Enhancing the Quality of EAP Writing through Overt Teaching
Bibliographic record
Abstract
This paper examines how overt teaching is instrumental in reducing subject-verb agreement (SVA) errors of Malaysian EAP learners which in turn improves the quality of their writing. The researchers used overt teaching of these grammatical items, that is, SVA and investigated how this method has significantly benefitted the learners who were second year university students from different cultural and language backgrounds. Data was collected using a pre-test and a post-test. Even though the learners had spent more than a decade learning the English language since their early education, the data collected in the pre-test showed that they made gross SVA errors in their writing. Treatment in the form of overt teaching of SVA was given to the learners, after which the post-test was administered. The comparison of data of the two tests revealed significant improvements in the learners’ usage of SVA which resulted in improved quality of their writing. The major findings on the learners’ grammatical problems especially in SVA and their response to overt teaching prove that overt teaching enhances the quality of EAP writing produced by students.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".