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Record W4223986193 · doi:10.22456/1982-8918.122740

Miscibility in blended approaches to PETE practice

2022· article· en· W4223986193 on OpenAlexaff
Tim Fletcher, Mats Hordvik

Bibliographic record

VenueMovimento (Porto Alegre) · 2022
Typearticle
Languageen
FieldHealth Professions
TopicPhysical Education and Pedagogy
Canadian institutionsBrock University
Fundersnot available
KeywordsMiscibilityBlended learningMetaphorFormative assessmentHomogeneousPsychologyMathematics educationMaterials scienceMathematicsEducational technology

Abstract

fetched live from OpenAlex

In this paper, we discuss findings from a collaborative self-study we conducted on enacting a blended approach (i.e. consisting of online and in-person components) to a Physical Education Teacher Education (PETE) course. Data consisted of reflective journals and recorded conversations. We examine assumptions about the structure, content, and pedagogies of “blended” approaches using the metaphor of miscibility, where liquids mix to form a homogeneous solution. The blended PETE course was not conceptualized or enacted in a way that made it miscible (i.e. it was immiscible), in that the in-person and online components were viewed as separate entities. This meant there were missed opportunities to offer formative experiences from one format to shape experiences in the other. Following the pandemic it will be important to consider the strengths of both in-person and online formats so that students experience the ways one can benefit the other.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.515
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.365
GPT teacher head0.485
Teacher spread0.120 · 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.

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

Citations5
Published2022
Admission routes1
Has abstractyes

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