The effect of environmental control course on architectural design projects (Case study environmental control course - Canadian International Collage – Egypt)
Bibliographic record
Abstract
It is a global strategy to save the environment through sustainable designs; this approach must begin throughout the college phase and progress through their professional career. During their academic journey, undergraduate architecture students take many courses, including a chain of Design courses and an Environmental Control (EC) course. The main aim of this paper is to discuss the importance of EC courses and how they affect design courses' outcomes. The research methodology starts with secondary data through a literature review about environmental studies and their impact on design, what the topics that the EC course has covered, and when it should be taught to be able to design a framework that helped the instructors in developing their courses' plan, then collect the data from the instructors of both courses EC and design to develop the EC course plan by adding new assignment asking students to apply what they learn during EC course in one of their previous design projects which submitted before registered the EC course and gained a knowledge of different environmental techniques. Finally, test the application of the suggested Framework by comparing the results of design projects before and after EC study and validate the results by publishing an online survey targeting the experts in different countries to demonstrate the importance of EC courses as a powerful learning tool in architecture education, as well as to be a comprehensive approach that reflects the relationship between design and environmental studies to improve both architects and building design performance.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 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) | 0.009 | 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".