English Writing Errors Committed by Saudi Students: A Study of Two Female University Groups
Bibliographic record
Abstract
The main aim of this study is to highlight the writing errors made by Saudi students majoring in English. The study selected a sample of two groups of female Saudi students residing in two Saudi regions: Tabuk and Hafr Al Batin. The students were requested to write approximately three to four paragraphs about one of three topics related to Saudi Arabia: social media and its effects on Saudi social life, marriage customs in Saudi Arabia, or the economy of Saudi Arabia. In analyzing the collected writing data, the students’ writing errors were identified and classified into four categories: grammar type, syntax type, mechanics type, and lexical type errors. Then, the frequency and error percentages of each subcategory were calculated for both groups. The findings show that both groups produced most errors in the subcategory of spelling followed by tenses subcategory even though the students from the University of Hafr Al Batin made overall higher percentages of errors than the errors’ percentages made by the students from the University of Tabuk. Further investigation reveals that all students in both regions hardly practice English writing and that Arabic interference contributes to the students’ English writing errors. The findings also imply that the curricula specialists at the Saudi ministry of education might consider including more educational material to improve the English writing skills of Saudi university students.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| 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".