‘I'd rather learn outside because nature can teach you so many more things than being inside’: Outdoor learning experiences of young children and educators
Bibliographic record
Abstract
Outdoor and nature-based activities promote better health and academic outcomes for children. The school context represents a critical opportunity to support increased outdoor time. Yet, outdoor learning (OL) is not being implemented consistently across school contexts, therefore, many students do not receive the opportunity to participate. This study was designed to support increased uptake of OL and explores young children’s perspectives of learning within an outdoor context and explores how educators support OL opportunities within an early learning context. This research places a focus on children’s voices in order to emphasize their perspective of the learning experience and to highlight experiential child-led processes within OL. We collected semi-structured interviews with students, their parents and school staff who were involved in OL. An exploratory thematic analysis was applied using QSR NVivo. Findings that emerged were organized under two main themes: Nature as the teacher and Child-led exploration of nature. Nature as a teacher contained three subthemes: 1) Seasonal change influencing inquiry, 2) Engagement with other living things in nature and 3) Dimensionality of the outdoors as an element that enhances learning – experiential immersive learning. Child-led exploration of nature contained one subtheme: Learning driven by play. These findings can be used to advocate for increased uptake of OL in education and to provide guidance to educators regarding how to include OL within their practice to enhance equitable access for children.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".