Dipping your toes in the water: early childhood science learning at a beach kindergarten
Bibliographic record
Abstract
Abstract The forest school approach to learning has gathered momentum in the UK and parts of Europe for well over 50 years. In other contexts such as Canada, China, New Zealand and Australia, nature-based early childhood education and care (ECEC) settings, influenced by European forest school approaches, are in a growth phase. While research attention is often given to ‘green spaces’ such as nature reserves, parklands and forests, less consideration has been given to the ‘blue spaces’. Blue spaces incorporate beaches and coastal environments and can be rich contexts for early childhood science education. One example of a nature-based approach to ECEC is the Australian ‘bush kinder’. Bush kinders are growing in number and educators have been observed to include sessions at beach environments as part of year-long bush kinder programmes. Beach kinders often involve four- to five-year-old preschool children and provide experiences to learn from and about the natural world through play in the water, on the sand and amongst coastal woodlands. This paper highlights the importance of educators in fostering science teaching and learning in the context of beach kinders. Through analysing early years science education research, guiding curriculum frameworks and early childhood learning, the importance of providing children with beach kinder opportunities to enhance understandings of early childhood science education is discussed. Drawing on vignettes from ethnographic data, gained through researcher participant observation, the benefits of educators scaffolding children’s of physical, chemical and biological science experiences present in coastal environments is considered in this paper.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".