‘Tutoring Is Not Proofreading’. Exploring the Perceptions of Writing Tutors at University Writing Centres, Saudi Arabia: An Exploratory Study
Bibliographic record
Abstract
Academic discourse is highly complex and requires writers to follow specific writing conventions. Many Saudi university students have underdeveloped writing skills (Al-Khairy, 2013). One way to assist second language (L2) learners and develop their academic writing skills is through academic language support offered by writing centres. The challenge for writing centre tutors lies in the predominant belief among many L2 students that tutors’ only role is to fix students’ mistakes. Although there has been significant growth in writing centres in Saudi universities, the perceptions of writing tutors concerning tutoring non-native students are still under-researched. This study uses thematic analysis to explore the role of writing tutorials as perceived by writing centre tutors in Saudi settings. Data were obtained using an interpretive inquiry through individual interviews of two tutors. The main findings of the interviews were that tutors perceived proofreading requests, low writing proficiency of tutees and tutees’ understanding of tutors’ role as influencing their tutorial practices. The implementation of this study may help regulate the role of tutors in writing centres in Saudi universities by highlighting new avenues that can improve writing tutorials, especially 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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".