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Record W4390297990 · doi:10.1080/09669760.2023.2299255

Shifting perspectives: developing and integrating educators’ notions of play and early math learning in kindergarten

2023· article· en· W4390297990 on OpenAlexafffund
Hanna Wickstrom, Angela Pyle

Bibliographic record

VenueInternational Journal of Early Years Education · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Technology Integration
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPerspective (graphical)Thematic analysisPedagogyMathematics educationProfessional developmentPsychologyProfessional learning communityEarly childhood educationTeaching methodQualitative researchSociologySocial scienceComputer science

Abstract

fetched live from OpenAlex

Growing evidence promotes the suitability of play-based learning to support young children’s developmental and academic learning. Educators’ differing perspectives of play and learning, however, lead to differing implementations of play in classrooms settings that either dichotomise or integrate the constructs of play and academic learning. As contemporary notions of play advocate for the integration of play and academic learning, this current study sought to understand the ways in which kindergarten educators were successfully shifting their perspectives and practices to align with more current views of play-based learning in kindergarten education. Through thematic analysis of interview data, this study identified how educators progressed through four stages of evolution in their perspectives of both play and early math learning in kindergarten. This professional evolution led to an integrated perspective of how various approaches to play can support young children’s math learning in practice. Discussion of these findings will highlight the key factors that helped educators to make a positive change in their professional practice through shifting their perspectives of play and math learning in kindergarten.

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)0.001
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 score0.250
Threshold uncertainty score0.274

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
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 head0.020
GPT teacher head0.350
Teacher spread0.330 · 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 designObservational
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
Published2023
Admission routes2
Has abstractyes

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