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Record W2945776441 · doi:10.1080/09575146.2019.1619670

Playing with a goal in mind: exploring the enactment of guided play in Canadian and South African early years classrooms

2019· article· en· W2945776441 on OpenAlexafffundabout
Hanne Jensen, Angela Pyle, Betül Alaca, Ellen Fesseha

Bibliographic record

VenueEarly Years Journal of International Research and Development · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of CanadaLEGO Foundation
KeywordsPsychologyEarly childhood educationDevelopmental psychologyPedagogySociology

Abstract

fetched live from OpenAlex

Guided play, a balanced approach to involvement in play that includes child- and adult-direction in learning activities, holds great promise for children’s effective and engaged learning in education. Recent studies in laboratory settings show benefits for academic and socio-emotional outcomes, while retaining a focus on child-centred exploration. Often, these studies feature an experimenter with one child or a small group. In contrast, educators in early years classrooms often need to support 20 children or more. To realise guided play’s promise, we need to explore the enactment of guided play in classroom settings, and how educators can engage young children in responsive ways to promote opportunities to learn in play contexts. We offer a cross-cultural comparison of guided play that occurred in 12 Canadian and 8 South African early years classrooms. Using a qualitative, thematic approach, we analysed video-recorded observations for: 1) the frequency of educator involvement in play contexts, 2) the role of the educator in those contexts, and 3) learning opportunities that emerged due to this involvement. Based on our analysis, we consider how educators can achieve guided play in classroom settings. Implications are discussed for practice, including barriers and enablers of guided play in culturally diverse settings.

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)0.003
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score0.958

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 head0.069
GPT teacher head0.340
Teacher spread0.271 · 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

Citations26
Published2019
Admission routes3
Has abstractyes

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