The Efficacy of Recasts in Language Intervention: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
PURPOSE: This systematic review and meta-analysis critically evaluated the research evidence on the effectiveness of conversational recasts in grammatical development for children with language impairments. METHOD: Two different but complementary reviews were conducted and then integrated. Systematic searches of the literature resulted in 35 articles for the systematic review. Studies that employed a wide variety of study designs were involved, but all examined interventions where recasts were the key component. The meta-analysis only included studies that allowed the calculation of effect sizes, but it did include package interventions in which recasts were a major part. Fourteen studies were included, 7 of which were also in the systematic review. Studies were grouped according to research phase and were rated for quality. RESULTS: Study quality and thus strength of evidence varied substantially. Nevertheless, across all phases, the vast majority of studies provided support for the use of recasts. Meta-analyses found average effect sizes of .96 for proximal measures and .76 for distal measures, reflecting a positive benefit of about 0.75 to 1.00 standard deviation. CONCLUSION: The available evidence is limited, but it is supportive of the use of recasts in grammatical intervention. Critical features of recasts in grammatical interventions 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 | 0.025 | 0.062 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.030 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".