The Effectiveness of Extensive Versus Intensive Recasts for Learning L2 Grammar
Bibliographic record
Abstract
This study investigated the effects of extensive versus intensive recasts. The focus was on the effect of feedback on learning English articles, which, as nonsalient target structures, have been shown to be difficult for many second language learners. Intensive recasts were operationalized as recasts provided on article errors only, while extensive recasts were provided on any errors including article errors. Forty‐eight adult intermediate learners of English as a second language (ESL) were divided into 3 groups: an intensive recast group, an extensive recast group, and a control group. Learners conducted 2 communicative tasks with a native‐speaker instructor and received feedback on their errors. They were pretested and posttested (immediately and after 2 weeks) using 3 different outcome measures: an oral picture description task, a written grammaticality judgment task, and a written storytelling task. The results revealed that the extensive recast group significantly outperformed the control group on the oral picture description and the grammaticality judgment tasks, whereas the intensive recast group did not. On the written storytelling task, both recast groups outperformed the control group, but the difference was not statistically significant. These findings point to the advantage of extensive recasts and challenge the assumption that recasts on single errors are necessarily more effective than recasts on a wide range of errors.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".