The Implications of Sustainable Design Considerations for an Effective Learning Environment in Industrial Design Studios
Bibliographic record
Abstract
Design studios are the central part of the learning environment in industrial design schools, and the incorporation of sustainable design considerations are essential for the optimization of the learning spaces for design students in our current times.These studio spaces can play a salient role in facilitating collaboration among students and promoting creativity.Recently, the School of Industrial Design's (SID) studios at Carleton University have been redesigned with an effort to resolve interior design challenges such as Indoor Environmental Quality (IEQ) and sense of space.Through semi-structured interviews and extensive field notes, this study sought to obtain feedback from end-users (students and faculty members) on their overall experience using the new design studios.This study also pinpoints problematic areas that need to be addressed, based on emerging themes.Preliminary directions and guidelines have been proposed that can be implemented in order to improve the studios in the School of Industrial Design.These recommendations include the improvement of IEQ, such as thermal comfort, lighting comfort, acoustic comfort, air quality, and energy consumption, as well as recommendations for other themes including maintenance and waste management and materials storage in relation to learning environments in design education.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".