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Record W1835372570 · doi:10.24908/pceea.v0i0.3686

DESIGN-BUILD PROJECT APPROACH IN A FIRST YEAR ENGINEERING DESIGN COURSE

2011· article· en· W1835372570 on OpenAlexafffundvenue
George Platanitis, Remon Pop‐Iliev

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2011
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsOntario Tech University
FundersNatural Sciences and Engineering Research Council of CanadaGeneral Motors of CanadaUniversity of Ontario Institute of Technology
KeywordsContext (archaeology)Computer scienceRepresentation (politics)Course (navigation)Function (biology)Space (punctuation)Software engineeringEngineering design processCADHuman–computer interactionSystems engineeringEngineering drawingEngineering

Abstract

fetched live from OpenAlex

We found that first-year engineering students often have difficulties to visualize and manipulate three-dimensional objects mentally, especially if the assembly involves multiple parts that need to work together in sequence to produce a required function. Ultimately, this lack of ability leads to poor representation of intended students’ design concepts in paper sketches, as well as poor or unacceptable detailed designs in CAD. Therefore, it is imperative that students develop their ability to manipulate complex objects in space very early in their academic careers. In this context, this paper focuses on the introduction and implementation of a challenging design-build project in the first-year engineering design course at UOIT intended to provide students with early opportunities to physically realize the spatial relationships and the three dimensional causality of the interaction of moving parts in an assembly.

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 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 score0.546
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.024
GPT teacher head0.213
Teacher spread0.189 · 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

Citations4
Published2011
Admission routes3
Has abstractyes

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