The Effect of Metadiscourse Awareness on L2 Reading Comprehension: A Case of Iranian EFL Learners
Bibliographic record
Abstract
This research attempts to investigate the degree of students’ achievement in reading comprehension in English as a foreign language through explicit instruction in metadiscourse markers. For this purpose, 80 students studying at a language school (placed at intermediate levels of English language proficiency) were chosen to participate in the experiment. Every possible measure was taken to ensure that the participants lacked enough knowledge about metadiscourse. The participants were then randomly divided into four equal groups each containing twenty students. The first experimental group (EG1) included twenty participants who received instructions in both textual and interpersonal metadiscourse. The second experimental group (EG2) received instructions in only textual metadiscourse markers. The third experimental group (EG3) received instructions in only interpersonal metadiscourse. The control group received no specific instructions in metadiscourse and was only exposed to some input enhancement material and relevant exercises.As a result of running a number of statistical procedures, instruction on metadiscourse revealed a positive effect on the participants’ achievement in reading comprehension in English. The results of the study have some implications for materials designers; they can improve a learner’s ability to understand and remember information from the text by improving it textually and interpersonally.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".