An Investigation of Speaking Strategies Employed by Iranian EFL Students
Bibliographic record
Abstract
Speaking, as one of the four macro-skills, fulfills the communicative function of language in both EFL and ESL settings. It is a tool for expressing thoughts and feelings and a way of linking to the society. Despite its importance and frequent use, speaking has remained the least studied and accessible skill for students to learn and for teachers to teach, and it has raised many questions for researchers to answer. Due to the shift from teacherto student-centered learning environments, students need to be helped in becoming strategic language learners in the long run. This study investigated the use of speaking strategies by Iranian EFL university students. Thirty-five female and 25 male students participated in the study. An Oxford Proficiency Test was administered to determine the students’ proficiency level. Accordingly, they were assigned to the three groups of high, intermediate, and low proficiency levels. The main instrument was a 38-item strategy questionnaire which was developed based on Likert-Scale answers. A t-test and a one-way ANOVA were run to compare the mean scores of the four factors and see if there are any significant differences between males and females, on the one hand, and high, intermediate, and low groups, on the other, with regard to different strategies. The result indicated that sex and proficiency level had significant roles in the using metacognitive strategies, with females showing greater favor over this factor than males. Also, high proficient students revealed more interest in the same factor than intermediate and low level students. For compensation strategies, sex showed to have a significant influence on strategic choice, with males having more preference for this factor than females. For other factors including cognitive and memory, and social/affective strategies, no significant differences were found among the variables of the study
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".