Bridging Western Theories and Indigenous Perspectives to Implement STEM in Outdoor Early Childhood Educational Settings
Bibliographic record
Abstract
During the Covid-19 pandemic, educators were obliged to rethink traditional classroom settings and explore alternative learning environments. Consequently, numerous outdoor education programs and forest schools emerged in North America during that time. These outdoor alternatives were met with great enthusiasm given that these programs offered a unique advantage during the pandemic, as they could easily enforce physical distancing while also providing a natural space with fresh air circulation. Concurrently, STEM (Science, Technology, Engineering, and Mathematics) education has become a popular focus in 21st-century classrooms. By incorporating STEM subjects into outdoor education programs, children are given the opportunity to develop their problem-solving, critical thinking, and analytical skills in a natural environment. By engaging in STEM activities such as building structures, observing and analyzing natural phenomena, and experimenting with technology, children can develop an appreciation and develop a deeper understanding and sense of belonging with the natural world while also gaining important skills for the future. This article emphasizes on how combining outdoor education and STEM subjects can result in a holistic approach to education that addresses the needs of the whole child. Children are not only able to learn about the natural world but also to develop fundamental skills that will help them in their future education and careers. Additionally, outdoor education can provide children with a sense of well-being and connectedness to the natural world, which can have positive effects on their mental and physical health. Keywords: STEM learning, Indigenous, Outdoor Learning, Forest School, Land-Based Learning, Western Theories, Early Childhood Education. DOI: 10.7176/JEP/14-9-01 Publication date: March 31 st 2023
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".