INTEGRATING MAKERSPACES INTO ENGINEERING DESIGN
Bibliographic record
Abstract
Makerspaces are informal sites in which people with similar interests can collaboratively build creative projects by using emerging technologies. In recent years, makerspaces have been created on most campuses and often linked to STEM learning practices. However, integrating makerspaces in engineering curriculum is often not done formally. In this paper, we discuss how the University of Ottawa integrated its makerspace into its cornerstone design curriculum and its design challenges. Cornerstone engineering design includes first- and second-year courses where students learn and apply design knowledge while working in teams. Each team is expected to develop three prototypes during the semester and solve a design problem for a client. Maker components are integrated in the labs, where many makerspace technologies, such as 3D printing and laser cutting, are taught and used in the development of the prototypes. In addition, the makerspace offers a yearly multidisciplinary client-based design challenge that is open to all students. This paper explores the integration of maker ideology and technology in curricular and extracurricular design activities. The paper outlines the connection between making and engineering design, the maker capacity for inclusion and sharing, the role of making activities in developing the identity of future engineers and the integration of course work into the makerspace.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".