Students’ Preferences of Oral Corrective Feedback: Traditional vs. Online Learning
Bibliographic record
Abstract
Previous studies explored teachers’ perceptions regarding different types of oral corrective feedback (OCF) (see e.g. Alshammari & Wicaksono, 2022). They found some similarities and dissimilarities between instructors’ views and their actual choices and practices regarding OCF, with one of the key findings being that recast was the most commonly used, mainly because teachers considered it very effective for their learners’ education (Alshammari & Wicaksono, 2022). This was not in line with most previous research, which found that recast was the predominant oral correction form employed, even though it was considered the least effective. It was mainly used to keep the smoothness of interaction or to prevent the arousal of negative feelings. However, there is a lack of studies investigating learners’ preferences regarding OCF. Therefore, the current research examines learners’ attitudes towards various oral correction strategies, considering the possible influence of multiple variables such as the context, and specifically comparing online vs. traditional learning English language classes.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".