Design Thinking in Education: Adding Collaboration, Uncertainty, Phronesis and Fairydust to Curriculum Design
Bibliographic record
Abstract
We exist in an age of supercomplexity with policy and strategies both impacting and restricting creative curriculum development and participatory classroom practices particularly in Higher Education (HE). As academic developers who have also taught undergraduate programmes we inhabit liminal space - both enacting and subject to policy - both professing and subverting practice. In this paper we outline how we have engaged in human centred curriculum design ourselves. Typically curriculum evaluation and development processes are presented to our staff-as-students as something far removed from design thinking (DT). Curriculum design emphasises thorough thinking, it is slow-paced, and continuously evaluated. DT requires trust and collaboration, open sharing of diverse and often contradicting ideas, rapid prototyping - a non-judgemental space that will help ideas develop and grow, playing with initiatives that might not work. DT encourages experimentation. We used a collaborative Practice-Based Research (PBR) approach to explore our processes to reveal how DT can be a valuable part of a more fast-paced, urgent, creative and human centred curriculum design.
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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 | 0.077 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.011 | 0.068 |
| Scholarly communication | 0.023 | 0.030 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".