Assessments of English Reading and Language Comprehension in Bilingual Children: A Systematic Review 2010 to 2021
Bibliographic record
Abstract
Learning to read marks an important milestone in children. Extensive research with monolingual and bilingual children has demonstrated that language comprehension (LC) forms fundamental building blocks for reading comprehension (RC). However, mixed findings are reported among studies that compare readings skills in children with and without diverse language experiences. Depending on how researchers operationalize the construct of LC and RC, studies use different standardized tests or assessments to assess reading skills in children, which may lead to different findings across studies. The current review systematically examined tests of LC and RC that empirical studies have used to assess bilingual children who speak English as their second language. Out of an initial sample of 374 studies, 25 were eligible for inclusion. We extracted LC and RC assessments from the studies and documented task- and administration-related factors. Moreover, participant characteristics, definition of LC as described by authors, and findings related to the relationship between LC and RC were examined for each study. Our results demonstrated variability in the measures and definitions used to assess and describe LC and RC, potentially explaining the mixed findings in the literature. We underscore the importance of considering the multidimensional nature of LC and the need to further explore how different administrative and task characteristics of LC tests relate to RC. Furthermore, this review provides researchers and practitioners with an original and extensive survey of the literature on how LC and RC were assessed among bilingual children. Lastly, we highlight limitations in the current literature and discuss practical implications in the field of school psychology in supporting children with diverse language experiences.
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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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".