Developing an Optimal Model for Environmental Education Based on a Comparative Study of Educational Systems in Developed Countries
Bibliographic record
Abstract
The aim of this study was to develop an optimal model for environmental education in Iran’s educational system based on a comparative analysis of the educational systems of four developed countries: Finland, Canada, the United States, and Iran. This was a descriptive-applied study using a comparative approach. Data were collected through the analysis of official curriculum documents, educational guides, policy reports, and the official websites of the selected countries' ministries of education. Purposeful sampling was applied based on criteria such as educational success, access to English-language resources, and geographical diversity. Data were analyzed using Brady’s four-step comparative model (description, interpretation, juxtaposition, and comparison). Results indicated that an effective environmental education model should consist of four key components: objectives, content, teaching methods, and assessment strategies. Sub-components such as fostering environmental responsibility, understanding human and technological impacts, practical training, the integration of digital tools, and qualitative assessment were found to be central to successful systems. The Iranian system demonstrated considerable gaps in curriculum integration, contextual relevance, and methodological diversity compared to its developed counterparts. Improving environmental education in Iran requires a comprehensive curriculum framework, technological integration, teacher training, and multi-dimensional assessment practices. The experiences of developed countries highlight that impactful environmental education must be cohesive, participatory, and grounded in real-world contexts.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| 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".