Experiential Learning to Teach User Experience in Higher Education in Past 20 Years: A Scoping Review
Bibliographic record
Abstract
Experiential learning is an effective method to teach User Experience (UX) to Human-Computer Interaction (HCI) students. Despite its popularity, there seems to be no comprehensive overview on (1) the current use of experiential learning in UX education at universities and (2) student learning outcomes and benefits resulting from the use of experiential learning. Hence, we conducted a scoping review to provide such overview. We analyzed 45 articles published from 2000 to 2021 and we found 12 types of experiential learning employed by HCI educators: applied research project, industry/community research project, hands-on activity, role-play, interactive workshops, guest speakers, in-house work placement, internship, flipped classroom, field project, lab, and design hackathon, from most to least frequent. Twenty-six articles reported student learning outcomes and benefits: (1) enhanced UX technical knowledge, (2) applied textbook knowledge into practice, (3) acquired soft skills, (4) student satisfaction, (5) increased awareness of user diversity, and (6) increased job marketability. Overall, we advance current HCI teaching practices by providing HCI educators with a list of experiential learning types that they can adopt in their classes to teach UX.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".