Brain Dominance Quadrants and Reflective Teaching among ELT Teachers: A Relationship Study
Bibliographic record
Abstract
The present study investigated the relationship between Iranian English language teachers’ reflectivity and their brain dominant quadrants. To this end, 102 Iranian EFL teachers at several language institutes and universities (i.e., Bojnord, Ghochan, Gonabad, Kashmar, Shandiz, Neyshaboor, & Mashhad) in Iran participated in this study. The Brain Dominance Survey which was developed by Ashraf, Tabatabaee Yazdi, & Kafi was employed to categorize participants as right and left brain dominant, and English Language Teaching Reflection Inventory developed by Akbari, Behzadpoor, & Dadvand was administered to measure teacher reflectivity. Then the data was analyzed using hierarchical multiple regression analysis to investigate the extent to which teachers’ brain dominant quadrants might have predictive power in their reflective teaching practices. Results indicated a statistically positive significant correlation with teachers who used their A quadrant and teaching reflectiveness whilst teachers with C quadrant dominance had a negative significant correlation with being reflective. Moreover, regression analyses revealed that there is no significant relationship between reflectivity and teachers’ B and D brain quadrants dominance. To teach more reflectively, teachers need to better understand their brain differences and how it can affect the teaching strategies. All teachers should find ways to combine teaching activities that involve both left and right of their brain, and not only practice six underlying factors of reflection in their teaching but also employ reflective procedures in order to develop their reflective practices.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".