Canadian Engineering Grand Challenges in co-curricular and online environment: Opportunities and Challenges
Bibliographic record
Abstract
Engineering Deans Canada (EDC) recently articulated Grand Challenges that recognize the role of engineers and the specific needs of Canadians in the form of Canadian Engineering Grand Challenges (CEGCs). The CEGCs offer a unique framework to motivate and engage engineering students from different disciplines and encourage collaboration and the sharing of their discipline expertise. The CEGCs also offer a framework for engineering students to develop leadership skills and gain awareness of their technological, innovation and stewardship roles. In this paper, we report on a student-led approach in the online environment for the creation of two workshops and one “Leadathon” case competition related to the CEGCs and leadership skills development. The activities were developed and delivered by a team of engineering students with the support of faculty members. We refer to this student-led model as “for-students-by-students’. Feedback collected from student facilitators and participants indicate that the resulting activities were effective in engaging students and raising awareness of the CEGCs and of their role to address societal problems as future engineers. We present the methodology that was adopted to leverage and take advantage of the online environment, while addressing differences in participant interactions and engagement from the perspective of opportunities and challenges. Finally we discuss potential avenues to integrate into the mainstream curriculum for-student-by-student model related to the interaction with CEGCs.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.024 | 0.007 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".