Perceptions on Oral Corrective Feedback: The Case of Iranian EFL Teachers and Students in Face-to-Face and Virtual Learning Contexts
Bibliographic record
Abstract
Oral-corrective feedback (CF) has often been a significant concern in Teaching English as a Foreign Language (TEFL). This study sought to investigate teachers’ and students' attitudes toward the oral CF in traditional and technology-enhanced classes. It also investigated the extent to which teachers' attitudes toward the oral CF matched their practices. A mixed-methods design was used for the study, utilizing data from questionnaires, observations, semi-structured interviews, and focus-group discussions. A sample of 162 female Iranian EFL students studying English at a private school participated in the study. The results showed that explicit correction (26%) and metalinguistic feedback (32%) were rated much more positively by the majority of students. Furthermore, the results indicated that they were more accustomed to receiving oral-feedback from the teacher in face-to-face classes than text- or audio-based feedback in technology-enhanced lessons. In addition, teachers' attitudes toward the CF were categorized into four themes: students' affective responses to CF, reasons for providing CF, timing of CF, CF in face-to-face instruction, and technology-enhanced instruction. The findings also showed that teachers' expressed beliefs about the frequency of CF provision predicted their practices, in many cases. This research has implications for EFL teachers and materials developers.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".