Experiences of Learning to Teach Physical Education: Navigating Tensions
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.018 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".