
Bibliographic record
Abstract
The current study investigated the predictive ability of language knowledge and reported strategy use on reading comprehension performance in English-speaking monolingual and bilingual students. One hundred fifty-five children in grade 4 through 6 (93 bilinguals and 62 monolinguals) were assessed on receptive vocabulary, word reading fluency, reading comprehension, and reading strategy use in English. An additional 38 adult bilinguals (i.e., English Language Learners) were assessed on the same measures. Although, the bilingual adult group and bilingual children had significantly lower English vocabulary knowledge relative to the monolingual children, the bilingual adults exhibited reading comprehension performance that was on par with the monolingual children; both groups outperformed the bilingual children. This discrepancy was accounted for by reported strategy use, wherein bilingual adults reported more inferencing, more connecting between sections of text and more reference to the text structure than the children. Reported strategy use also accounted for unique variance in reading comprehension performance above and beyond the contributions of English vocabulary knowledge and word reading fluency. Findings highlight the strategies that successful readers report and emphasize the value of promoting effective strategy selection in addition to language instruction in the development of reading comprehension skill.
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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 | 0.000 |
| 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".