Advantages and disadvantages to teaching Physical Education Teacher Education through emergency remote delivery: experiences from a collaborative self-study
Bibliographic record
Abstract
COVID-19 disrupted traditional educational programming and forced post-secondary programs to shift online for Fall 2020. Physical Education Teacher Education (PETE) had an especially difficult task to re-envision their programs. The purpose of this collaborative self-study was to explore the advantages and disadvantages of teaching PETE online during emergency remote delivery and to share lessons learned from our experience with the PETE field. Using the self-study of teacher education practice (S-STEP) methodology, two teacher educators collected data (24 journal reflections, five transcribed Zoom meetings, and course documents and artifacts) corresponding to the teaching of their elementary curriculum and pedagogy courses. Data was analyzed using reflexive thematic analysis. Themes were generated in relation to our reflections on (a) Problematising our pedagogical practice (b) The struggle of letting go of perfect, and (c) You can try, but you can’t do it all (and shouldn’t). Discovered advantages and disadvantages of teaching PETE through remote delivery are discussed along with our lessons learned regarding synchronous/asynchronous balance, course structure, and PETE content.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".