Developing principles of physical education teacher education practice through self-study
Bibliographic record
Abstract
Background: The articulation of specific principles of teacher education practice allows teacher educators to make explicit the beliefs, values, and actions that shape their practice. Engaging in processes to articulate the principles that guide practice is beneficial not only for teacher educators and their colleagues but also for students. There are, however, few examples of principles that guide physical education teacher educators' practices. Self-study of teacher education practice (S-STEP) methodology offers one way of examining and articulating principles of practice. In this study, I make connections across several S-STEP research projects I have conducted individually and with colleagues, and share the principles that guide my practice with the physical education teacher education (PETE) community.Purpose: The purpose of this paper is to articulate my principles of practice using S-STEP. Specifically, I ask: (a) How can the articulation of my principles of practice reflect broad understandings of PETE? and (b) How can sharing principles of practice encourage debate and discussion amongst members of the PETE community? To what extent do the principles articulated have resonance for others?Participants and data collection: Six published self-studies as well as the raw data from those studies provided the data for this research. The raw data used in those studies consisted of self-generated data and data generated by others. Self-generated data consisted of written reflective journal entries gathered over five years and recorded audio conversations with two critical friends. Data generated by others consisted of semi-structured interviews conducted with two cohorts of pre-service teacher candidates: one consisting of 10 pre-service primary generalist teachers the other of 9 pre-service physical education specialists. Three interviews were conducted with each participant. Exit slips (informal evaluations) were also gathered from the specialist cohort.Data analysis: First, elements of the previously conducted self-studies were synthesised to identify general themes and outcomes that represented principles of practice. Second, in several instances, the raw data were revisited to verify and contextualise quotes and excerpts, and consider the extent to which the data captured the principles that were being articulated.Findings: Three central principles were identified that shape my understanding of a pedagogy of PETE: (a) building community is the foundation of practice, (b) not just modelling – explaining and reflecting upon modelling, and (c) identity matters. Identifying these principles has enabled me to better enact social constructivist approaches to learning, make explicit my personal and professional knowledge to myself, students, and colleagues; find meaning in my practice, and; begin sharing my partial understanding of practice with others in the teacher education community to generate debate and discussion.Conclusions: Self-study encourages teacher educators to share their knowledge so that it may be discussed, challenged, and critiqued to further collective understandings of teacher education practice. In this spirit, these principles are not offered as an exhaustive list of all that guides PETE practice, but as suggestive of possibilities that might reflect shared understandings of teacher education and thus have the potential to influence policy.
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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 | 0.049 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".