Critical Pedagogy: EFL Teachers’ Views, Experience and Academic Degrees
Bibliographic record
Abstract
Although critical pedagogy has brought about positive changes in the field of education by shifting from traditional pedagogy to emancipatory pedagogy, not much attention has been paid to the factors affecting teachers’ beliefs of critical pedagogy and only few studies have been conducted to design reliable and valid instruments to study EFL (English as a Foreign Language) teachers’ beliefs about different aspects of teaching in the field of critical pedagogy. Consequently, there is a gap in our knowledge of critical pedagogy in terms of Iranian EFL teachers’ beliefs about critical pedagogy and their tendency to implement it in teaching EFL. This study was conducted to help fill this gap, through developing a questionnaire and focusing on the relationship between teachers’ teaching experience and educational background, and their beliefs about critical pedagogy. To this end, a critical language pedagogy questionnaire was developed and validated, using factor analysis. The questionnaire was administered to 403 respondents. Pearson Correlation Coefficient and MANOVA were used to analyze the data. The result indicated that there were significant differences among the BA, MA and PhD participants’ awareness of critical pedagogy, with the PhD holders found to be the most aware of principles and practices of critical language pedagogy. Furthermore, teachers’ teaching experience had a significant relationship with their awareness of critical pedagogy with more experienced teachers scoring higher on the four factors in the questionnaire.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".