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Record W4399102501 · doi:10.1080/09575146.2024.2358422

Using action research to infuse nature-based loose parts play into the Kindergarten Program

2024· article· en· W4399102501 on OpenAlexaffabout
Hongliang Hu

Bibliographic record

VenueEarly Years Journal of International Research and Development · 2024
Typearticle
Languageen
FieldPsychology
TopicOutdoor and Experiential Education
Canadian institutionsTD Bank GroupUniversity of Toronto
Fundersnot available
KeywordsAction researchAction (physics)PsychologyMathematics educationPedagogyDevelopmental psychology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.800
Threshold uncertainty score0.565

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.212
GPT teacher head0.556
Teacher spread0.344 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

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