Design thinking as instructional design: examining a professional learning community for pre- and in-service teachers
Bibliographic record
Abstract
Across jurisdictions, new and experienced teachers are expected to engage in ongoing professional learning that centers context, student learning, and teachers as adaptive instructional designers. The present study examines one such professional learning opportunity. From 2017 to 2020, a university teacher education program partnered with a school division in Alberta, Canada, to create a professional learning community (PLC) for instructional design. Pre- and in-service teachers jointly participated in 12-month cycles of formal workshops, sustained practicum placements, and iterative opportunities for co-learning and reflection to strengthen their skills as instructional designers, with a specific focus on a design thinking approach. Semistructured interviews conducted between June 2020 and June 2021 with six pre- and in-service teachers who had participated in the PLC identified five key themes: (a) a sense of willingness, (b) teaching for innovation, (c) creating space to change practices, (d) a notion of ‘currency’, and (e) collaboration across stakeholder groups. Participants’ insights offer situated examples of how such collaboration may extend knowledge sharing across pre- and in-service boundaries to provide multilevel supports for teacher-led professional learning.
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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 | 0.023 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.012 | 0.022 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| 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".