Design thinking as instructional design: examining a professional learning community for pre- and in-service teachers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it