WHY DOES IT MATTER? EXPLAINING THE IMPORTANCE OF COMPLEMENTARY STUDIES TO FIRST-YEAR ENGINEERING STUDENTS
Bibliographic record
Abstract
While complementary studies are an accreditation requirement and feature prominently in the Canadian engineering curriculum, focus-group conversations with upper-year engineering students have indicated that a lack of awareness of, and appreciation for, soft skills development often prevents students from benefiting from complementary studies to the fullest. Given this reported difficulty to grasp the importance of complementary studies, a study was undertaken at the University of Guelph using a quasi-experimental design to explore the possibility that triggering self-assessment and awareness about career development early in the engineering curriculum promotes greater engagement with complementary studies and soft-skill development. First-year engineering students took part in a learner-centered activity focused on the importance of complementary studies for the development of soft skills. Through active learning exercises and case studies of successful engineering graduates, who described the skills and knowledge required to perform their daily work, the session was designed to encourage students to develop greater self-awareness and intentionality about complementary studies and their associated graduate attributes. The outcomes of this activity and issues on how to embed it in the Engineering first-year curriculum will be discussed
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".