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Record W2105099440 · doi:10.24908/pceea.v0i0.3913

IMPROVEMENTS TO A UNIVERSITY CAPSTONE DESIGN PROGRAM THROUGH THE USE OF INDUSTRIAL MENTORS AND INCREASED MILESTONE DELIVERABLES

2011· article· en· W2105099440 on OpenAlexaffvenue
J. Mikkelsen, I. Yellowley, Antony J. Hodgson, P. Cramond, David McAdam

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2011
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Pedagogy
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMilestoneDeliverableCapstoneEngineering managementWork (physics)EngineeringRelevance (law)Medical educationComputer scienceSystems engineeringMechanical engineeringMedicine

Abstract

fetched live from OpenAlex

This paper reviews some recent major changes made to the Senior Mechanical Engineering Capstone Design Program at UBC. The program now consists of a two-term senior level design sequence where student teams work on open-ended design problems sponsored by outside clients. In order to reinforce relevance and ensure that practices parallel those of industry, the Department recruited local senior engineers to serve as engineering mentors to the students and work in concert with the course instructors. Several milestones were established during the duration of the program year to reinforce good design practice beginning from an agreement on client needs and proceeding through concept generation, selection, analysis and finally ending with prototype construction and evaluation. The paper highlights the improvements made to the program as a result of these changes and presents an example of a student design project developed under the new model.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.019
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.995
Threshold uncertainty score0.102

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.030
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0030.001
Scholarly communication0.0040.002
Open science0.0030.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0160.006

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 head0.054
GPT teacher head0.218
Teacher spread0.164 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2011
Admission routes2
Has abstractyes

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