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Record W2977749784 · doi:10.24908/pceea.v0i0.13072

A snapshot of engineering education in Canada

2018· article· en· W2977749784 on OpenAlexafffundvenueabout
Nancy J. Nelson, Robert Brennan

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2018
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsUniversity of Calgary
FundersNatureNatural Sciences and Engineering Research Council of CanadaUniversity of British ColumbiaUniversité de MonctonUniversity of Calgary
KeywordsAccreditationEngineering educationCurriculumMedical educationEngineeringEngineering managementSnapshot (computer storage)Engineering ethicsPedagogyPsychologyMedicineComputer science

Abstract

fetched live from OpenAlex

Although all accredited engineering programs in Canada are assessed by the same governing body, each institution has its own set of expectations regarding its distribution of effort, the types of research conducted by its faculty, and the way it delivers its curriculum. Individual departments and programs each have their own strengths and challenges, but collectively they share the responsibility of educating tomorrow’s engineers.This paper presents a summary of the results of a descriptive study examining three aspects of engineering education in Canada: the balance and types of research, teaching, and service that engineering educators are doing, the level to which engineering educators are engaging with engineering education research, and the look and feel of the learning environment that undergraduate engineering students experience in accredited engineering programs in Canada.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.881
Threshold uncertainty score0.863

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.018
Science and technology studies0.0140.001
Scholarly communication0.0070.001
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0230.002

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 head0.003
GPT teacher head0.171
Teacher spread0.168 · 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 designObservational
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

Citations16
Published2018
Admission routes4
Has abstractyes

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