Oral Versus Written Feedback: Attitudes of Female Saudi University Students
Bibliographic record
Abstract
Oral and written feedback have been found to be useful in learning English as a Second Language (L2). Yet it is not clear what form of feedback L2 learners prefer. This study therefore investigated 47 Saudi female university students’ attitudes to both oral and written feedback. The data was collected by an online questionnaire consisting of three constructs: a) attitudes to written feedback; b) attitudes to oral feedback; and c) attitudes to written versus oral feedback. In terms of the first, the results showed that most participants expressed positive attitudes to written feedback, which they would be happy to receive on all the mistakes they make in their writing. Most participants were also positive about oral feedback and wanted their teacher to correct all their speaking errors, including errors of grammar, pronunciation and vocabulary. However, participants did not want to be corrected in front of other students as this could make them nervous. Overall, most participants agreed that oral feedback helped them improve their English skills more than written feedback. But despite such a positive attitude, most participants still found oral feedback embarrassing. Knowing students’ perceptions of corrective feedback (CF) is vital, because negative attitudes to feedback could harm the language learning process.
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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 | 0.001 | 0.005 |
| 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) | 0.003 | 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".