A Meta‐Analysis on the Effects of Peer Tutoring on Emergent Bilinguals’ Academic Achievement
Bibliographic record
Abstract
ABSTRACT This meta‐analysis investigated the effects of peer tutoring interventions on the academic achievement of Emergent Bilingual (EB) students from preschool through Grade 12. Fourteen studies, including peer‐reviewed journal articles and unpublished dissertations published between 2010 and 2021, were analyzed based on study characteristics, student variables, and intervention characteristics. The findings indicate that peer tutoring yielded a moderate effect size (g = 0.58, 95% CI = 0.22–0.94) on EBs’ academic outcomes. The majority of studies focused on elementary‐aged students, with significantly fewer studies addressing preschool or secondary‐level EBs. Nearly all studies involved Spanish‐speaking students (n = 14), and most targeted reading outcomes demonstrated a moderate effect size (g = 0.62, 95% CI = 0.24–1.00). Only a limited number of effects came from studies involving EBs with disabilities. These findings underscore the potential of peer tutoring to support reading development among EBs while highlighting critical research gaps, particularly at the secondary level and in core content areas such as mathematics. Implications for practice and directions for future research 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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.029 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".