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Record W3094920229 · doi:10.1115/detc2020-22730

Paired Computer-Aided Design: The Effect of Collaboration Mode on Differences in Model Quality

2020· article· en· W3094920229 on OpenAlexaff
Hamza Arshad, Vrushank Phadnis, Alison Olechowski

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCADComputer scienceQuality (philosophy)Task (project management)Mode (computer interface)Control (management)Computer Aided DesignHuman–computer interactionProduct (mathematics)Product designEngineering drawingSystems engineeringArtificial intelligenceEngineering

Abstract

fetched live from OpenAlex

Abstract We present the results of an experiment investigating two different modes of collaboration on a series of computer-aided design (CAD) tasks. Inspired by the pair programming literature, we anticipate that partners working in a fully synchronous collaborative CAD environment will achieve different levels of quality in CAD models depending on their mode of collaboration — one in which the pair is free to work in parallel, and another where the pair must coordinate to share one control. We found that a shared CAD control led to significantly better overall CAD quality than parallel CAD control. In addition, the shared control mode led to more complete and consistent CAD models, as well as the tendency for participants to follow instructions to correctly replicate features for the design task. As is predicted in the literature, a trade-off relationship (albeit weak) between quality and speed via the parallel collaboration was found. In contrast, the shared control mode shows no clear relationship between speed and quality. Collaborative CAD is increasingly seen as an appealing tool for modern product design teams. This study suggests that the benefits of this tool are not solely the effect of the tool itself, but result from the collaboration style of the designers using the tool.

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)0.000
metaresearch head score (Gemma)0.000
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: none
Teacher disagreement score0.550
Threshold uncertainty score0.196

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.090
GPT teacher head0.321
Teacher spread0.231 · 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 designSimulation or modeling
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

Citations2
Published2020
Admission routes1
Has abstractyes

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