The Effect of Textual Metafunction on the Iranian EFL Learners’ Writing Performance
Bibliographic record
Abstract
This study is devoted to the effect of ‘textual metafunction’ on the levels of coherence and cohesion in the Iranian EFL learners’ English writing performance. Sixty Iranian intermediate EFL learners who were adult females participated in this study were randomly divided into two groups; experimental, and control. They were given a writing pre-test. Then both groups’ subjects attended an essay writing class, two sessions per week, for a ten-week term; however, while the experimental group was taught how to write a standard three-paragraph essay in English, and apply the textual metafunction in it, the control group was only taught how to write a standard three-paragraph essay. After the completion of the instructional period, both groups were given a writing post-test in which they were asked to write a standard three-paragraph essay on a subject. The analytic scoring scale of ‘Hungarian School-Leaving English Examination Reform’ (2001, as cited in Tankó, 2001) was employed by three independent raters for rating the writing samples. A ‘t-test’ on the mean scores of both groups indicated a significant difference between the scores of the post-tests, meaning that the textual metafunction was significantly effective in the experimental group’s writing task. Moreover, while the mean scores of the control group’s pre-post tests were the same, the mean score of the experimental group’s post-test was higher than that of the pre-test, meaning that textual metafunction increased the levels of cohesion and coherence in their writing task.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".