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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".