Determining the relative importance of the CEAB graduate attributes for engineering: An exploratory case study at the University of Manitoba
Bibliographic record
Abstract
Changes to engineering accreditation requirements in Canada in 2009 initiated a shift to outcomes-based education and continual program improvement. The 12 CEAB graduate attributes were introduced – competencies all graduating engineers are required to demonstrate. They presented a conundrum: how best to teach and assess them? This, coupled with resolve to educate engineers to tackle 21st century problems, characterized one driver in the emerging global discipline of Engineering Education. Generally, the CEAB graduate attributes are accepted as presented: individual competencies with emphasis on the first listed, the more ‘traditional’ skills. However, research in the field indicates that teamwork and communication skills are top competencies for engineering practice, and suggest attribute clusters. These findings diverge from the implied ranking and individual treatment of the CEAB graduate attributes. Considering the research, and motivated to inform engineering curricular improvement at the University of Manitoba, this doctoral study was designed to investigate the relative importance of the CEAB graduate attributes, and how they cluster in engineering practice as perceived by engineering stakeholders. The content validity of the Biosystems Engineering program was then evaluated. Findings showed that stakeholders ranked Individual and Teamwork and Communications Skills as the top engineering competencies, and all graduate attributes were between 6.1% - 10.9% relatively important, in sharp contrast to the Biosystems program, which is comprised of almost 50% Knowledge Base for Engineering. Findings demonstrated students placed more emphasis on value attributes than faculty or industry stakeholders, a perception worth exploring to diversify engineering populations. Furthermore, the graduate attributes can be conceptualized as four new clusters, renamed Problem Solving Skills, Interpersonal Skills, Ethical Reasoning, and Creativity and Innovation, and can be theorized using Bloom’s three Domains of Cognitive, Psychomotor, and Affective Learning. The Biosystems Engineering Program is already designed to accommodate curricular changes to improve content validity. This research also informs curricular improvements for the greater faculty, and accredited engineering programs across Canada. Overall, the findings are supported by the literature, and stress the negligence of artificially separating engineering competencies, particularly into dichotomous ‘traditional’ and ‘professional’ skills, and encourage a paradigmatic shift towards thinking about, and educating, the whole engineer.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".