Designing transformational professional learning opportunities: understanding participants’ experiences in an on-line and in-place imagination-focused self-study
Bibliographic record
Abstract
The question of how to transform teachers’ practices is vexing, especially if the goal is to change deep set conceptions of human separation from the natural world. In this research, we employed an arts-based methodological approach to consider what kind of professional learning design supports transformation towards pedagogies that acknowledge humankind’s interconnectedness with the more-than-human world. We examined educators’ experiences in a program called Spring TIPs (Teaching Imagination in Place), a self-study designed to engage three principles of eco-social change: Relationality, Immersion and Affect. Seeking to engage imagination explicitly in our methods to enrich analysis possibilities, we employed digital collaging alongside thematic coding to analyse the data collected in focus group interviews. Findings indicate how the program design supported participants’ transformative unlearning and new learning. Unexpectedly, the arts-based and imaginative research process supported our unlearning too, revealing implications and next steps for imagination-focused professional learning for teachers and researchers.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| 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".