Determining the Dependencies of Engineering Competencies for Engineering Practice: An Exploratory Case Study
Bibliographic record
Abstract
Abstract Similar to the changes brought about by ABET’s adoption of student learning outcomes, changes to engineering accreditation requirements in Canada in 2009 initiated a shift to outcomes-based education and emphasis on continual program improvement. A list of 12 attributes was introduced – competencies that can be mapped on to the ABET General Criterion 3: Student Outcomes. The Canadian Engineering Accreditation Board (CEAB) requires all students graduating from CEAB accredited engineering programs demonstrate these competencies. This has presented a challenge: how best to teach and assess them? Generally, the CEAB graduate attributes were/are accepted as offered: 12 individual competencies, with emphasis often placed on the first listed – Engineering Knowledge, Problem Analysis, Investigation, Design, and Engineering Tools – the more ‘traditional’ engineering skills – even if this emphasis was not intended by CEAB. In fact, research in the field indicates that teamwork and communication skills – competencies found in the ‘middle’ of the list – are top competencies for engineering practice. Additionally, the need to investigate potential clusters of competencies has been emphasized in this research, identified as a gap in both engineering education and research. Considering the research, and motivated to inform engineering education curricular design and improvement at the University of Manitoba, an exploratory case study was designed in part to investigate how the CEAB graduate attributes cluster for a new engineer in engineering practice as perceived by key engineering stakeholders. The data consisted of perceived similarities between each possible pair of graduate attributes collected from engineering student, faculty and industry stakeholders. Multidimensional scaling analysis showed that the 12 graduate attributes can be conceptualized as four clusters, which we have suggested be titled, Problem Solving Skills, Interpersonal Skills, Ethical Reasoning, and Creativity and Innovation. These findings, supported by the relevant literature, highlight the need to further explore how engineering competencies cluster in practice to add empirical support for program changes aimed toward 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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".