The role of extensive recasts in error detection and correction by adult ESL students
Bibliographic record
Abstract
Most of the laboratory studies on recasts have examined the role of intensive recasts provided repeatedly on the same target structure. This is different from the original definition of recasts as the reformulation of learner errors as they occur naturally and spontaneously in the course of communicative interaction. Using a within-group research design and a new testing methodology (video-based stimulated correction posttest), this laboratory study examined whether extensive and spontaneous recasts provided during small-group work were beneficial to adult L2 learners. Participants were 26 ESL learners, who were divided into seven small groups (3-5 students per group), and each group participated in an oral activity with a teacher. During the activity, the students received incidental and extensive recasts to half of their errors; the other half of their errors received no feedback. Students’ ability to detect and correct their errors in the three types of episodes was assessed using two types of tests: a stimulated correction test (a video-based computer test) and a written test. Students’ reaction time on the error detection portion of the stimulated correction task was also measured. The results showed that students were able to detect more errors in error+recast (error followed by the provision of a recast) episodes than in error-recast (error and no recast provided) episodes (though this difference did not reach statistical significance). They were also able to successfully and partially successfully correct more errors in error+recast episodes than in error-recast episodes, and this difference was statistically significant on the written test. The reaction time results also point towards a benefit from recasts, as students were able to complete the task (slightly) more quickly for error+recast episodes than for error-recast episodes.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".