Exploring the Use of Discourse Markers in EFL Students’ Writing through Google Docs
Bibliographic record
Abstract
This study aimed to investigate the discourse markers (DMs) used in EFL undergraduates’ writing through Google Docs. It tends to find out whether there are any significant differences in DMs used in the narrative, descriptive, and process essays, and the significant issues arising from the EFL undergraduates’ use of DMs in essay writing. It adopted a qualitative case study to obtain data from 36 narrative, descriptive, and process essays written by 12 pairs of EFL undergraduates. The DMs in written essays are investigated, categorized, and analyzed according to Fraser’s (1988) model of message relationship markers. The findings indicate that (1) there is no relationship between the number of words within the essays and the number of message relationship markers, (2) the EFL undergraduates use the highest number of parallel DMs in the three essays: narrative, descriptive, and process essays, followed by contrastive, inferential, and elaborative DMs, respectively. The thematic analysis of the EFL undergraduates’ written essays (3) showed a range of significant issues such as the overuse of DMs, EFL undergraduates’ misuse and ignorance of DMs, and the multiple uses of DMs. Finally, the study presents pedagogical implications for writing instructors in increasing awareness of EFL undergraduates of DMs, including their varied types, functions, and proper uses in writing courses
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".