Students’ Preferences and Attitude toward Oral Error Correction Techniques at Yanbu University College, Saudi Arabia
Bibliographic record
Abstract
Error correction has been one of the core areas in the field of English language teaching. It is “seen as a form of feedback given to learners on their language use” (Amara, 2015). Many studies investigated the use of different techniques to correct students’ oral errors. However, only a few focused on students’ preferences and attitude toward oral error correction techniques, which determine students’ success in language learning. This quantitative research explored teachers’ and students’ preferences as well as students’ attitude toward the use of oral error correction techniques in the language classroom. The participants of the study were English language students and English language teachers at Yanbu University College (YUC) in Yanbu Industrial City, Saudi Arabia. A classroom observation checklist and questionnaires were used to collect the data. The study findings revealed that recast and explicit correction are the preferred techniques by the majority of the students and teachers. The findings also indicated that students have positive attitude toward oral error correction. As the classroom observation revealed that recast was highly used by teachers, it is recommended that teachers should also use other techniques to correct students’ oral errors. In addition, it is recommended that before correcting students’ oral errors teachers should always take into account the purpose of the activity and the proficiency level of students.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".