RealEngineering: Space – Designing the Community-Applied Space Engineering Program
Bibliographic record
Abstract
Engineering education is still largely offered through traditional, content-heavy approaches, with key technical topics in individual courses separate from those that emphasize the practice of the engineering profession, resulting in fragmented student workloads. Traditional assessments do not accommodate students’ unique, diverse learning perspectives. These issues fail to recognize that engineering is above all else a community-of-practice, requiring practitioners to demonstrate innovation and resilience to address today’s complex challenges in sustainable ways. More recent programs adopt project-based pedagogies, that engage learners in engineering problems that affect their communities. This paper proposes taking the project focus further, with a structure that allows faculty and students to collaborate on real-world engineering work that is not just done for, but also with, the community, and with sustainability built in. Such an approach establishes an overarching connection between the “work” of an engineer and what it is to be a future engineer for society. The authors are developing a 4-year space engineering program proposal, where students from all years will collaborate to design, build, launch and operate a cubesat for, and with, the community as the full focus of their 4-year degree. A six-week pilot slice of the program took place in the summer of 2021 with 20 students from all undergraduate year groups collaborating on a community-focussed, sustainable small space-mission design activity to change power dynamics around water quality data in northern and indigenous Canadian communities. Students worked in organizational teams, with structured teambuilding and collaboration time, focussed working sessions from subject-matter-experts, microcredential learning and unstructured team time to advance their project. This culminated in a mission concept review with a team of expert, industry and community partners. This paper presents some of the key ideas that informed the program, and the tools used to frame the learning journey in an undergraduate engineering degree. The pilot demonstrated students’ readiness to take on complex, unstructured challenges and organize themselves, and the potential to offer undergraduate learning spaces that have a very different connection to community and global issues.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".