Implementing a Nature-Based Approach in Elementary Schools
Bibliographic record
Abstract
The Master of Teaching Research Project is a qualitative study that addresses the topic of implementing a nature-based approach in elementary schools. The existing literature highlights the benefits of exposing young children to nature, and suggests possible downfalls if children do not have opportunities to meaningfully engage with the outdoors and natural materials. However, much of the literature focused on an early childhood setting instead of a school environment. With this in mind, the main research question that guided this study was: How does a small sample of elementary teachers implement nature-based learning with their students? Data was collected through semi-structured interviews with two elementary school teachers currently working in Ontario. Findings suggest that a nature-based approach can be integrated into a range of schools, regardless of the school environment. In addition, nature-based educators from this study addressed ways in which teachers can incorporate the outdoors along with natural materials, while still connecting these experiences to the Ontario curriculum. Findings also show that a teacher’s perceptions of the outdoors and their willingness to incorporate nature-based experiences play a significant role. Implications for the educational community and personal practice are discussed, and recommendations are made for school boards, educators, parents/caregivers, as well as areas for further research in this field.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".