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Record W2119709930 · doi:10.24908/pceea.v0i0.4801

IMPROVING OUTCOMES IN STUDENT DESIGN COURSES THROUGH QUALITATIVE USER RESEARCH AND CONTEXTUAL IMMERSION

2013· article· en· W2119709930 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2013
Typearticle
Languageen
FieldComputer Science
TopicPersona Design and Applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMindsetQualitative researchPerspective (graphical)User experience designContext (archaeology)Computer scienceImmersion (mathematics)Participatory designHuman–computer interactionEngineeringSociology

Abstract

fetched live from OpenAlex

Shifting from the course-based mindset into the real-world context of the user is a challenge that students often face during design courses. This can result in designs and proposed solutions that do not fully meet the technical and business needs of the client. This paper proposes a greater use of qualitative methods, paired with a deep immersion in the user environment, and highlights the value in design education through a case study example. A focus on qualitative user-studies in the discovery phase of design helps to give students perspective on the unique characteristics of users and the design context. The Engineers in Scrubs Program, in collaboration with the Uganda Sustainable Trauma Orthopaedic Program (USTOP), at the University of British Columbia is highlighted as one such example.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.683
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.045
GPT teacher head0.336
Teacher spread0.291 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it