Beyond Theory: A UX Outcomes Casebook for HCI Education
Bibliographic record
Abstract
The CHI community has expressed a growing interest in creating and sharing educational materials related to User Experience (UX) outcomes, particularly emphasizing summative research. Based on insights gathered at a CSCW 2003 workshop on understanding and evaluating UX outcomes at scale, we identified two areas of focus: (1) the need to develop Human-Computer Interaction (HCI) educational resources for UX, specifically focusing on summative methods and industry practices, and (2) the opportunity to further review and discuss the potential of a casebook—a textbook centered around case studies. This Special Interest Group (SIG) at CHI 2024 aims to directly address these opportunities by bringing together a community of academic and industry researchers for the exchange of ideas, ultimately guiding the development of educational resources that equip HCI students with strong summative research skills as they enter the UX field. At the SIG, we will discuss HCI educational resources for UX outcomes and present a casebook outline, gathering feedback, insights, and interest regarding the proposed case studies and general format.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".