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Record W2168412156 · doi:10.24908/pceea.v0i0.3949

USING MASTERY LEARNING TO TEACH THE ENGINEERING DESIGN PROCESS

2011· article· en· W2168412156 on OpenAlexafffundvenue
Danny Mann, Kristopher Dick, D. S. Petkau, M. G. Britton, S. Ingram

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2011
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsNatural Sciences and Engineering Research Council of CanadaUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTrilogyCapstoneProcess (computing)Engineering design processComputer scienceDesign processConceptual designEngineering managementMathematics educationEngineeringWork in processPsychologyOperations managementHuman–computer interactionArtificial intelligenceMechanical engineering

Abstract

fetched live from OpenAlex

Capstone design courses are often used to teach the engineering design process. Typically, students have one opportunity to experience the design process before graduating as qualified engineers. The Design Trilogy is a capstone design “course” consisting of three stand-alone courses completed in consecutive years. In Design Trilogy I, students find a conceptual solution to a problem provided by an industry client. In Design Trilogy II, students are expected to reach the analysis stage while working on a new industry problem. Finally, Design Trilogy III students are expected to produce a detailed design, including a cost analysis and prototype (if possible), for a third industry problem. Thus, students have a better opportunity to master the engineering design process because they have three exposures to the early stages of the process.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.282
Threshold uncertainty score0.801

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 science0.0000.000
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 head0.030
GPT teacher head0.231
Teacher spread0.201 · 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

Citations2
Published2011
Admission routes3
Has abstractyes

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