Use of Discourse Markers in the Composition Writings of Arab EFL Learners
Bibliographic record
Abstract
The purpose of this paper is to investigate the use of discourse markers that Yemeni EFL learners use in their composition writings. The research questions addressed in this paper are (1) what are the DMs that are frequently used by Yemeni EFL learners? , and (2) is there a direct relationship between the use of such markers and the writing quality of the learners in question? The 50 essays written by the study sample were analyzed following Fraser's (1999) taxonomy. The findings of the study reveal that the most frequently used discourse markers are the elaborative ones, followed by the inferential, contrastive, causative and topic relating markers. It is also shown that there is no strong positive correlation between learners' total number of discourse markers used and the writing quality of the participants. There is, however, a positive correlation between the topic relating markers and the writing quality of the learners. The paper concludes with some recommendations and suggestions that should inform EFL writing instruction in this part of the world and in other similar contexts.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".