Bibliographic record
Abstract
Abstract For over 50 years, the forest school approach to nature learning has gathered momentum in the UK and across parts of Europe including Scandinavia (Knight, 2016). In other contexts such as Canada, New Zealand and Australia, nature-based early childhood education and care settings, influenced by European forest school approaches, have begun to gain popularity. Opportunities for STEM education occur in nature-based settings, such as forest schools and nature kindergartens, yet this area has only garnered limited research attention to date. One such example of a nature kindergarten which emerged in the 2010s is Australian ‘bush kinder’ where 4- to 5-year-old preschool children experience and learn from nature. This paper arrives at an innovative conceptualisation of STEM teaching and learning in bush kinders. Through analysing research in early years STEM education, teacher pedagogy and early childhood learning, I propose a teaching and learning process that is replicable for similar nature-based early childhood education and care settings. Drawing on vignettes from ethnographic fieldwork data, the conceptualisation of an integrated approach to STEM teaching in bush kinders is illustrated. To frame the approach to STEM teaching, this analysis builds on the notions that STEM teaching and learning can take the form of a five-phased cyclical process. It is this process that contributes to the conceptualisation of STEM teaching and learning in early childhood education.
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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 | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".