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Record W4410632567 · doi:10.22215/etd/2025-16466

Boundary Objects: Exploring the Benefit of Using Iconic Design Objects in Teaching Colour, Material, and Finish (CMF) in Undergraduate Industrial Design Education

2025· dissertation· en· W4410632567 on OpenAlexaff
Nathalie Cecile-Marie Tambay

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldComputer Science
TopicDigital Media and Visual Art
Canadian institutionsCarleton University
Fundersnot available
KeywordsBoundary objectBoundary (topology)Visual artsEngineering drawingIndustrial designMathematics educationEngineeringArchitectural engineeringComputer scienceMultimediaHuman–computer interactionComputer graphics (images)SociologyArtPsychologyMathematicsSocial science

Abstract

fetched live from OpenAlex

This study explores the benefits of using Iconic Design Objects as Boundary Objects in teaching Colour, Material, and Finish (CMF) Design in undergraduate Industrial Design Education. Teaching the methods and tools of a newly emergent discipline such as CMF design presents challenges in how to introduce theory from similar disciplines without blurring the progress of their differentiation. Explorative and formative research into the discipline of CMF preceded qualitative research of observing undergraduate students using the xDX teaching collection of chairs, producing CMF design products, and a thematic analysis of the student reflection papers at the conclusion. Student projects were presented before a panel of CMF experts to gain insights into the experience through a discussion. The results support the use of a historical collection objects as boundary objects in the transfer of theoretical knowledge to application and offered insights into the benefits of using boundary objects in design education.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.828
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.107
GPT teacher head0.315
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 teacher head, not a consensus.

Study designOther design
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 routes1
Has abstractyes

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