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Record W4412870805 · doi:10.24908/pceea.2025.19742

Assessing Whether Engineering Students Are People-Focused or Thing-Focused

2025· article· en· W4412870805 on OpenAlexafffundvenue
Halal Ozhakkal Latheef, Libby Osgood, Chris Power

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2025
Typearticle
Languageen
FieldComputer Science
TopicPersona Design and Applications
Canadian institutionsUniversity of Prince Edward Island
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPsychologyEngineering ethicsEngineering

Abstract

fetched live from OpenAlex

Engineers are often perceived as focusing more on the technical aspects of a product instead of on the users. Design methodologies such as human-centred design and co-design attempt to shift this focus from the artifact or device to the people who require the design. This study aims to develop a methodological approach to examine structured reflections to assess whether a student is people-focused or thing-focused. The weekly reflections of two summer students working on an accessibility project were categorized on a four-point scale and further analyzed based on the project phases. The results revealed that as the project progressed, there was a statistically significant shift in focus from people to things (p <.05). Individual differences were also observed, with one participant showing a stronger focus on client requirements while the other emphasized user needs. The findings suggest that engineering education could incorporate approaches promoting people-focus throughout the design process, particularly in later stages, to improve usability and technical feasibility. As this is a pilot study intended to develop a methodological approach, the small sample limits the generalizability of the findings, and future research will expand the sample size and validate an instrument.

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 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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.305
Threshold uncertainty score0.751

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.011
GPT teacher head0.253
Teacher spread0.241 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2025
Admission routes3
Has abstractyes

Explore more

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