The Impact of Makerspaces on Engineering Education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Makerspaces are gaining more ground in universities and other educational institutions as a novel approach to boost creativity, innovation, and provide more opportunities for experiential and hands-on learning experience. Albeit being multidisciplinary, and open spaces in nature,Makerspaces still lack integration to the curricula of engineering schools. With increasingly competitive markets, there is a need to educate future engineers with necessary skills to be more creative and to be able to compete in today’s global market. A twophase study was developed to study the integration of the Makerspace concept in engineering schools. The first phase was based on interviews with five North American University Makerspaces that vary in size, objective, business model, and management structure to identify best Makerspace practices in preparationof the establishment of the University of Ottawa’s Richard L’Abbé Makerspace. The second phase was a survey administered to engineering students who have used the Richard L’Abbé Makerspace since its opening in the fall of 2014 to assess its impact on their engineering competencies, in particular design skills, problem analysis, communication and teamwork skills, investigation skills, and entrepreneurial skills. This paper aims at studying best practices of Makerspaces on campus and their impacts onengineering education and on the development ofdesired skills and competencies for engineering students.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 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 it