Understanding Written Corrective Feedback in Second-Language Grammar Acquisition
Bibliographic record
Abstract
Written Corrective Feedback (WCF) is used extensively in second-language (L2) writing classrooms despite controversy over its effectiveness. This study examines indirect WCF, an instructional procedure that flags L2 students’ errors with editing symbols that guide their corrections. WCF practitioners assume that this guidance will lead to increased grammatical competence over time in new writing samples. This study finds that these assumptions are correct overall. However, in-depth analyses of L2-English learners’ correction behaviors in four elicitation tasks over a 12-week period demonstrate that WCF is not uniformly effective at increasing accuracy for all grammatical constructions. In fact, WCF fails to exert any positive effect with a number of grammatical constructions. This result can be understood via Skill Acquisition Theory (SAT) when the treatability of constructions with WCF is considered. Specifically, grammatical constructions that include only a binary option for correct usage are highly amenable to positive change via WCF since employing WCF is akin to correcting errors flagged on a true/false test. By contrast, grammatical constructions with more than a binary choice for correct usage, akin to correcting errors flagged on a multiple-choice test, are not amenable to positive change.
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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 | 0.003 | 0.039 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".