Learning to teach health and physical education : the experiences of elementary student teachers
Bibliographic record
Abstract
This research investigates elementary student teachers’ experiences of learning to teach health and physical education (HPE) in a one-year pre-service teacher education program at Windermere University in Canada. The participants in the research are preparing to become elementary classroom teachers; a group who often recall negative prior experiences of HPE from their time as school pupils and report an overwhelming lack of preparation and confidence to teach HPE. Mixed-methods of data gathering were employed in the form of pre- and post-test surveys of 308 student teachers, and three interviews conducted with a purposive sample of ten student teachers. Four main findings emerged from the research. First, elementary student teachers’ embodied identity as healthy and physically active individuals profoundly shaped their prior experiences of HPE. Second, the 12-hour HPE course offered in Windermere’s pre-service program broadened student teachers’ views of HPE and provided them with some basic strategies for teaching elementary HPE. Third, the practice teaching experience provided some student teachers with opportunities to either observe or to try teaching HPE; few had opportunities to do both. Fourth, there was a positive and statistically significant change in student teachers’ identities as teachers of HPE from the beginning to the end of the pre-service teacher education program. Implications for school HPE, pre-service teacher education programs, policy regarding teachers of HPE, and future avenues for research are discussed in light of the findings.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".