Student Development of Lifelong Learning Orientations Within and Beyond Undergraduate Engineering Programs
Bibliographic record
Abstract
Competency as a lifelong learner can enable engineering students to exercise agency in their careers. Despite the prominence of the graduate attribute in accreditation processes, undergraduate programs are still trying to understand how curriculum can support lifelong learning. Large-scale alumni surveys rarely investigate learning throughout careers, or the lasting influences of different elements of the undergraduate experience on these journeys. Studies have seldom investigated longer-term effects of engineering curriculum especially for individuals’ attitudinal characteristics (lifelong learning orientations). This mixed-methods study of University of Toronto alumni aimed to identify program-level curriculum practices that facilitate or hinder lifelong learning in career trajectories. In the first stage, exploratory semi-structured interviews (n = 24) were used to apply integrated research lenses to the local setting and narrow the scope of curricular practices and lifelong learning characteristics for further investigation. In the second stage, a broader alumni survey (n = 279) measured relationships between lifelong learning orientations, curriculum factors, and individual characteristics including comparisons across different engineering programs. In the third stage, interviews with survey participants (n = 12) were implemented to understand individual learning journeys. Sequential and mixed analyses identified subtle differences in lifelong learning patterns for alumni of different programs, overshadowed by individual differences. 28% of alumni across programs reported increased interest-driven motivation and 43% reported reduced failure-avoidant motivation in the present compared to the undergraduate setting. Alumni are also motivated to achieve mastery and/or make a positive impact. Careers demand contextual adaption of problem-solving or design-related skills, further development of professional competencies, and, notably, an ability to make sense of physical and social systems by applying lenses that extend beyond traditional engineering paradigms. Curriculum content and learning experiences need to be re-envisioned from day one to incentivize connective understanding across different disciplinary perspectives. This will ensure alumni can self-direct their learning towards addressing all facets of sociotechnical problems, engage in interdisciplinary workplace collaborations, and manage career challenges and changes. Relying on entrenched curricula to develop lifelong learners does not serve all students; engineering programs need to make adjustments and coach students deliberately to develop beneficial lifelong learning orientations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".