Discourse markers in academic and non-academic writings of Thai EFL learners
Bibliographic record
Abstract
The ability to use discourse markers (DMs) to create cohesion and coherence of a text is essential for EFL learners at the university level to express ideas and thoughts in various types of writing assignments, such as academic papers and reflections. Hence, this study attempted to shed more light on the use of DMs in academic and non-academic writings of Thai EFL learners. The main objective was to investigate the types, overall frequency, and differences, and similarities of discourse markers in both styles of writing. Sixty essays, consisting of 20 academic essays and 40 non-academic ones, were selected as the primary data. Academic essays were selected from the Critical Reading and Writing course of Xavier Learning Community (XLC), Thailand, while the non-academic ones were selected from the XLC English Newsletter. The data were analyzed based on Fraser’s taxonomy (2009). The results showed that 2.521 DMs distributed in five types, namely contrastive discourse, elaborative discourse, inferential discourse, temporal discourse, and spoken discourse markers, were identified in the 20 academic and 40 non-academic essays. The most frequently used DM was elaborative discourse markers (EDM), F=1,703. This study concluded that raising awareness of DMs would assist Thai EFL learners in producing an effective and coherent piece of 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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".