Reflective Teaching And Self-Efficacy Beliefs: Exploring Relationships In The Context Of Teaching EFL In Iran
Bibliographic record
Abstract
This article reports on a study that explored the relationship between reflective teaching and teachers’ self-efficacy beliefs. Two questionnaires, the English Language Teaching Reflection Inventory (Akbari, Behzadpoor, & Dadvand, 2010) and Teachers’ Efficacy Beliefs System-Self (TEBS-Self) (Dellinger, Bobbett, Olivier, & Ellett, 2008), were distributed among 225 Iranian EFL (English as a Foreign Language) teachers. Pearson product-moment correlation analysis showed a significant positive relationship between the general factors of teacher reflectiveness and self-efficacy. Standard multiple regression identified Efficacy for Learner Engagement as the only predictor of teacher reflectiveness and Meta-Cognitive Reflection as the only predictor of teacher self-efficacy. Finally, the interconnections between the components of the two constructs were investigated using Structural Equation Modelling. While most of the components of both variables were significantly interrelated, some were not, and Cognitive Reflection and Efficacy for Classroom Management had a negative relationship. The results are discussed in light of the literature, and suggestions for further research are presented.
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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 | 0.010 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".