A framework for designing the open spaces in the children’s educational centers based on the (Seven Cs) with emphasis on pProve the learning
Bibliographic record
Abstract
The main purpose of this study was to identify the effectiveness factors in the design of open space in educational centers for children and its relationship to learning. Because learning and convey the meaning is an inseparable part in every educational process, accordingly, the authors tried to introduce the theory which named (Seven Cs including: C1: character, C2: context, C3: connectivity, C4: change, C5: chance, C6: clarity, C7: challenge). The authors believe that using this method can provide a favorable environment and adaptable with the educational goals, i.e. increasing the quality of learning and efficiencies for the children. The method used in this research is directed-content-analysis which aims is a trying to develop a theory for greater efficiency. This theory results of the several year efforts by the experimental and survey method in Canada. Initially, the resources evaluated and analyzed, then extracted the significant factors in children's environment. Consequently, these factors are measured within (SevenCs) and finding showed that both of them are in one direction. Then it is providing the framework and principles for designing the educational open spaces, according to the different needs of children which can be a beneficial guide for designers. Finally, it can be said that these principles can prove the satisfaction and greater efficiency of educational concepts for children.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".