Using action research to infuse nature-based loose parts play into the Kindergarten Program
Bibliographic record
Abstract
This research explores how nature-based loose part play helps unfold young children’s learning connected with the natural world in the Kindergarten Program. It uses action research to investigate nature-based loose parts play in connection with the Ontario Kindergarten Curriculum to provide unique insights into identifying and addressing action research questions in practice and enhancing educational discourses. Nature-based loose parts play is an approach for learning to reconnect young children with nature and environment through hands-on experiential experiences, to build a foundation that enhances their learning indoors and outdoors and to develop deeper understanding of the relationships of all living things. The findings reveal that, through nature-based loose parts play, creating invitations for environmental learning is the first step to intrigue Kindergarten children towards further exploration and investigation. Nature-based loose parts play is viewed as a methodology and a pedagogy to fully integrate outside and inside learning environments to enrich children’s learning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".