MétaCan
Menu
Back to cohort
Record W1953616297 · doi:10.24908/pceea.v0i0.5758

ENCOURAGING EMPATHY IN ENGINEERING DESIGN

2015· article· en· W1953616297 on OpenAlex
Holly R. Algra, Clifton R. Johnston

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.
fundA Canadian funder is recorded on the work.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2015
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEmpathyEngineering design processCreativityGenerative grammarExperiential learningPerceptionMerge (version control)Computer scienceKnowledge managementEngineeringPsychologyEngineering ethicsSocial psychologyMathematics educationArtificial intelligence

Abstract

fetched live from OpenAlex

Empathy is imperative for the creation of user-friendly products, and can be both taught and learned according to Jon Kolko in his book, “Well-Designed”. In it, he suggests that successful design requires the integration of human factors and an empathy with the users. However, in such statements as “the left circle is engineering, the right circle is design” and “engineering is a reductive activity… design, however, is frequently a generative activity” seem to imply that engineering does not overlap with design. Section headings including “Motivating Engineers”, and “How do you bridge the …gap between engineers and designers?” also strengthen the idea that engineering and design are not performed by the same people. Much of the literature on human factors implies that engineers are analytical, solution-oriented, and thorough. However, creativity and human considerations seem to have been left to someone else, or pushed to the end of the design process as a last-minute add-on. In this work, we focused on how to change this perception by helping engineers to better integrate human factors and empathy into their design processes.We have been exploring potential approaches that could encourage the two seemingly disparate worlds to merge together. After an initial design project with a focus on incorporating experiential learning and human factors did not achieve the expected outcomes, it was clear that encouragement and intentions were not enough to integrate empathetic principles into engineering design. Our research included analyzing different product choices based on experience in a specific area, and a case study to identify the source of human consideration in a capstone design project. This has culminated in the idea that a tool needed to be created to help novice designers introduce human factors into the early stages of their design process.We avoided making a checklist which could be completed with no real consideration for the user. Instead, we created a prototype of an application which we believe would help spark discussion and ideation, while interacting with designers on a platform that is accessible and recognizable. In this paper, we will describe the development activities that were required for this tool as well as the additional work needed to create an operational application for multiple operating platforms. In addition, we will discuss how we believe this will influence the incorporation of human factors into the design processes of novice designers and in which applications we believe this will be the most useful.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.168
Threshold uncertainty score0.919

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.015
GPT teacher head0.211
Teacher spread0.195 · 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