Experiential Learning in Outdoor Classrooms in Southern Alberta: A Case Study Involving Teachers at Urban Schools
Bibliographic record
Abstract
Noticing an increase in the construction of learning spaces adjacent to schools, this research study was designed to find out how teachers are using outdoor classrooms in urban settings to support student success and well-being through experiential learning. Case studies were carried out at four urban school locations. Key elements were touring the sites, and interviewing the teacher participants. An intentional variety of geographic and socio-economic locations were selected as well as a variety of student ages (K-12) and teacher experiences for this project. Despite the wide range of contexts, commonalities were identified between the research sites regarding: understandings of nature as an educational place, supports required to enable learning to take place in nature settings, what we can learn from the land in outdoor classrooms while incorporating Indigenous ways of knowing, what outdoor classroom experiences teachers designed, and how they relate to nature and to their students in these outdoor classroom spaces. During the planning stages of this project, the Covid-19 pandemic was still a concern, and the study also examined how the pandemic influenced the choice to teach and learn outside. Key findings identified four key elements for outdoor experiential learning success from the interview themes: preparation, nature awareness, connection, and regulation. Necessary supports included administrative encouragement, professional development and the reduction of barriers for teachers. Other implications on how to increase outdoor learning opportunities included sharing successes with other teachers, providing supplies, and having nature spaces easily accessible.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".