TEACHING AND ASSESSING “LIFELONG LEARNING” IN ENGINEERING COMMUNICATION COURSES
Bibliographic record
Abstract
Recent changes to CEAB’s accreditation process have resulted in the need for engineering programs in Canada to find ways to assess critical graduate attributes. While many of the attributes can be measured through traditional methods, others are more subtle and challenging to assess. One that can be particularly challenging both to teach and assess is lifelong learning. As its name suggests, lifelong learning is a process that begins before and continues after a person’s formal education; it is a learner-initiated activity or habit of mind. As such, educators must develop ways to ensure that students understand the importance of learning itself, both during and after their formal engineering studies.Technical Communication courses are excellent vehicles for delivering and reinforcing the skills and competencies associated with lifelong learning. This paper will explore how “Lifelong Learning” as a CEAB graduate attribute can be taught and assessed in communication courses (APSC 176 and APSC 201) housed in an engineering program at UBC’s School of Engineering. This paper will also explore the next steps in developing appropriate metrics for determining the success of these courses in meeting this element of accreditation.
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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 | 0.012 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".