The Impact of Discourse Marker Instruction on Fluency, Accuracy, and Complexity Improvement of Iranian Intermediate EFL Learners
Bibliographic record
Abstract
The purpose of the study was to investigate the impact of discourse marker (DM) instruction on fluency, accuracy, and complexity improvement of Iranian intermediate EFL learner’s writing. To this aim, among the two hundred forth year English major learners in Dezful university, Iran, fifty of them who were in the intermediate level, based on the scoring system of the university, were recruited. They were given a topic to write before intervention. Then, the fifty participants passed through twenty-hour instruction on micro and macro DMs, based on Belles-Furtuno’s (2004) classification of discourse markers. The mentioned classification included both sentential and supra sentential markers. In the process of explicit instruction (EI) of DMs, they were given various exercises and activities to apply DMs and learn the function and usage of these units and input flood (IF) was performed along with corrective feedback (CF) with the help of the teacher with their mistakes and misunderstandings of DMs. After intervention, they were given another topic to write to examine if EI+IF of DMs could help them improve fluency, accuracy, and complexity of their writing. To quantify the results the Wolfe-Quintero (1998) method was used and it was unveiled that all the three components of writing improved after intervention, which practically means instruction of DMs could enhance learner’s writing in the three aspects. The findings can be used by teachers and syllabus designers to consider DMs as one of the most crucial components in writing courses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".