Saudi EFL Learners’ Preferences of the Corrective Feedback on Written Assignment
Bibliographic record
Abstract
The aim of the current study is to investigate the Saudi English as a foreign language (EFL) learners’ preferences for corrective feedback on written assignments. This mixed-method study used a closed-ended Likert scale questionnaire that was adopted and adapted to suit the participants under investigation. Additionally, an open-ended question was used to gain more insight. Both instruments were completed by 114 Saudi female EFL learners whose ages ranged from 12 to 13 years old and who were studying in the seventh grade at a private school in Jeddah. The instruments were given to the learners after 6 weeks of implementing three different types of feedback on written assignments. The quantitative part of the study was descriptively analysed using SPSS to find the learners’ preferences in corrective feedback, and a one-way ANOVA was used to find the differences between learners’ preferences among groups. The qualitative part of the study was thematically categorised and manually analysed using Excel. The findings revealed that the learners’ preferences did not vary according to the type of corrective feedback. However, the vast majority of learners preferred having constructive feedback on how to correct their mistakes. Additionally, learners preferred the use of electronic devices to receive corrective feedback. This study suggests that teachers consider learners’ preferences on corrective feedback so that they can incorporate these into their teaching plans.
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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 | 0.003 | 0.011 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".