Oral corrective feedback in second language classrooms
Bibliographic record
Abstract
This article reviews research on oral corrective feedback (CF) in second language (L2) classrooms. Various types of oral CF are first identified, and the results of research revealing CF frequency across instructional contexts are presented. Research on CF preferences is then reviewed, revealing a tendency for learners to prefer receiving CF more than teachers feel they should provide it. Next, theoretical perspectives in support of CF are presented and some contentious issues addressed related to the role of learner uptake, the role of instruction, and the overall purpose of CF: to initiate the acquisition of new knowledge or to consolidate already acquired knowledge. A brief review of laboratory studies assessing the effects of recasts is then presented before we focus on classroom studies assessing the effects of different types of CF. Many variables mediate CF effectiveness: of these, we discuss linguistic targets and learners' age in terms of both previous and prospective research. Finally, CF provided by learners and the potential benefits of strategy training for strengthening the role of CF during peer interaction are highlighted.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".