Investigating Canadian Engineering Students’ Perceptions of Graduate Attributes: Frequency, Criticality, and Relative Importance
Bibliographic record
Abstract
This study investigates the perceptions of Canadian engineering students regarding the frequency and criticality of the 12 graduate attributes (knowledge, skills, values, and behaviors that engineering students are expected to demonstrate upon graduation) outlined by the Canadian Engineering Accreditation Board (CEAB). This study aims to assist engineering educators in gaining a better understanding of students’ expectations regarding how engineering competencies will be demonstrated in practice. This information can guide the improvement of engineering curricula and help engineering programs meet accreditation requirements for continuous enhancement. Descriptive and test statistics were used to analyze a quantitative survey administered to 340 undergraduate engineering students at a large Canadian university. Findings suggest that the students perceived the frequency and criticality of most graduate attributes differently. Individual and teamwork, communication, professionalism, lifelong learning and engineering tools, were viewed as more frequent than critical, while ethics and equity, impact of engineering, investigation, and design were perceived as more critical than frequent. The study also found that communication, individual and teamwork, and problem analysis were perceived as the graduate attributes with the highest relative importance (frequency multiplied by criticality), which is consistent with the literature.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".