The Role of Individual Preferences in the Efficacy of Written Corrective Feedback in an English for Academic Purposes Writing Course
Bibliographic record
Abstract
This study examined the effectiveness of written corrective and the role of individual differences (ID) in the uptake of the feedback. Data was taken from a nine-week, English as a foreign language (EFL) writing course from 101 intermediate (n=101) students at a private university in Kobe, Japan. Using an explanatory sequential mixed methods design, quantitative data was first collected concerning writing errors, followed by qualitative semi-structured interviews. Three classes were placed into either two treatment groups (direct and indirect) or a control group, and completed four writing tasks (pre-test, post-test and two delayed post-tests). The study found the two treatment groups showed significant improvements on local and global errors, whereas the control group did not. Additionally, the qualitative component elicited the influence of affective factors. The study adds to the body of literature addressing the impact of written corrective feedback, specifically on students’ self-editing strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".