Developing Principles of Practice for Implementing Models-Based Practice: A Self-Study of Physical Education Teacher Education Practice
Bibliographic record
Abstract
Purpose: Models-Based Practice (MBP) has been suggested as one possible physical education future. However, there are few examples that consider the challenges faced implementing MBP. The purpose of this research is to develop and articulate principles of practice for implementing MBP in physical education teacher education. Method: Self-Study of Teacher Education Practice methodology guided collection of teacher educator and preservice teacher (n = 9) data. Results: Principles of practice are identified: (a) providing opportunities for beginning teachers to analyze their learning about and through MBP provides unique insights into using MBP, (b) experiencing and examining alternatives to MBP provides preservice teachers with opportunities to practice pedagogical decision making, and (c) individual and group meetings support teacher educators and preservice teachers in crystallizing understandings of MBP implementation. Conclusion: The articulation of principles of practice offers insights into how teacher educator practice might be examined, developed, and shared for use by others.
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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 | 0.064 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 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".