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Record W3036584948 · doi:10.24908/pceea.vi0.14143

COMPARISON OF THE CIVIL ENGINEERING CURRICULUM AMONG SEVERAL CANADIAN UNIVERSITIES

2020· article· en· W3036584948 on OpenAlexaffvenueabout
Mohammad Mehdi Ebadi, Michele Richards, Carol Brown, Samer Adeeb

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2020
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsWindsorCurriculumAccreditationCapstoneEngineeringEngineering managementMultidisciplinary approachEngineering educationEngineering ethicsProcess (computing)Modular designLibrary scienceSustainabilityCivil engineeringSociologyMedical educationPedagogyComputer scienceEnvironmental scienceMedicineSocial science

Abstract

fetched live from OpenAlex

Growing attention to environmental sustainability, modular construction, and application of new generation of materials, accompanied with advanced data collection techniques and computer modeling, has revolutionized the area of Civil Engineering within the past few years. This demonstrates the necessity of continually reviewing the curriculum to assure that graduating engineers are knowledgeable enough to deal with complex problems in their area of specialty. This is also essential to satisfy the continual improvement process (CIP) requirements mandated by the Canadian Engineering Accreditation Board (CEAB). As a first step to design a rigorous CIP, a comprehensive comparison was made between the Civil Engineering curricula of the University of Alberta (UofA) and eight other major universities across Canada, including the University of Calgary, University of Toronto, McGill University, University of Windsor, University of Regina, University of British Columbia (UBC), University of Waterloo, and Polytechnic of Montreal. After categorizing the courses into twelve different streams, it was observed that some universities paid less attention to a specific stream in comparison with the average, which could be identified as a gap in the curriculum. A capstone design or group design project that is multidisciplinary and covers multiple areas of specialty is the predominant approach followed by most of the universities.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.198
Threshold uncertainty score0.971

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.000
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 head0.005
GPT teacher head0.186
Teacher spread0.181 · 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 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

Citations2
Published2020
Admission routes3
Has abstractyes

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