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Record W2126763788 · doi:10.24908/pceea.v0i0.3628

Progress on Defining the CEAB Graduate Attributes at Carleton University

2011· article· en· W2126763788 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Learning Practices
Canadian institutionsCarleton University
Fundersnot available
KeywordsAccreditationComputer scienceProcess (computing)Context (archaeology)Component (thermodynamics)Taxonomy (biology)Noun phraseHierarchyKnowledge managementNounArtificial intelligenceProgramming languagePolitical science

Abstract

fetched live from OpenAlex

The Canadian Engineering Accreditation Board (CEAB) is requiring engineering programs to demonstrate that their graduating students have certain specified attributes beginning in 2014. At Carleton University we have been working on developing our approaches to meeting this requirement for some time. This paper presents some of the aspects of our efforts that appear to be unique. It was important to include in the process coverage of the Ontario government's Undergraduate Degree Level Expectations (UDLEs). After reviewing the UDLEs we created what we are describing as a thirteenth Graduate Attribute – Limits of Knowledge. With the establishment of this attribute both the CEAB and UDLE requirements are covered with a single process.Considerable effort was given to the process for defining competencies (specific and measurable criteria associated with each of the broad attributes) in a clear and functional manner.Our process separates each competency into three components: area of knowledge, expectation levels and context. The area of knowledge is a noun phrase that clearly descrives the specific aspect of the graduate attribute to the beasured. The expectation levels include both threshold and target specifications using the revised Bloom's Taxonomy as a cognitive hierarchy. The final component of each competency is contect which allows each discipline to specify a possibly unique area of application.

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.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.437
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.030
GPT teacher head0.252
Teacher spread0.222 · 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