Cohesive Errors in Writing among ESL Pre-Service Teachers
Bibliographic record
Abstract
Writing is a complex skill and one of the most difficult to master. A teacher’s weak writing skills may negatively influence their students. Therefore, reinforcing teacher education by first determining pre-service teachers’ writing weaknesses is imperative. This mixed-methods error analysis study aims to examine the cohesive errors in the writing of English as a Second Language (ESL) pre-service teachers of differing language proficiency levels—Medium and High-level, as indicated by the band levels achieved in the Malaysian University English Test (MUET). 200-word narrative essays were collected from 30 final-year ESL pre-service teachers from UKM via email. The study found that the Medium-level pre-service teachers made the most errors in lexical cohesion, reference and conjunction cohesion categories. However, High-level pre-service teachers made more errors in lexical cohesion, ellipsis and reference. Collocation proved the most difficult form of cohesion for both groups of pre-service teachers, while High-level pre-service teachers made more errors in ellipsis than the Medium-level pre-service teachers. Nevertheless, this still indicates that the pre-service teachers’ overall mastery of cohesive writing is insufficient. Therefore, the teaching of these cohesive devices should be further fortified in the linguistic courses undertaken by ESL pre-service teachers to ensure that they are well-equipped in all aspects of cohesive writing.
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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 | 0.004 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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".