Designing a mobile makerspace: A strategy for increasing diversity by offering engineering outreach workshops to underrepresented youth
Bibliographic record
Abstract
Makerspaces, physical spaces that provide access to fabrication tools, technologies, and resources, are potentially changing the way educators envision teaching and learning. The purpose of this poster is to illustrate how an engineering design process is being used to help guide Connections Engineering Outreach to design, build, implement, and evaluate a mobile makerspace. One of the objectives of the mobile makerspace is to provide outreach workshops to underrepresented and underserviced groups in an attempt to increasing the diversity in Science, Technology, Engineering, and Mathematics (STEM). Since January 2018, the mobile makerspace has delivered workshops to approximately 2000 students in Grades 3-8. Preliminary results from an online survey indicate that the workshops provided high levels of student engagement and opportunities to learn about STEM. Interview results also suggest that the workshops are helping build the capacity of educators towards using makerspace technology. This project is yet to complete one full cycle of the engineering design process and will be conducting on-going program evaluation
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 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".