Bibliographic record
Abstract
This study examines the preferences and perceptions of Saudi EFL learners concerning the use of Corrective Feedback (CF) during speaking activities. The participants consisted of sixty EFL pre-intermediate female learners in their preparatory year at the English Language Institute (ELI) in King Abdulaziz University in Saudi Arabia. The study utilized both quantitative and qualitative approaches, including a questionnaire to establish learners’ CF preferences when it came to the correction of errors during speaking activities, followed by interviews with ten learners to establish additional information on, and the reasons for, these preferences. The findings revealed that the students held a positive attitude to CF during speaking activities, strongly agreeing that their teachers’ CF could improve their speaking skills. The study also found that students preferred CF to be immediate and to be given by their teachers, who they considered the most qualified to provide such feedback. In addition, the majority of respondents favored receiving CF on their oral grammatical errors. This study provides beneficial information concerning students’ preferences towards the use of CF during speaking activities. This has the potential to contribute to EFL classroom practice, enabling teachers to reevaluate their instruction, particularly in relation to speaking skills, in order to improve speaking proficiency. Moreover, these results contribute to the literature focusing on EFL learners’ preferences when it comes to the use of the CF in English speaking classes in Saudi Arabia.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".