Grammar Errors Made by ESL Tertiary Students in Writing
Bibliographic record
Abstract
The educational context in Malaysia demands students to be equipped with sound grammar so that they can produce good essays in the examination. However, despite having learnt English in primary and secondary schools, students in the higher learning institutions tend to make some grammatical errors in their writing. This study presents the grammatical errors made by tertiary students in their writing. The participants were a group of Diploma students who sat for a university entrance exam. One hundred and forty-four written essays of the students were collected and analysed using content analysis. Findings revealed that subject-verb agreement and tenses were the most common type of errors. Students over-generalised and perceived that the tenses could be used interchangeably. Another common error found was in the students’ construction of complex sentence. In such constructions, they failed to include essential and nonessential clauses. If teachers do not teach strategies to assist students in comprehending the concept of Subject-Verb Agreement (SVA), tenses, essential and nonessential clauses, these students will continue to make such errors in their tertiary education. The findings may have useful implications for English language teachers as understanding students’ learning difficulties and providing appropriate grammar instruction is the key to effective teaching for ESL teachers.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".