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Record W3200224300 · doi:10.1123/jtpe.2021-0137

Teachers’ Engagement With Professional Development to Support Implementation of Meaningful Physical Education

2021· article· en· W3200224300 on OpenAlexaffabout
Stephanie Beni, Tim Fletcher, Déirdre Ní Chróinín

Bibliographic record

VenueJournal of Teaching in Physical Education · 2021
Typearticle
Languageen
FieldHealth Professions
TopicPhysical Education and Pedagogy
Canadian institutionsBrock University
Fundersnot available
KeywordsPhysical educationProfessional developmentPsychologyIdeal (ethics)Process (computing)Mathematics educationQualitative researchPedagogyFaculty developmentComputer scienceSociology

Abstract

fetched live from OpenAlex

Purpose: The purposes of this research were to design a professional development (PD) initiative to introduce teachers to a pedagogical innovation—the Meaningful Physical Education (PE) approach—and to understand their experiences of the PD process. Method: Twelve PE teachers in Canada engaged in an ongoing PD initiative, designed around characteristics of effective PD, across two school years as they learned about and implemented Meaningful PE. Qualitative data were collected and analyzed. Findings: This research showed that teachers valued a community of practice and modeling when learning to implement Meaningful PE. While teachers were mostly favorable to the PD design, there were several tensions between ideal and realistic forms of PD. Discussion: This research offers support for several characteristics of effective PD to support teachers’ implementation of a novel pedagogical approach and highlights the need to balance tensions in providing forms of PD that are both effective and practical.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.033
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.005
Scholarly communication0.0060.002
Open science0.0020.009
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.073
GPT teacher head0.536
Teacher spread0.463 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations19
Published2021
Admission routes2
Has abstractyes

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