The Evaluation of Iranian EFL Learners’ Interlanguage Pragmatic Knowledge through the Production of Speech Acts
Bibliographic record
Abstract
This study evaluated Iranian intermediate EFL learners’ knowledge of interlanguage pragmatic learning outcomes through the production of the speech acts of apology, request, and refusal. The study drew upon the conceptualization of Austin (1962) speech act theory and Brown and Levinson (1987) politeness theory as the theoretical framework of this study. The participants of the study included 235 EFL intermediate learners. Discourse Completion Task (DCT) was used as the instrument for data collection. The results of the data from the tests on speech acts showed that learners utilized more conventional or routinized strategies to perform these speech acts. Moreover, the results revealed that there were not so much differences in the frequency, shift and type of speech acts strategies or semantic formulas utilized in the production of speech acts by Iranian EFL learners in responding to a higher, an equal, and a lower-status person. The results also were suggestive of the learners’ lack of pragmalinguistic and sociopragmatic knowledge. The implication of this study is for language teachers to teach interlanguage pragmatics explicitly in EFL contexts to draw learners’ attention to both pragmalinguistic and sociopragmatic features, pay more attention to these areas and allocate more time and practice to solve learners’ problems in these areas.
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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 | 0.007 |
| 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 | 0.001 |
| 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".