A COURSE BASED APPROACH TO RECOGNIZING STUDENT EFFORTS IN ENGINEERING DESIGN COMPETITIONS
Bibliographic record
Abstract
Abstract –Recently, the University of Ottawa has sought to increase experiential learning opportunities for its engineering students. A great deal of effort has been made to remove barriers and increase resources for students participating in large-scale international engineering design competitions. However, so far students involved have participated in these extracurricular activities purely out of interest and to gain experience. In this paper, we seek to recognize this immense effort made by students by developing a course in which students can receive credits for working on these projects. The course was split into two sessions per week involving three hours of lecturing, practical learning activities and group meetings with full guidance from the instructor and three hours of laboratory time for students to work on their project. A few key aspects of the course were found to highly benefit the teams that had participating members in the course. A technical skill development project requiring each student to develop a new skill considered useful by their teams, proved to be the course highlight. Inter-team collaboration developed and continued after the course ended. Other aspects, while beneficial, would require improvement in future offerings the course. Extensive design report writing and presentations in the course solidified participating students’ abilities in these aspects, which was made evidently clear during these portions of each competition, but drew extensive complaints from students. Customized quizzes related to the specific competition rules for each team, given early on, gave poor results but helped strengthen rule compliance compared to previous years. Finally, improved team organization and a significant increase in team performance at competition was achieved by all teams who had students participating in the course, demonstrating the course’s success.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".