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Record W2909548101 · doi:10.24908/pceea.v0i0.13032

Use of a roleplaying exercise to illustrate design stakeholder roles in a first-year design course

2018· article· en· W2909548101 on OpenAlexaffvenue
Juan Abelló, Alys Avalos-Rivera, Saloome Motavas, Vladan Prodanovic, Sandra Zappa‐Hollman

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicProblem and Project Based Learning
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsStakeholderSustainabilityMedical educationProcess (computing)Course (navigation)Mathematics educationPsychologyEngineeringManagementSociologyPedagogyPolitical sciencePublic relationsComputer scienceMedicine

Abstract

fetched live from OpenAlex

VANT 150 is a first-year course in designand sustainability for international students enrolled inthe Vantage One program at UBC. One of its learningoutcomes is to understand the importance ofcommunication between different stakeholders in thedesign process.The 2016 final exam revealed that students haddifficulties understanding the positions of the designer,client and user as stakeholders. A simulation(roleplaying) exercise was implemented in 2017 in orderto help students better understand these roles and raiseawareness about the importance of communicationbetween them.The 2016 and 2017 final exams included a question todifferentiate stakeholder roles. We found that the averagescore in this question was 8% higher in 2017 than in2016. This difference is statistically significant withp < 0.005. This suggests the stakeholder simulationactivity helped our students better understand theseconcepts.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.014
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0020.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0130.002

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.

Opus teacher head0.049
GPT teacher head0.258
Teacher spread0.209 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2018
Admission routes2
Has abstractyes

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