A REVIEW OF HOW LIFELONG LEARNING IS PLANNED AND ENACTED IN CANADIAN ENGINEERING PROGRAMS
Bibliographic record
Abstract
Lifelong learning is a vital competency for navigating contemporary industry and society. Canadianengineering programs are expected to instill lifelong learning in students by the time they graduate. While lifelong learning initiatives, interventions, and assessment instruments are often proposed, there is no clear consensus on how the graduate attribute should be addressed at the program level. As a first step towards a possible consensus, this research investigates how the lifelong learning graduate attribute is operationalized in Canada. By reviewing indicator definitions and scholarly research, we answer three questions: 1) How do different engineering programs in Canada define lifelong learning in terms of knowledge, skill-based, and attitudinal indicators? 2) How do different engineering programs in Canada enact content, pedagogy, and assessment to influence the development of lifelong learning in students? and 3) How does this compare toconceptualizations of lifelong learning in the broader education literature? This review found different interpretations of the CEAB definition of lifelong learning: some institutions emphasize self-directed learning skills, including identifying and addressing knowledge gaps, while few emphasize attitudes such as curiosity and commitment to lifelong learning. Many Canadian instructors are taking steps to address lifelong learning in their teaching practices, but deliberate teaching and assessment of lifelong learning across programs is rare. Compared to lifelong learning frameworks in the broader literature, Canadian programs appear to emphasize lifelong learning skills and some metacognitive knowledge over attitudes. Skills are likely easier to defineas indicators and assess, but it is unclear whether a focus on skills leads to an internalized lifelong learning orientation.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.030 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".