Progress on Defining the CEAB Graduate Attributes at Carleton University
Bibliographic record
Abstract
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.042 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.018 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".