Getting Radical: Using Design thinking to Tackle Collaboration Issues
Bibliographic record
Abstract
Design Thinking (DT) has recently been adopted in some higher education disciplines as an effective pedagogical approach to enable students to acquire the skills needed for solving real world problems. As a human-centered, iterative process, design thinking is characterized by working with others to understand, define and solve problems using empathy, creativity, and radical collaboration. Many university courses also stress collaboration as a learning approach. However, not all students function well in collaborative environments. Based on their work in the Design-based Thinking course at the Werklund School of Education, University of Calgary, the authors asked, “could the design thinking process be used to foster collaboration among students and encourage radical collaboration”? In this paper the authors present a brief overview of the literature in this area and propose some parallels between the design thinking and collaborative team building processes.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".