The Relationship between Iranian EFL Learners’ Use and Preferences of Language Learning Strategies and Their Meta-Knowledge and Tasks of Pragmatic Competence
Bibliographic record
Abstract
The present study investigated the relationship between Iranian EFL learners’ use and preferences of language learning strategies and their meta-knowledge and the tasks of pragmatic competence. Quick Oxford Placement Test, version 2 (2004) was administered and 120 upper-intermediate EFL university students majoring in English translation and TEFL were recruited from the available branches of the Islamic Azad University, Fars province. Likewise, a questionnaire entitled Iranian EFL learners’ meta-knowledge and pragmatic tasks (2016) was used to explore EFL learners’ meta-knowledge and tasks of pragmatic competence and the mean score was calculated. Then, Oxford Strategy Inventory for Language Learning (1990) was used to analyze EFL learners’ use and preferences of language learning strategies and the mean score was calculated. Also, to examine any possible relationship between EFL learners’ use and preferences of language learning strategies and their meta-knowledge and pragmatic tasks, some correlations were run. The results of data analyses showed that Iranian EFL learners were moderate in meta-knowledge and tasks of pragmatic competence and in their use of language learning strategies in general. More detailed analyses of the data ranked the order of the applications of the strategies as: social strategies, meta-cognitive strategies, cognitive strategies, memory strategies, compensation strategies and affective strategies from the most to the least frequent strategies applied by the participants of the study. There was a significant medium positive relationship between meta-knowledge and tasks of pragmatic competence and meta-cognitive strategy. However, there were slight but significant positive relationships between meta-knowledge and tasks of pragmatic competence and compensation and affective strategies and no relationships between meta-knowledge and tasks of pragmatic competence and social, affective and compensation strategies.
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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 | 0.005 |
| 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 | 0.000 |
| 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".