Physical and cultural characteristics of instructional spaces in four developed countries from the viewpoints of Iranian parents and students
Bibliographic record
Abstract
In the last decades, with the great changes and reforms in the educational systems, the importance of educational spaces has attracted the educational specialists in the developed countries. For making these spaces more attractive, specialists applied different strategies. For this reason, and getting more information about the quality of educational spaces in four developed countries, the present research has been done by the researchers.The research method was qualitative. For collecting data, many resources were used including library documents, Internet information, archive materials and comprehensive interviews with students and their parents. Research population included students who had been studying for at least three years in developed countries such as United States, Canada, England and Australia who are now studying in Iranian schools. Interviews with their parents were also included. Data analysis was done by coding, classifying, and organizing the data. Findings of this study are presented in two sections: 1- physical specification of educational spaces, and, 2- cultural climate of these environments. For physical situation of educational spaces, findings indicated that because of the non-centralized or semi-centralized educational system, there were many differences between schools in each state. According to students and their parents, most schools in these countries benefited from suitable and favorable spaces and the classroom patterns were designed according to new teaching methods. Findings of cultural climate indicated that five factors including sympathy, responsibility, accountability, respect, and an enjoying climate were dominated in the schools.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".