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Record W1954537253 · doi:10.24908/pceea.v0i0.5740

FIRST YEAR ENGINEERING DESIGN – GUELPH’S TEDDY BEAR WHEEL CHAIR EXPERIENCE

2015· article· en· W1954537253 on OpenAlexafffundvenueabout
Warren Stiver

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2015
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEngineering educationEngineering design processStyle (visual arts)EngineeringComputer scienceEngineering managementMechanical engineeringVisual arts

Abstract

fetched live from OpenAlex

First year engineering design courses arenow common across Canadian engineering schools.These courses can be challenging to develop and deliver.They are often stuck in the chicken versus egg problem.Can I teach design with no engineering? Can I teachengineering with no design? How does one introducefour years of engineering education and an engineeringcareer in one course? How to do so across many or allengineering disciplines? How to do so in a foundationalmanner? Can it be done in a meaningful way? Can it beengaging and fun? A Teddy Bear Wheel Chair (TBWC)design project is the focal point of Guelph’s first yearengineering design course. The TBWC integratescomputers, mechanics, biomechanics (Teddy Bear style),environment, safety, sustainability, materials, costing,hands-on, perseverance, ethics and DESIGN. The TBWCparticipates in curling, sprinting and scoring goals. Theresult is a challenging and fun competition thatintroduces all of Guelph’s engineering students to theirengineering design careers. This paper and presentationwill share one instructor’s efforts to make all of this work.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.412
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.192
Teacher spread0.181 · 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

Citations2
Published2015
Admission routes4
Has abstractyes

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