Learning from others: A five-year experience on teaching empathic design
Bibliographic record
Abstract
Design is about making our world a better place. Design education is a key factor in improving the quality of life and we have to teach our future designers to be better listeners, observers, storytellers and creators. A toolbox to help aspiring designers to seek insight through the aesthetic experience of others is essential to develop their empathic abilities and to learn how to think outside the box. However, it is surprising to realize that only a few design schools offer courses, let alone approaches, to engage in such methodology. This kind of toolbox was created in a pedagogical integration project conducted at the University of Montreal School of Industrial Design and was the result of five years of workshop teaching. Thus, this paper proposes a critical reflection on pedagogical challenges met in teaching empathic design practices. The observations collected in the workshops allowed three types of findings: the a prioris, the learning experience, and the limits of the creative integration of data. As a way to integrate research based-tools to support and defend design strategies, the approach presented in this paper allows the use of more refined methods to teach empathic 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 | 0.021 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".