The Role of Comprehension Monitoring in Predicting Reading Comprehension Among French Immersion Children
Bibliographic record
Abstract
Purpose This study investigated the extent to which comprehension monitoring in children’s first and second language predicts reading comprehension.Method Children’s ability to detect inconsistencies in orally presented stories was measured by response to a judgment question about whether the story made sense and by the identification of the inconsistency within the story. The participants included 115 English-French bilingual children (MageGrade2 = 7.8 years) recruited from a French immersion program in Canada.Results In each language, two regressions were carried out to examine the contribution of comprehension monitoring to reading comprehension in Grades 2 and 3, and one regression was computed to examine the contribution of Grade 2 comprehension monitoring to Grade 3 reading comprehension. The concurrent results revealed that, in Grade 3, children’s comprehension monitoring was a unique predictor of reading comprehension in English and French. This relationship was not observed in Grade 2. Notably, the longitudinal analyses indicated that Grade 2 children’s comprehension monitoring in English made a significant contribution to English reading comprehension in Grade 3. However, this relationship was not established in French.Conclusions These results promote a call to include support for higher-level oral language skills during the early stages of bilingual reading instruction.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".