Common Writing Errors Among EFL Students at Dhofar University in Oman: An Analytical Study
Bibliographic record
Abstract
The present study aims to analyze the common writing errors made by EFL students at Dhofar University in the Sultanate of Oman. The study included 93 first-year students enrolled in a university requirement course. The students’ written English essays were collected to carry out the analysis. A number of errors in the students’ essays are identified and classified into various types. The results of the analysis of the students’ writing samples show that the common errors of EFL students at Dhofar University are basically related to spelling and grammar. Spelling and grammatical errors are classified into different types, with a frequency count for each type of error. Grammatical errors account for the biggest number of errors which are distributed on eight different types. These types are listed in order based on their frequency as follows: (1) verb tense and form, (2) plurality (3) subject-verb agreement (4) prepositions (5) part-of-speech (6) word order (7) articles (8) adjective form. Spelling errors, on the other hand, are classified into four types which are listed in order as follows: (1) omission (2) substitution (3) insertion (4) transposition. Based on these results, a number of recommendations for treatment of writing errors are suggested.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".