Understanding the role of designers' personal experiences in interaction design practice
Bibliographic record
Abstract
Using designers' personal experiences in interaction design practice is often questioned in a predominantly rationalist practice like HCI and professional interaction design. Perhaps for this reason, little work has been conducted to investigate how designers' personal experiences can contribute to technology design. Yet it's undeniable designers have applied their personal experiences to their design practice and also benefited from such experiences. This paper reports on a multiple case study that looks at how interaction designers worked with their personal experiences in three industrial interaction design projects, thus calling for the need to explicitly recognize the legitimacy of using and better support of the use of designers' personal experiences in interaction design practice. In this study, a designer's personal experiences refer to the collections of his/her individual experiences derived from his/her direct observation or past real-life events and activities, as well as his/her interaction with design artifacts and systems whether digital or not.
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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 | 0.028 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".