Correcting students’ written grammatical errors: The effects of negotiated versus nonnegotiated feedback
Bibliographic record
Abstract
A substantial number of studies have examined the effects of grammar correction on second language (L2) written errors. However, most of the existing research has involved unidirectional written feedback. This classroom-based study examined the effects of oral negotiation in addressing L2 written errors. Data were collected in two intermediate adult English as a second language classes. Three types of feedback were compared: nonnegotiated direct reformulation, feedback with limited negotiation (i.e., prompt + reformulation) and feedback with negotiation. The linguistic targets chosen were the two most common grammatical errors in English: articles and prepositions. The effects of feedback were measured by means of learner-specific error identification/correction tasks administered three days, and again ten days, after the treatment. The results showed an overall advantage for feedback that involved negotiation. However, a comparison of data per error types showed that the differential effects of feedback types were mainly apparent for article errors rather than preposition errors. These results suggest that while negotiated feedback may play an important role in addressing L2 written errors, the degree of its effects may differ for different linguistic targets.
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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 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.003 | 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".