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Record W4383877078 · doi:10.3390/educsci13070701

The Role of Play and Objects in Children’s Deep-Level Learning in Early Childhood Education

2023· article· en· W4383877078 on OpenAlex
Ole Johan Sando, Ellen Beate Hansen Sandseter, Mariana Brussoni

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEducation Sciences · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsBC Children's Hospital
FundersNorges Forskningsråd
KeywordsEarly childhood educationPsychologyFacilitatorEarly childhoodDeep learningObject (grammar)Mathematics educationDevelopmental psychologySocial psychologyArtificial intelligenceComputer science

Abstract

fetched live from OpenAlex

This research investigates the significance of the physical environment in early childhood education and care (ECEC) institutions as a facilitator of deep-level learning. Building upon Laevers’ concept of deep-level learning, this study explores the interplay between objects in ECEC settings, children’s play, and their deep-level learning. The primary objective is to examine the potential mediating role of play in the relationship between objects and deep-level learning. The research methodology involves the analysis of a sample consisting of 928 two-minute video observations collected from eight ECEC institutions in Norway. The results demonstrate a positive association between children’s engagement in play, their utilization of objects, and deep-level learning. The findings suggest that constructive and symbolic play partly mediate the positive relationship between deep-level learning and object utilization. These outcomes highlight the pivotal role of play in early childhood education and emphasize how elements within the physical environment can effectively support 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.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.152
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.303
Teacher spread0.289 · 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