Grammatical Errors Found in English Writing: A Study from Al-Hussein Bin Talal University
Bibliographic record
Abstract
This study investigated the frequent grammatical errors, found in the writings of Arab students’ taking English writing courses in AL-Hussein Bin Talal University Learners’ errors were considered positively as the best sources to identify students’ limitations in English writing. Therefore the present study intended to investigate the grammatical errors of Arab students’ writings in English in AL-Hussein Bin Talal University and to see if there are any differences in the grammatical errors according to year of study. To conduct this study data was collected from the writing sessions of writing classes that were taught during the fall semester of 2019. The data was collected, analyzed and categorized from students, all majoring in English Language and Literature and ranging from freshman to seniors. A Grammar test Questionnaire designed by the researchers was distributed to the students in these writing sessions. The results showed that the most frequent grammatical error was with the verb tense on a mean of (3.75), followed by errors in the article on a mean of (3.62), wrong word order on a mean of (3.57), noun ending on a mean of (3.40) and least was sentence structure on a mean of(3.39). The results also showed that the seniors on the grammar test on all its parts did better than the freshmen, juniors and sophomores that are the least problems were found among the seniors.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".