MétaCan
Menu
Back to cohort
Record W1522740365

Experiences of Learning to Teach Physical Education: Navigating Tensions

2016· article· en· W1522740365 on OpenAlexaff
Shannon Kell

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldHealth Professions
TopicPhysical Education and Pedagogy
Canadian institutionsMount Royal University
Fundersnot available
KeywordsMathematics educationPsychologyPedagogyPhysical educationSociology
DOInot available

Abstract

fetched live from OpenAlex

This narrative inquiry explored two pre-service teachers ’ experiences of learning to teach Physical Education during a 16-week internship. A research puzzle was named: how learning to teach is experienced by pre-service teachers and how they dwell in spaces of tension while learning to teach Physical Education. Two pre-service teachers in secondary urban school settings were part of the study over a six month period before, during, and after the 16-week fall semester internship. Field texts included audio recorded and transcribed group and one-on-one conversations, field notes from school visits and teaching observations, journal writing and reflections, artifacts from the participants’ internship, and text message conversations. Narrative accounts that inquired into their experiences were co-composed with each participant. Three threads of narrative connection reverberated, moving toward new wonderings related to the research puzzle: shifting stories to live by, teaching their way, and working alongside teachers. Questions arose about how we might be able to use this inquiry to reflect on our own experiences and practices and how narrative inquiry may be a valuable methodological approach for Physical Education teacher education.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.170
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.097
GPT teacher head0.531
Teacher spread0.434 · 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; both teacher heads agree on what is shown here.

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

Citations2
Published2016
Admission routes1
Has abstractyes

Explore more

Same topicPhysical Education and PedagogyFrench-language works237,207