An Investigation of Writing Errors Made by Saudi English-Major Students
Bibliographic record
Abstract
The present study aims to explore writing errors made by Saudi foreign language learners of English. The study seeks to address the following questions: a) to what extent do Saudi English-major students face difficulties in English writing; and b) what types of errors do Saudi English-major students make in their writing. Addressing these will facilitate an examination of the role of the first language and the difficult nature of writing in English. Twenty-four male English-major students attending a Saudi university participated in the study (aged 19–22 years). The participants wrote 48 compositions over a two-week period, from which the data for this study were collected. These written compositions were analysed manually by the researcher. The findings of the study reveal that: a) the targeted participants had difficulty in writing accurately in English, given the high rate of errors they made; and b) capitalisation, spelling and use of articles were the top three types of errors accounting for around 50 per cent of overall errors. The writing difficulties that students face require Saudi universities to revise their writing courses materials and teaching approaches.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".