Quality Learning Environments: Design-Studio Classroom
Bibliographic record
Abstract
Design education requires a specific setting that facilitates teaching/learning activities including lecturing, demonstrating, and practicing. The design-studio is the place of design teaching/learning activities and where students/students and students/instructor interaction occur. Proper interior design improves not only the function of such learning environment but also the confidence of its users involved in the teaching/learning process. This study finds impetus in the lack of research data relative to the design of the design-studio classroom, most crucial space in design and architectural education. The purpose of the study is to examine the design-studio classroom environment and to determine, by the perception of its users, to which level this specific environment assures users’ needs and objectives. A survey was developed and distributed to a purposive sample of design and architecture educators. Ninety four responds were collected. The results of the study support the stability of earlier findings that the physical environment has a direct impact on the satisfaction of the space users. The findings suggest that lighting, noise, glare, air quality, temperature, seats comfort and possibilities of arrangement are all essential environmental features in the achievement of an appropriate pedagogic environment. Likewise, it was found that designated workstations are important part of the teaching/learning process of design. It also emerges from this study that lighting is the most important feature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".