‘It’s Messy and It’s Frustrating at Times, but It’s Worth It.’ Facilitating the Professional Development of Teachers Implementing an Innovation
Bibliographic record
Abstract
This self-study research focuses on one teacher educator’s experience of learning to enact a pedagogy of facilitating teachers’ professional development (PD) through a community of practice (CoP) for teachers who were learning to use the Meaningful Physical Education (PE) approach. Twelve teacher participants with a range of experience levels were supported through a CoP in learning about and implementing the approach in their classrooms across two school years. These teachers had no prior connection to the teacher educator; they were voluntary participants who showed an interest in learning about Meaningful PE. The role of the teacher educator, who was conducting this research as part of a university-affiliated research project, was to facilitate CoP meetings while simultaneously collecting data on teachers’ implementation of Meaningful PE. Identity theory is used to make sense of the facilitator’s experience of learning to enact a pedagogy of facilitation. Qualitative data sources include: researcher reflective journal; teacher interviews; CoP meeting transcripts; and non-participant observations in teachers’ classrooms. Three themes are highlighted: (1) the facilitator’s experience of developing an identity as a facilitator of PD, (2) aligning a personal pedagogical philosophy with practice, and (3) navigating the unexpected. This study highlights the role of self-study research in helping the beginning facilitator of teachers’ PD understand their sense of self and identity and navigate the tensions associated with learning to take on a new role.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".