The Effects of Corrective Feedback on Chinese Learners’ Writing Accuracy: A Quantitative Analysis in an EFL Context
Bibliographic record
Abstract
Scholars debate whether corrective feedback contributes to improving L2 learners’ grammatical accuracy in writingperformance. Some researchers take a stance on the ineffectiveness of corrective feedback based on theimpracticality of providing detailed corrective feedback for all L2 learners and detached grammar instruction inlanguage classrooms. On the other hand, many researchers promote the efficacy and significance of the role playedby corrective feedback in the process of L2 writing. This research employs a quasi-experimental design andexamines two major issues: (1) the extent to which CF facilitates or improves students’ writing accuracy; (2) students’expectations and preferences for CF. The research consists of 105 college level EFL learners from three intact classesin an Eastern Chinese University. One class was assigned to the control group which only received comments oncontent of their writing. The other two classes were then assigned to each of the two experimental groups whichreceived indirect or direct CF. Data collection includes student text/error analysis, treatments (i.e., provision ofcorrective feedback), examination of tests (i.e., pretest, posttest and delayed posttest), and questionnaires. Within aresearch period of ten weeks, this study did not reveal statistically significant group differences between the two CFgroups and the control group on overall error reduction. However, students believed CF was important and beneficial,although there is contradiction between what the students believed and their teachers’ actual practices in theclassroom. Pedagogical recommendations for EFL teachers are also discussed.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".