A Comparative Study of Arts Education Curriculum of Primary Schools in Iran and Canada
Bibliographic record
Abstract
The rationale for conducting this study was based on an interdisciplinary approach between art and curriculum. The main purpose was to examine Iranian and Canadian primary arts education curricula based on these four components: goals, content, teaching methods and evaluation methods. The research method was comparative, using Bereday’s four-step approach. The sample selection method was based on the strategy of “different systems, different results”. Data were collected using documents, available information in official government databases, books, and publications, that were analyzed based on John Stuart Mill’s agreement/difference method. The findings revealed significant similarities and differences between the primary schools’ arts education curricula in Iran and Canada. The similarities were found to be based on the goals and the differences mainly in the content of curriculum. Regarding the goals, although both countries have used their national cultural backgrounds in formulating the goals, Canada also has considered the native and folk culture in development of the curriculum. Furthermore, in Canada, the emphasis is on awareness of inner emotions as a prerequisite for the production of art. In the element of content, dance and music education is not included in the Iranian arts education curriculum, and in Canada, the storytelling falls under the category of drama. As to the teaching methods, getting artists to teach art is not conventional in Iran’s education, but in Canada, artists teach art alongside teachers.Moreover, the purpose of arts education evaluation in Canada is to "improve learning", while in Iran, the measure of "learning ability" is generally considered.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".