Biophilic Design for Restorative University Learning Environments: A Critical Review of Literature and Design Recommendations
Bibliographic record
Abstract
The influence of environmental design on people’s wellbeing and productivity has been well studied in some settings such as offices, hospitals, and elementary schools, but salutogenic and biophilic design in urban post-secondary educational environments remains understudied and warrants closer investigation. There are unique challenges faced by these students and implementing health promoting and restorative, environmental design strategies could improve the quality of life and learning outcomes of university students. This paper identifies pertinent themes in published multi-disciplinary literature relating to the influence of the built environment on university students: emotional stress, happiness, stimulation, cognitive function, social support, belonging, places to study, lighting, and ventilation. The results of the semi-structured literature review identifies, analyzes, and categorizes relevant studies that examine nature views, nature images, natural colors, natural materials, auditory and olfactory aspects of nature, nature images with water, indoor plants, campus landscapes, study spaces, local materials and style, daylight access, and thermal and environmental comfort. These are organized according to the biophilic patterns identified by Browning, Ryan, and Clancy. Trends and gaps in understanding the influence of biophilic design on university settings are discussed, and the paper identifies evidence-based design recommendations for incorporating biophilic design in university settings.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".