Unveiling the Hidden Synergy Between Extra-Curriculars and Engineering Education within the Framework of CEAB Graduate Attributes: A Personal Reflection
Bibliographic record
Abstract
Navigating undergraduate engineering education can be a challenging process, with students facing a multitude of expectations and pathways towards graduation. While core elements like courses are central, the significance of "extra-curricular" opportunities in enriching the learning experience is often undervalued. This paper challenges the notion of these activities as "extra" by examining their role in developing skills aligned with the 12 Canadian Graduate Attributes. Through an analysis of literature and a case study of a first-in-the-family female engineering student, this paper explores the skills that engineering students can acquire through extra-curricular involvement through a personal reflection. Furthermore, it investigates the relationship between these skills and the Canadian Engineering Graduate Attributes, aiming to redefine the connection between curricular and extra-curricular activities. Preliminary findings suggest a link between extra-curricular engagement and students' personal and academic development, yet there is a gap in understanding how these activities align with the Canadian Engineering Graduate Attributes. This study aims to bridge this gap, continuing the conversation on the importance of extra-curricular activities in engineering education. By redefining the role of extra-curricular activities, this research seeks to promote their integration into the curriculum, encouraging broader student participation and fostering learning beyond the classroom.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.027 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| 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".