Corrective feedback and learner uptake in communicative classrooms across instructional settings
Bibliographic record
Abstract
This paper reports similarities and differences in teachers’ corrective feedback and learners’ uptake across instructional settings. Four communicative classroom settings - French Immersion, Canada ESL, New Zealand ESL and Korean EFL - were examined using Lyster and Ranta’s taxonomy of teachers’ corrective feedback moves and learner uptake. The results indicate that recasts were the most frequent feedback type in all four contexts but were much more frequent in the Korean EFL and New Zealand ESL classrooms (83% and 68%, respectively) than in the Canadian Immersion and ESL classrooms (55% for both). Also, the rates for both uptake and repair following recasts were greater in the New Zealand and Korean settings than in the Canadian contexts. The findings of this study suggest that the extent to which recasts lead to learner uptake and repair may be greater in contexts where the focus of the recasts is more salient, as with reduced/partial recasts, and where students are oriented to attending to linguistic form rather than meaning. The study underscores the importance of considering the influence of context on corrective feedback and learner uptake.
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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 | 0.007 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".