Explaining Ideal Teacher Competences in the Islamic Republic of Iran—Based on the Revolutionary Documentations of Its Education and Pedagogical System
Bibliographic record
Abstract
The roles of teachers and schools are changing, and so are expectations about them. Teachers must educate in progressively multicultural classrooms, coordinate students with particular needs, utilize ICT for teaching viably, engage in evaluation and accountability processes, and involve parents in schools. In such, this study aimed to identify and introduce ideal teacher competences in the Islamic Republic of Iran based on the revolutionary documentations of its education and pedagogical system. To do so, 544 pages of the texts of these documentations were meticulously studied and analyzed by qualitative content analysis using the inductive method in creating categories. Then, 138 items representing ideal teacher competences in the Islamic Republic of Iran were extracted and categorized. The results of the research showed 5 main domains of competences—including knowledge, skill, attitude, action, and ethics—as well as their sub-qualifications and related components. This analysis facilitates codification of special criteria for recruiting efficient and effective personnel; planning, predicting and designing a curriculum based on teacher competences; and attracting the attention of experts and macro curriculum planners of universities responsible for teacher training.
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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 | 0.003 |
| 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 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".