The Practice of Teachers’ Written Corrective Feedback as Perceived by EFL Teachers and Supervisors
Bibliographic record
Abstract
This study investigates EFL Post-Basic education teachers’ and EFL supervisors’ perceptions toward the importance of providing WCF and the practice of WCF including the techniques, focus, and follow-up methods. It attempts to examine the differences between teachers’ and supervisors’ perceptions on teachers’ WCF practices. The data was collected from 156 EFL teachers who were teaching Post-Basic education grades (11-12) and 62 EFL supervisors through using an online questionnaire. The two participant groups (teachers and supervisors) were randomly selected from three governorates in Oman: Muscat, Al Batinah South, and Sharqia North. The study findings reveal that both teachers and supervisors valued the importance of providing WCF on writing errors. The researcher found that EFL Post Basic education teachers commonly used unfocused indirect coded WCF technique. They mainly focused on forms, particularly the grammatical errors. It was also found that they often used one-draft approach after providing WCF. The supervisors’ responses showed that they had similar views on these practices. Thus, there were no statistically significant differences between the perceptions of the two groups regarding teachers’ WCF practices.
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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 | 0.006 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".