The Influence of English Pronunciation System on Spelling Errors Among Saudi Students
Bibliographic record
Abstract
This paper discusses how English pronunciation system causes spelling errors among undergraduate Saudi students. The research participants were five students in English Language Department at Tabuk University and five English language lecturers from the same department. Semi-structured interviews designed for the lecturers and students, as well writing task distributed to the students were used to generate data for the study. The findings reveal that phonological differences between English and Arabic cause difficulty in the learners’ spelling. The students strive to write words in a similar way to how they are pronounced as they are unaware of the rules of English pronunciation which is totally different from their mother-tongue. The confusion with English pronunciation system causes several spelling errors, such as the errors related to final [e], vowels, silent letters and double consonants. This paper concludes that the Arab students need to be familiarized with English pronunciation system and they need to be made aware that English words are not necessarily spelt as how they are pronounced, which is in contrast to their mother tongue.
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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 | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".