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Record W2598667874 · doi:10.24908/pceea.v0i0.6462

EVOLUTION OF THE DESIGN ENGINEERING MENTORSHIP PROGRAM

2017· article· en· W2598667874 on OpenAlexafffundvenue
Kush Bubbar, Alexandros Dimopolous, Roslyn Gaetz, Peter Wild, Michael McWilliam

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2017
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Victoria
KeywordsMentorshipCoachingContext (archaeology)CertificationExperiential learningCreativityEngineering managementCapstoneComputer scienceProgram Design LanguageMedical educationEngineering ethicsEngineeringSoftware engineeringPsychologyMathematics educationManagementMedicine

Abstract

fetched live from OpenAlex

The Design Engineering Mentorship Program (DEMP) is a five-day intensive training program focused on developing appropriate competencies in graduate students required to effectively teach engineering design at the undergraduate level.Evolution of the present program is discussed in context of feedback and observations from the now defunct Design Engineering & Instruction program. The structure of the procedural based DEMP program is fully described including new experiential based workshops on creativity and coaching led by a PCC certified coach.Motivating factors and implementation details of each of the workshops are described in detail in context of the competencies attributed to a design instructor.The first instance of the DEMP program will be offered in September 2016.

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)0.012
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0030.002
Open science0.0030.006
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0220.005

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 head0.008
GPT teacher head0.203
Teacher spread0.195 · 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 designNot applicable
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

Citations5
Published2017
Admission routes3
Has abstractyes

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