Teaching about planning in pre-service physical education teacher education: A collaborative self-study
Bibliographic record
Abstract
Learning how to develop lesson and unit plans is recognised as a priority for teacher education programmes; however, recent empirical research on planning is scarce, particularly in physical education. The purpose of this research was to analyse how and why we teach physical education pre-service teachers (PSTs) to plan in the ways we do. A secondary purpose was to consider alternative approaches to teaching about planning based on this analysis. Over one academic term, we used collaborative self-study of teacher education practice methodology and gathered several forms of qualitative data, including reflective journal entries, recorded video conversations, and teaching artefacts. Through sharing and interrogating our assumptions about the nature of planning and how to teach PSTs about planning, we came to see several flaws in the approaches we had typically used, particularly in terms of the emphasis given to the products (i.e. developing and submitting complete lesson plans) over the processes of planning, and how this emphasis did not necessarily support PSTs’ learning. This was partially because we found it challenging to model our processes of planning for PSTs in authentic ways. We agree that planning is and should be a central part of learning to teach; however, this research suggests that the ‘typical’ actions in how we teach PSTs about planning may be ripe for disruption and redesign. This research provides a rationale for a better balance to be struck between teaching about planning-as-process and teaching about planning-as-product.
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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 | 0.025 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".