The Effects of Direct Written Corrective Feedback on Improvement of Grammatical Accuracy of High- proficient L2 Learners
Bibliographic record
Abstract
The present article reports the findings of a study that explored(1) whether direct written corrective feedback (CF) can help high-proficient L2 learners, who has already achieved a rather high level of accuracy in English, improve in the accurate use of two functions of English articles (the use of ‘a’ for first mention and ‘the’ for subsequent or anaphoric mentions); and (2) whether there are any differential effects in providing the two different types of direct written CF (focused and unfocused) on the accurate use of these grammatical forms by these EFL learners. In this study, sixty high-proficient L2 learners formed a control group and two experimental groups. One experimental group received focused written CF and the other experimental group received unfocused written CF, while the control group received no feedback. The statistical analyses indicated that both experimental groups did better than control group in the post-test, and moreover, focused group significantly outperformed unfocused one in terms of accurate use of definite and indefinite English articles. Overall, these results suggest that focused written CF is more effective than unfocused one, at least where English articles are concerned, in improving grammatical accuracy of high-proficient L2 writers and thus strengthens the case for teachers providing focused written CF.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".