The Impact of Teaching Critical Thinking on EFL Learners’ Speaking Skill: A Case Study of an Iranian Context
Bibliographic record
Abstract
English speaking proficiency requires more than knowing its grammatical and semantic rules. It also includes the knowledge of how native speakers of one language use the language in the context of structures of interpersonal exchange, within which many factors interact. Critical thinking is the deliberate determination of whether we should accept, reject, or suspend judgment about a claim and of a degree of confidence with which the language speakers accept or reject it. The present quasi-experimental study aimed to investigate the impact of teaching critical thinking on the speaking skill of EFL learners. To this end, 44 male and female intermediate students at Respina Talk (i.e., Iran-Canada) language school with the age range of 20-35 were selected in order to achieve the objectives of the study. According to the obtained results, there was a significant relationship between the promotion of critical thinking and EFL learners’ speaking skill. The findings of this study may have some theoretical and practical implications for material developers, EFL teachers, language learners, etc.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".