Teacher knowledge of biophilic classrooms in relation to student well-being
Bibliographic record
Abstract
The purpose of this study was to answer the research question: What do teachers currently know and need to know about biophilic classrooms in relation to student well-being? A look at the existing literature reveals that biophilic spaces can decrease stress levels, improve academics, improve behaviour, and therefore contribute to improvements in overall well-being. Using a mixed methods needs assessment framework, 41 teachers in Canada (primarily British Columbia) participated in a survey and 7 participated in an interview. The survey and interview asked teachers to discuss their current knowledge and implementation of biophilic classrooms, their knowledge on how a physical classroom space can affect students, what barriers may be faced when implementing this design, and what teachers need to be successful in implementing biophilic classrooms into schools. While most participants had a basic understanding of the effects of biophilic spaces on student well-being, there is a distinct gap between what is known and what is currently being implemented. This study provides some knowledge on the existing conditions in the classroom and next steps for how we can bridge this gap and encourage more teachers to consider biophilia when designing their classroom space to better support student well-being.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".