A Comparison of virtual physical education teacher education experiences in Brasilia and Ottawa: learning together in COVID-19 times
Bibliographic record
Abstract
The COVID-19 pandemic inspired changes in the way we teach and think about the curriculum of physical education (PE). This comparative inquiry pays particular attention to the ways PE was introduced in two university teacher education programs, one based in Brasilia-Brazil and the other in Ottawa-Canada. The three dimensions which guided our comparative analysis included: a) our respective COVID-19 circumstances, b) the PE Elementary Curriculum which we introduce to our teacher education students, and c) our course plan for teaching Physical Education Teacher Education (PETE) students from August to December of 2020. Despite the difficulty of going through the trauma brought forth by COVID-19, this article reveals how three academics came together to co-create, share, and compare their PETE practice which integrated technology, virtual games, break-out room discussions and social media challenges. Through ongoing dialogue and an openness to learn from each other, this comparative inquiry created an opportunity for an exchange of ideas, support and hope for the future of PETE.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.033 | 0.015 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".