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Record W2172141735 · doi:10.24908/pceea.v0i0.3143

Engineering Graduate Attributes - Investigation

2010· article· en· W2172141735 on OpenAlexaffvenueabout
Warren Stiver, Andrea Bradford, Sheng Chang, Khosrow Farahbakhsh, David Lubitz, Joanne Ryks, Bill van Heyst, Hongde Zhou, Richard G. Zytner

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2010
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsAccreditationProcess (computing)Context (archaeology)CurriculumMeaning (existential)Engineering design processWork in processComponent (thermodynamics)Work (physics)Element (criminal law)Engineering educationEngineering managementEngineeringComputer scienceEngineering ethicsPsychologyPedagogyMedical educationPolitical scienceMechanical engineeringOperations managementGeography

Abstract

fetched live from OpenAlex

The Canadian Engineering Accreditation Board (CEAB) has added a graduate attributes component to the overall accreditation process.This addition is in part driven by Canada's participation in the Washington Accord.CEAB recognizes that it will take some time for CEAB and Canadian schools to gain comfort and skills with this new element of the review process.In that context, it is not until reviews beginning in 2014 that schools could be considered deficient as a result of the graduate attribute system.The Environmental Engineering group at the University of Guelph has embarked on exploring the Investigation attribute.The Environmental Engineering group's exploration is a work in progress that we are certain will be and should be highly iterative.The group started with defining what the Investigation attribute means in the context of the Environmental Engineering.This was followed by an exercise to create a functional framework that would effectively capture this investigation meaning.The framework chosen is an Investigation Process that has similarities to the Design Process.The Investigation Process obviously differs in being driven by advancing knowledge and understanding.The group is currently surveying current curriculum against this framework.Each Investigation Process element within each course will be judged in terms of reaching the Idea, Connection or Extension level.It is expected that our exploration will lead to a current curriculum map and the development of initial recommendations for the advancement of our students Investigation skill sets.

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)0.026
metaresearch head score (Gemma)0.071
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 score0.080
Threshold uncertainty score0.159

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.071
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.007
Science and technology studies0.0050.002
Scholarly communication0.0090.003
Open science0.0020.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0120.005

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 head0.007
GPT teacher head0.180
Teacher spread0.172 · 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

Citations4
Published2010
Admission routes3
Has abstractyes

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