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Globally, universities have heavily invested in makerspaces. This investment requires an understanding of how students use tools and how tools to aid in engineering education, as well as how the spaces can be improved. Network analysis of human systems can often yield valuable information about how the networks work and function. Applying network techniques to makerspaces can yield helpful information that is otherwise not visible. This thesis’s primary focus is the application of a variety of bio-inspired network techniques to improve the understanding of the makerspace. Several parallels can be drawn between makerspace networks and other mutualistic networks, such as plant-and-pollinator networks where the system’s success depends on the interaction between the two species. The ecological metrics would establish measurable values that the health and conditions of a network can be evaluated using. These three metrics are nestedness, modularity, and connectance, which can provide structural information about the network and act as diagnostics tools that can change depending on different system conditions. The makerspace at the universities went through several regulatory changes due to COVID-19, providing a unique opportunity to utilize the metrics to analyze the health of the space under higher regulatory restrictions and return to normal operations. The makerspace is converted into a bipartite network to allow for ecological analysis techniques where the spaces are modeled with students interacting with tools. Null models evaluate the significance of the nestedness and modularity results. Findings indicate that makerspaces tend to be structurally nested, but when compared to normal operating conditions, they can be seen to exhibit modularity during the higher restriction environment. The makerspace network and subsequent analysis provide insight into the use of ecological metrics in human systems and provide potential ideas for results to be used in various networks. The following network analysis also yields valuable information identifying essential hub tools and student interactions within the space, showcasing the capabilities the ecological study of human networks can have on human systems.
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Bio‐inspired human network diagnostics: Ecological modularity and nestedness as quantitative indicators of human engineered network function
Analyzing interactions between actors from a systems perspective yields valuable information about the overall system's form and function. When this is coupled with ecological modeling and analysis techniques, biological inspiration can also be applied to these systems. The diagnostic value of three metrics frequently used to study mutualistic biological ecosystems (nestedness, modularity, and connectance) is shown here using academic engineering makerspaces. Engineering students get hands‐on usage experience with tools for personal, class, and competition‐based projects in these spaces. COVID‐19 provides a unique study of university makerspaces, enabling the analysis of makerspace health through the known disturbance and resultant regulatory changes (implementation and return to normal operations). Nestedness, modularity, and connectance are shown to provide information on space functioning in a way that enables them to serve as heuristic diagnostics tools for system conditions. The makerspaces at two large R1 universities are analyzed across multiple semesters by modeling them as bipartite student‐tool interaction networks. The results visualize the predictive ability of these metrics, finding that the makerspaces tended to be structurally nested in any one semester, however when compared to a “normal” semester the restrictions are reflected via a higher modularity. The makerspace network case studies provide insight into the use and value of quantitative ecosystem structure and function indicators for monitoring similar human‐engineered interaction networks that are normally only tracked qualitatively.
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- PAR ID:
- 10498870
- Editor(s):
- Clifford Whitcomb
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Systems Engineering
- ISSN:
- 1098-1241
- Page Range / eLocation ID:
- 1-13
- Subject(s) / Keyword(s):
- bio-inspired design connectance makerspaces modularity nestedness network analysis system design
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1002/sys.21756
