The Relationship between Iranian EFL Teachers’ Critical Thinking Ability and their Professional Success
Bibliographic record
Abstract
In the face of too much incoming information and too many people trying to convince us in today’s world, the ability to think critically gains an ever greater saliency as a prime goal of student and teacher education. The present study aimed at substantiating the relationship between EFL teachers’ critical thinking ability and their student-evaluated professional success. To this end, measures of the critical thinking ability of 67 Iranian EFL teachers were obtained using the Farsi version of Watson-Glaser Critical Thinking Appraisal, Form A (WGCTA-FA) (Watson & Glaser, 1980) (Faravani, 2006). In addition, their professional success was estimated by their students through the Successful Iranian EFL Teacher Questionnaire (SIETQ) (Pishghadam & Moafian, 2009). The Pearson product-moment correlation analysis indicated a statistically significant relationship between the two sets of measures (r = 0.7, p ? 0.05). More specifically, the multiple regression analysis demonstrated that three of the five aspects of critical thinking as defined by Watson and Glaser (1980), namely ‘drawing inferences’, ‘interpreting evidence’ and ‘evaluating arguments’, are significantly positively correlated with SIET scores. Implications relate to the need to accommodate ‘critical thinking’ as an essential aspect of EFL teacher education and teacher evaluation programs, and to readdress the concept of EFL/ESL teacher effectiveness with an eye to teachers’ critical thinking ability.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".