Teaching Creativity: Some Experiences in Teaching Emotional Design
Bibliographic record
Abstract
There are disciplines stable and well formalised (e.g. Mathematics or Physics) and disciplines more soft and less formalise (e.g. Design or Literary Critics). While it is quite clear what to teach and how to measure the teaching results in the former, it is not so easy for the latter. Teaching Design means provide the students with a set of knowledge easily definable (e.g. representations, material properties, use of tools, history, etc.) and with a set of capabilities and sensitivities usually transferred from a master to a pupil through behaviours examples (e.g. how to invent new solutions, how to provide aesthetically valuable artefacts, how to convey emotions through artefacts, and so on). The paper will present a set of experiences carried on in teaching Emotional Design, in such a way that the students are simultaneously involved in learning / applying theoretical aspects and in defining / evaluating procedural methods for stimulating creativity and new solutions. The approach involves two parallel lines: the former consists of a set of design steps carried on individually, being each of them a re-design of the previous one , according to new theoretical knowledge acquires; the latter consists in a collective evaluation of the results, both as check of the validity of the theoretical indications and as set up of common practices, considered as fruitful by the en-tire group. So, a course can be organised according the following lines: 1. The disciplinary content is subdivided into well defined parts, each of them self-consistent and applicable without the others; 2. a specific design theme is chosen; 3. an analysis of the existing solutions on the theme are examined; 4. each student is required to provide the sketch/concept of a new artefact related to the theme; 5. for each of the disciplinary parts in which the content has been subdivided: a. a frontal lesson is held to the class; b. each student is required to modify/re-design his/her previous sketch as a consequence of the new knowledge; c. the individual contributions are collected, organised and collectively discussed, evaluating which actions provided the best results; d. the selected practices are properly described; 6. the previous point is repeated for all the parts in which the content has been subdivided, as in point 1; 7. Finally, a “research paper” is written, as collective contribution to a methodological approach in applying the disciplines. The paper will describe the disciplinary content of Emotional Design, will illustrate the various parts for the subdivision of the content, and will show the results, both from the point of view of the design results and of the selected good practices according to the discussions. The experiences have been carried on in different courses for graduation in Design, and for post-graduated students, as well as for PhD students. The experience of the postgraduated students will ne described in detail, while only some references will be given for the others. Critical remarks will complete the paper..
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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 | 0.017 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| 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".