On Becoming an Engineer: The Essential Role of Lifelong Learning Competencies
Bibliographic record
Abstract
Similar to the ABET EC-2000 3a-k learning outcomes, one of the required attributes within the accreditation framework developed by the Canadian Engineering Accreditation Board (CEAB) is lifelong learning.It is a competency defined by CEAB as an ability to identify and to address [students'] own educational needs in a changing world in ways sufficient to maintain their competence and to allow them to contribute to the advancement of knowledge.It is an attribute that is often held up as an exemplar demonstrating the difficulties inherent in assessing the graduate attributes, particularly the ones that reflect the professional or workplace skills of engineers.Some consider lifelong learning an outcome best measured a priori: in other words, it is cogitated as an aptitude that students will best epitomize once they are graduated and working as professional engineers.However, the knowledge, skills, behaviours, attitudes and values that engender lifelong learning are indeed present in our students, and one of the most effective ways to activate and observe this attribute is to engage students in discussions regarding their experiences and perceptions of their learning.This paper presents the findings from a qualitative directed content analysis of two interviews and four focus groups, as 13 student participants discuss their learning experiences within their engineering programs in the Faculty of Engineering at the University of Manitoba, a large research university in Central Canada.Students' lifelong learning aptitudes, which are defined in this study by Deakin Crick Et al.'s seven Dimensions of Learning Power, are evidenced in the data, demonstrating both the capacity of, and the means by which to assess this attribute while students are in our programs.Additionally, we can use students' developing competencies in lifelong learning to improve our own understanding of how students transform into becoming engineers.This paper makes a case for keeping lifelong learning as a required outcome and graduate attribute for our engineering students, and advocates for careful deliberation regarding the definition of lifelong learning, especially in regards to the recently proposed changes to ABET EC-2000 Criteria 3.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".