Inspiring the Next Generation: Challenges and Strategies for Onboarding and Retention in an Undergraduate CubeSat Design Team
Bibliographic record
Abstract
The University of Toronto Aerospace Team (UTAT) Space Systems Division is a fully student levy-funded, student-led undergraduate design team that develops CubeSats with research-oriented payloads. UTAT’s mission is to provide undergraduate students with unique opportunities to develop engineering design skills outside of the classroom, and therefore has a distinct focus on member growth and education. As an undergraduate student team, UTAT faces a unique set of challenges in onboarding members and maintaining a strong knowledge base on the team. These challenges include onboarding members with limited technical experience, equipping them with satellite design skills, and maintaining high interest levels among volunteer members with limited time to contribute. The team has implemented a wide range of strategies related to onboarding and member development over the past two years. Notable examples include hosting workshops and regular work sessions, and employing practice projects for technical skill development. This paper presents these practices in depth and evaluates their impacts using both quantitative and qualitative metrics of team success including retention rates, team demographic data, and individual perceptions of team dynamics. It also evaluates these practices against scientifically backed models, while evaluating the effectiveness of these models in the student team environment. Lessons learned include the importance of emphasizing a culture of inclusivity and psychological safety as well as utilizing workshops and skill-building modules both in the onboarding phase and throughout the year to generate and maintain interest in the team. The practices presented here are relevant and transferable to similar organizations including student teams, industry projects, and research initiatives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".