Pre-service Teachers’ Experiences of Learning About and Through Models-Based Practice
Bibliographic record
Abstract
Background: Models-Based Practice (MBP) represents an innovative approach toward meaningful pedagogical and curricular change in physical education. However, little is known about the ways pre-service teachers (PSTs) learn about multiple models (theories and benchmarks), while also learning through the models (experiencing the models as learners) during Physical Education Teacher Education programs. Purpose: Drawing from Loughran’s (2006) pedagogy of teacher education theory, the purpose of this paper is to understand PSTs’ experiences learning about and through MBP focusing specifically on the following models: Teaching Personal and Social Responsibility, Cooperative Learning, Peer Teaching, and Teaching Games for Understanding. Method: Participants were PSTs enrolled in three distinct PETE courses. An ethnographic approach was used to provide rich description and interpretation of data, uncovering shared meanings of nine PSTs' experiences of MBP. The following data were gathered: 27 individual interviews, 2 focus group interviews, and 16 reflective journals. Using content structures from pedagogy of teacher education theory, data were analysed by coding and comparing participants’ responses. Results: Many PSTs gave positive responses to MBP after enrolling in one relatively short 13-week course. Yet, PSTs suggested that increased experiential learning through multiple models over several courses led to increased understanding of MBP and identification as prospective teachers who could use MBP in the future. Discussion: The results of this research support suggestions that PSTs should be provided with extended opportunities to learn about and through several models during PETE programs. Program-wide adoption offers the greatest promise in supporting future implementation of MBP.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".