What is Success? Lessons Learned from a Student-Taught Co-Curricular CAD Program
Bibliographic record
Abstract
When engineering students become teachers in a student-led, co-curricular, computer-aided design (CAD) training program, they gain a new perspective.This paper on engineering practice explores how the student leaders' definition of success evolved over three years and documents the lessons they learned as a result.The students initiated the program through the Engineering Success Centre, a help centre devoted to providing academic support for students in a Sustainable Design Engineering degree.Additional CAD training was needed primarily due to the lack of a dedicated course, as CAD is currently taught as part of a first-year communications design course with different student instructors each year.Additionally, the CAD training program is an opportunity for students to earn a certification that could bolster both their skills and their resume.In a retrospective analysis of program records, teaching materials, and communication with students, this paper documents testimonials from the student leaders, students in the program, and their staff and faculty mentors to compare the three years that the program was offered.The co-curricular program shifted from a more traditional format to a more exploratory format, and the definition of success changed from counting the number of certifications to focusing on student learning and engagement.The student instructors' experiences as teachers have shifted how they act as students, as they now notice the pedagogical practices of their instructors.Ultimately, empowering students as teachers in co-curricular programs benefits both students and student instructors, and lessons learned are offered to implement similar programs.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".