English Grammatical Errors of Students in the Universities and Schools in Yemen: An Analysis
Bibliographic record
Abstract
It is a well-known fact that, English is a global language and assumes great importance all over the world. No doubt, one of the useful and important areas of teaching and learning the English language is Applied Linguistics. Hence, in this paper, I have made a humble attempt to analyze mistakes and errors in English grammatical system made by learners of the second language in general and particularly students in Yemen. Keeping this in view, the number of universities and schools in Yemen introduced English in their syllabus as a compulsory subject and many teachers and students have been leaving no stone unturned to overcome various difficulties in Grammatical System to teach and learn correct English. Hence, the main focus of this paper to highlight the importance of analysis of mistakes and errors in English Grammatical System made by students in Yemen especially at I & IV levels. Moreover, concepts such as ‘Interlingual Errors’, ‘Intralingual Errors’, ‘Mother Tongue Interference’ etc have been discussed. The paper also makes a brief mention of some models to analyze errors and a few sources of errors that are common among linguists. A reference to the constructive analysis of the English Grammatical System and that of Arabic has been made while pointing out similarities and dissimilarities. Finally, this paper includes some findings, suggestions, conclusions, and pedagogical implications.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".