Corrective Feedback and the Development of Second Language Grammar
Bibliographic record
Abstract
This chapter reviews themes in research into the effectiveness of oral corrective feedback, typically provided by language teachers, on L2 grammatical development. It synthesizes research evidence for the effects of oral corrective feedback on learners’ development of grammar and the relative efficacy of different corrective feedback strategies, such as output-prompting and input-providing. Further themes concern the effectiveness of oral corrective feedback on salient and non-salient grammatical features and in relation to learners’ varying levels of knowledge of the targeted features. Even though most research in this area concerns the development of accuracy, the chapter includes a review of the considerably smaller body of literature that offers insights into the potential value of oral corrective feedback on the development of fluency. The chapter reviews the different kinds of oral and written tests that have been used in research to gauge grammar learning, some of which teachers may wish to consider adopting to assess their learners. Based on the cumulative evidence from research, we make suggestions for classroom teachers, although we recognize that teachers’ decisions about the provision of oral corrective feedback are often based on multiple factors, including affective factors and teaching objectives.
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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 | 0.001 | 0.004 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".