Preferred Methods of Providing Correction to EFL Students: A Case Study of Saudi Universities
Bibliographic record
Abstract
The basic objective of English language instruction is to help students achieve language competency for communicative purposes while making as few errors as feasible. Corrective feedback (CF) is essential because of how well it improves students' English language skills. The connection between language learners' and teachers' views on the forms, methods, and timing of CF has not yet received the attention it deserves from educational researchers. By examining the perspectives of both teachers and students in higher education, this study seeks to better understand the effects of constructive criticism. Three hundred sixty university sophomores took part in the mixed-method study. The data from the surveys, the students' follow-up interviews, and the discussion with ten EFL instructors were analyzed to reach several findings. The findings demonstrated that both educators and students valued the use of remedial feedback delivered verbally to improve English language skills. Metalinguistic feedback, immediate feedback on grammatical and lexical mistakes, and explicit correction and recast were all highlighted by the author as effective treatments for phonological problems. Teachers thought that students were not negatively affected by timely correction, but they did see that students preferred delayed corrective input. From a pedagogically relevant stance, these results have consequences for language teachers and students alike.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| 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".