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Record W1762394994 · doi:10.24908/pceea.v0i0.4664

MODELING AN ABBREVIATED PRODUCT DESIGN CYCLE IN A FIRST-YEAR ENGINEERING DESIGN COURSE

2012· article· en· W1762394994 on OpenAlexaffvenueabout
Patricia Sheridan, Jason Foster, Geoffrey S Frost

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2012
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPraxisEngineering design processCornerstoneProduct designProduct (mathematics)Conceptual designComputer scienceProcess (computing)Design educationDesign processEngineering managementEngineeringSystems engineeringWork in processHuman–computer interactionMathematicsOperations management

Abstract

fetched live from OpenAlex

All Engineering Science students at the University of Toronto take the cornerstone Praxis Sequence of engineering design courses. In the first course in the sequence, Praxis I, students practice three types of engineering design across three distinct design projects. Previously the final design project had the students first frame and then develop conceptual design solutions for a self-identified challenge. While this project succeeded in providing an appropriate foundational design experience, it failed to fully prepare students for the more complex design experience in Praxis II. The project also failed to ingrain the need for clear and concise engineering communication, and the students’ lack of understanding of detail design inhibited their ability to make practical and realistic design decisions. A revised Product Design project in Praxis I was designed with the primary aims of: (a) pushing students beyond the conceptual design phase of the design process, and (b) simulating a real-world work environment by: (i) increasing the interdependence between student teams and (ii) increasing the students’ perceived value of engineering communication.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.071
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.017
GPT teacher head0.223
Teacher spread0.206 · 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 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

Citations1
Published2012
Admission routes3
Has abstractyes

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