TEACHING AND ASSESSING “LIFELONG LEARNING” IN ENGINEERING COMMUNICATION COURSES
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it