Consideration of the Design Attribute in the 2008 CEAB Accreditation Procedures
Bibliographic record
Abstract
The 2008 Canadian Engineering Accreditation Board (CEAB) "Accreditation Criteria and Procedures" document introduces a significant change in the engineering accreditation process for Canadian engineering programs.Under this new system, each program must demonstrate that students meet specific outcomes for each of twelve attribute criteria specified in the document.In July 2009, the collective NSERC Chairs in Design Engineering were asked by the National (Canadian) Dean's Education Committee to review and comment on the new CEAB accreditation process, particularly with respect to the design attribute.After some preparatory work, a small group of chair-holders and an expert in teaching and learning outcomes assessment met for two days in September to respond to this task.A "design process" approach was determined to be the best way to describe the design attribute by developing a series of high level and lower level outcome statements for each step in the generic engineering design process.It was also clearly determined that design competency could not be described as an attribute separate from the other eleven attributes listed in the CEAB document.In fact, all of the other eleven attributes were inextricably wound into engineering design.This paper will describe the systematic outcomes-based process applied to approach this issue, as well as provide an overview of the results.
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 | 0.063 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".