The process(es) of learning about teaching using models-based practice: Pre-service teachers’ experiences
Bibliographic record
Abstract
This research takes on the recommendation to continue examining the use of models-based practice (MbP) in diverse contexts by considering pre-service teachers’ (PSTs’) experiences of learning to teach using MbP in a physical education teacher education (PETE) program in Norway. Guided by the theory of a pedagogy of teacher education ( Loughran, 2006 ), this research was driven by the question: “What are PSTs’ experiences of learning about teaching using MbP in one comprehensive PETE course?” The context was a 15-credit PETE course taught collaboratively by four teacher educators to two cohorts of first-year undergraduate PSTs (25 PSTs in each cohort). Data were generated through a total of 24 focus group interviews with eight PST groups before, during, and upon completion of the course. A hybrid approach of inductive and deductive theme development enabled us to produce knowledge of how PSTs’ learning evolved through four phases: (a) (traditional) assumptions about physical education and teacher education, (b) learning about and through a new way of teaching and learning physical education, (c) challenging and being challenged by the traditional “gym” culture in schools, and (d) understanding what it means to be and become a (physical education) teacher. This research offers support to claims about the challenges in creating coherence at different levels in PSTs’ learning experiences in a Norwegian PETE program. At the same time, we show that MbP can provide PSTs with a coherent learning experience, potentially resulting in changes to how PSTs think about teaching physical education.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".