Corrective Feedback in Instructional Pragmatics
Bibliographic record
Abstract
This chapter has two broad aims: to explore the potential for a role for corrective feedback in instructional pragmatics; and to review studies of instructional pragmatics that have investigated the effectiveness of corrective feedback. The chapter starts with the observation that there has been a disinclination to correct learners’ pragmatic errors. In fact, studies of instructional pragmatics rarely refer to “errors,” which is a construct integral to feedback studies. Allowing for this difference in orientation, the chapter discusses potential issues related to correcting pragmatic errors, such as challenges in identifying errors, the feasibility of correcting pragmalinguistic versus sociopragmatic errors, and the lack of firm norms to use in correction. Next, the chapter summarizes the findings of nine studies published between 2005 and 2017 and assesses their methodological strengths and weaknesses. The review revealed that although most of the studies reported positive effects for corrective feedback, many of the studies reviewed suffered from major methodological limitations. Owing to the nature of the available evidence, the chapter advocates neither for nor against the implementation of corrective feedback in instructional pragmatics. The chapter concludes by providing guidelines for future principled investigations into the role of corrective feedback in instructional pragmatics.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".