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Record W2592363726 · doi:10.24908/pceea.v0i0.6479

A Case Study in Incorporating Significant Design Content into a Third-Year Industrial Engineering Course “Design and Analysis of Production Systems”

2017· article· en· W2592363726 on OpenAlexaffvenueabout
Scott A.C. Flemming

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2017
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsDalhousie University
Fundersnot available
KeywordsContext (archaeology)Industrial designCourse (navigation)Nova scotiaFeelingEngineeringEngineering managementEngineering ethicsMathematics educationPsychologySociologyMechanical engineering

Abstract

fetched live from OpenAlex

In recent years the CEAB has ben communicating to Engineering Faculties in Canada that “Engineering Design” is a key attribute that graduates should have when they finish their undergraduate degree. It hasalso been suggested that producing engineers with significant design skills is important for the Canadian economy as a whole and, in Dalhousie University’s context, Nova Scotia. Unfortunately “Design” is adifficult skill to teach or transfer; a recent article in Maclean’s suggests many engineering graduates around the country are leaving the university with an uneasy feeling that all they have been taught to dois “plug and chug.” How do we respond to this need? This paper offers a case study of how a third-year Industrial Engineering course shifted from a mainly book-and-formula based course to an offering which incorporated significant open-ended design content (25%) intended to both satisfy CEAB requirements and address the need for students to exercise their creative, hands-on problem-solving skills. Student project outcomes as well as anecdotal and SRI data suggest the shift to a design-focussedcourse was a success.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.134
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.035
GPT teacher head0.233
Teacher spread0.198 · 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

Citations0
Published2017
Admission routes3
Has abstractyes

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