Learning Through Correction: Oral Corrective Feedback in Online EFL Interactions
Bibliographic record
Abstract
Feedback has been vital and essential in all educational settings and English as a Foreign Language (EFL) classroom interaction draws no distinction. It contributes to students' language learning and achievement. It, when involves learners during classroom interactions, becomes oral corrective feedback and has been viewed as a dynamic practice for EFL teachers to correct their learners’ mistakes on the spot. This study investigated teachers' oral corrective feedback practices in an online EFL classroom interactions context. Furthermore, it correlated teachers' responses with the type of oral corrective feedback, gender, and years of experience. To achieve the study objectives, the descriptive-correlational method was used. A questionnaire and a semi-structured interview were applied to a sample of 61 EFL teachers. The results of the questionnaire revealed that EFL teachers consistently practiced oral corrective feedback in online classroom interactions. Also, no significant differences were shown in the study sample's responses based on the type of oral corrective feedback, implicit or explicit, gender, and years of teaching experience. Considering the study findings, some implications and recommendations are suggested.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".