Heuristics for Problem Framing: Collaborative Scaffolding in Design Thinking Education
Bibliographic record
Abstract
To help DT learners in management education through the process, educators employ instructional scaffolds, i.e. facilitation, guidelines and interventions intended to enable DT learners to reach the zone between what they can do unsupported and what they cannot do even with support. However, faced with an unfamiliar task and process, group members themselves might assume scaffolding responsibilities to help others in the group reach further than they could without it. To examine the relationship between instructional and collaborative scaffolds in management education, we conducted a qualitative empirical study of MBA and Executive MBA courses in Design Thinking at a North American business school. We found that instructional scaffolds were enacted, enriched and reinterpreted in the collaborative scaffolding in the learner groups, with significant consequences for achieving the intended learning outcomes. The paper contributes to the literature on learning and teaching in management education, and more specifically to scaffolding in design thinking and other problem-based educational approaches.
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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 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".