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Record W2156758862 · doi:10.4018/ijqaete.2012040104

Mapping the Relationship Between the CDIO Syllabus and the CEAB Graduate Attributes

2012· article· en· W2156758862 on OpenAlexaffabout
Guy Cloutier, Ronald J. Hugo, Rick Sellens

Bibliographic record

VenueInternational Journal of Quality Assurance in Engineering and Technology Education · 2012
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsQueen's UniversityUniversity of CalgaryPolytechnique Montréal
Fundersnot available
KeywordsCDIOSyllabusAccreditationEngineering educationComputer scienceEngineering managementSet (abstract data type)EngineeringSoftware engineeringMathematics educationMedical educationMathematicsProgramming languageMedicine

Abstract

fetched live from OpenAlex

The recently introduced Canadian Engineering Accreditation Board (CEAB) requirements for Graduate Attributes require demonstrated learning outcomes for the first time. The Conceive, Design, Implement, Operate (CDIO) approach includes a set of outcomes in the form of the CDIO Syllabus. The Syllabus also provides guidance on how to document outcomes in order to meet the requirements of the CEAB Graduate Attributes. This article provides a framework for Canadian engineering programs to satisfy the CEAB requirement to demonstrate learning outcomes through a mapping of the CDIO Syllabus topics to the CEAB Attributes, and verification of the completeness of that list. An engineering program can meet all of the CEAB Graduate Attribute requirements by addressing a subset of the CDIO syllabus; however, a CEAB accredited program may not meet all of the CDIO standards.

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)0.007
metaresearch head score (Gemma)0.077
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.176
Threshold uncertainty score0.351

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.077
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.005
Science and technology studies0.0020.002
Scholarly communication0.0070.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.002

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 head0.050
GPT teacher head0.302
Teacher spread0.252 · 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 designQualitative
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

Citations18
Published2012
Admission routes2
Has abstractyes

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