Are Reading Interventions for English Language Learners Effective? A Meta-Analysis
Bibliographic record
Abstract
Despite concerted efforts to improve the reading skills of English language learners (ELLs), it remains unclear if the interventions they have been receiving produce any positive results. Thus, the purpose of this meta-analysis was to examine how effective reading interventions are in improving ELLs' reading skills and what factors may influence their effectiveness. Twenty-six studies with reported outcomes for pretest and posttest were selected, and four moderators (group size, intensity of intervention, students' risk status, and type of intervention) were coded. The results of random-effects analyses showed that the reading interventions had a large effect on ELLs' reading accuracy ( d = 1.221) and reading fluency ( d = 0.802) and a moderate effect on reading comprehension ( d = 0.499). In addition, for real-word reading accuracy, intervention groups composed of more than five students were less effective than groups composed of two to five students, and longer intervention sessions were less effective than shorter ones. Overall, our findings suggest that reading interventions have positive effects on ELLs' reading skills, and they should not be delayed until these students have reached a certain level of oral English proficiency.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.023 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".