Providing an Optimal Model for the Elementary Teacher Education System at Farhangian University in Iran, Based on the Experience of Japan, Canada, and Australia
Bibliographic record
Abstract
Purpose: The main objective of this research is to adapt appropriate models from Japan, Canada, and Australia, aligned with the culture and education system of Iran, for developing students in the teacher education programs in Iran. Methods: The research approach is mixed-methods (qualitative-quantitative), with an applied purpose, utilizing George Brady's deductive research model. Data analysis was conducted using confirmatory factor analysis (CFA) through the Smart PLS software. This study employs the four stages of description, interpretation, proximity, and comparison in George Brady's model to examine and compare the main elements of the curriculum (objectives, content, teaching-learning strategies, materials and resources [human and equipment], teaching-learning opportunities, learning environment conditions, and evaluation) in the selected countries to improve Iran's curriculum. Subsequently, after achieving a consensus score, a framework for the curriculum was proposed. Findings: The most important findings indicate that improving the quality of the teacher education system is the main goal, and lifelong learning is the specific objective of this system in the studied countries. Additionally, based on the strategic model for Iran's teacher education system, and considering the performance of leading countries and the findings from interviews using the Delphi method and model fitting, it can be concluded that teaching-learning opportunities, with a factor loading of 4.578, had the greatest influence on the curriculum, while content, with a factor loading of 2.788, had the least impact. Other components—materials and resources (human and equipment), learning environment conditions, teaching-learning strategies, evaluation, and objectives—ranked second to fifth, with factor loadings of 4.060, 3.059, 3.288, 2.801, 2.788, and 2.469, respectively. Conclusion: Startups that prioritize fostering an innovative and collaborative culture, adopt inclusive leadership, and leverage both employee-driven and data-driven innovation are better positioned for long-term success. External support through incubators and accelerators provides valuable resources and networks that further contribute to the scaling and sustainability of startups. This study highlights the importance of organizational culture, leadership, and innovation as central components in the entrepreneurial ecosystem, offering practical insights for startup founders and managers.
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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 | 0.004 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".