Resilience and Innovation in Response to COVID-19: Learnings from Northeast Academic Makerspaces
Bibliographic record
Abstract
Abstract Studies over the last decade have emphasized the need for hands-on, experiential learning and the importance of making in engineering education. This emphasis has led to the blossoming of makerspaces in engineering schools and universities more broadly. The lockdown due to the COVID-19 pandemic has forced universities to shift to fully remote teaching and close their makerspaces in Spring 2020, and the Fall 2020 semester has seen a whole gamut of models for teaching. What happened to makerspaces and how have they tried to maintain their key role in both extra-curricular and curricular learning? This paper will present an overview of common challenges to typical academic makerspace operation. It will highlight the changes and adaptations in operation due to Covid-19 at four universities in the US Northeast. Three of the four institutions are public, while one is private. The spaces have been open from three to five years, and three are directly supported by or housed in the school of engineering, while the other one by the school's IT department. All four makerspaces have historically been open to the entire university. Academic makerspaces support both curricular and extra-curricular design projects and learning at many institutions. While the Covid-19 pandemic has forced most universities to switch to fully remote or some combination of hybrid/hyflex and remote courses, many of the physical activities necessary for prototyping are in flux. Many universities have allowed their labs and makerspace to open in a limited capacity, while some have suspended all or almost all operations. Specific changes to capacity, access, hours, and funding will be provided in detail for each makerspace. Innovations in training methods (such as online training modules), reservation systems, machine operations, or new methods to support remote and in-person collaboration will be documented. Results will be presented as case studies to support future research on the impact of collaboration within academic makerspaces and to share resources and ideas on how these spaces have and will continue to adapt. Any best practices that emerge from the four spaces will be highlighted. Future research questions from each interview will be posed. Collecting and assessing the impact in the short term will allow engineering education and design education researchers to begin studying the long term impact of the pandemic on both these spaces and collaborative, project-based learning.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.019 | 0.019 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.003 | 0.027 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".