Impact of classroom environment on student wellbeing in higher education: Review and future directions
Bibliographic record
Abstract
Given the emerging concern for student wellbeing in public health discourse, a question arises: What role do campus buildings play in shaping the overall wellbeing of students? Following the PRISMA guideline, this study reviews the current building science literature that explores the relationship between higher education learning environments, specifically classroom spaces, and the wellbeing of students. Our investigation reveals that the existing literature primarily frames student wellbeing in terms of individual comfort and health. While acknowledging the importance of these aspects, we emphasize the desirability of embracing wider social and collective dimensions from an interdisciplinary perspective. We also advocate for a departure from the traditional approach that focuses primarily on mitigating adverse environmental effects to one focused on net positive environmental and human benefits. Encompassing these two perspectives, this paper presents a holistic approach to better understand the wellbeing of both individuals and the communities within educational settings. This comprehensive perspective aims to highlight the diverse and collective dimensions influencing campus wellbeing, contributing to a regenerative pathway toward achieving net-positive design and sustainability in both human and environmental terms.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".