Learning to facilitate student voice in primary physical education
Bibliographic record
Abstract
Student voice pedagogies in physical education (PE) offer teachers a mechanism to promote meaningful experiences by actively engaging students in decision-making regarding their learning. Over one academic year, the experiences of one generalist classroom teacher's enactment of student voice pedagogies in their primary PE practice were explored within a Self-Study of Teaching and Teacher Education Practice (S-STTEP) frame. Data sources included post-lesson personal reflections, a researcher journal, and transcripts from meetings with a critical friend. Qualitative data was also collected from students ( n = 19) over a shorter timeframe of six months, and took the form of student work samples, along with transcripts from focus group interviews ( n = 2, with eight total participants). Findings show that the enactment of student voice pedagogies requires significant scaffolding for both the teacher and their students. The teacher needs to learn how to listen to, nurture, and act on their students’ voices, while students require assistance in developing their capacity to share their voices. Thus, the enactment of student voice pedagogies takes time, and necessitates a period of trial and error, to ensure the educator is providing authentic student voice opportunities in their practice. This study adds an additional layer to student voice research by providing a teacher's perspective of learning how to enact student voice pedagogies in PE. Furthermore, the findings add to the limited research into the use of student voice pedagogies at primary level.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".