Teaching the concept of adaptive thermal comfort in building design education
Bibliographic record
Abstract
Traditional building design education in the disciplines of building services, civil engineering, mechanical engineering, etc. focusses on quantifiable factors, i.e. factors which can be subject of calculation, simulation and dimensioning. Since non-quantifiable factors are not part of calculation or simulation models, there is a tendency that they are not adequately considered in education and, as a result, in building planning and operation. In the case of human thermal comfort, building professionals are required to understand the mechanisms and impact of non-quantifiable factors related to human thermal comfort and health, most of which are part of the adaptive thermal comfort concept. This paper identifies the challenges in teaching the concept of adaptive thermal comfort through a collection of lived experiences of the authors. The paper then proposes appropriate intended learning outcomes and teaching methods to equip students with the background knowledge, understanding and skills required to consider human adaptation in their designs. It is widely accepted that integrated design is a precondition for a building design that functions well, offers sufficient comfort for their users and can be operated in a sustainable way. Integrated building design requires that different disciplines work closely together and understand and respect their different focus areas and languages. Herein the case of human thermal comfort requires engineers who understand the mechanisms and impact of non-quantifiable factors on human well-being indoors. Within the work of Annex 69: “Strategy and practice of adaptive thermal comfort in low energy buildings” we developed a framework and a guideline for adopting adaptive thermal comfort principles in design and operation of buildings, which may serve as a complementary tool in an integrated design process and inclusion of adaptive thermal comfort in education.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".