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1. Modularity Analysis of Makerspaces to Determine Potential Hubs and Critical Tools in the Makerspace

   https://doi.org/10.18260/1-2--41476  

   Layton, Astrid; Linsey, Julie; Blair, Samuel; Hairston, Garrett; Banks, Henry (August 2022, ASEE Conferences)

   Globally, universities have heavily invested in makerspaces. Purposeful investment however requires an understanding of how students use tools and how tools aid in engineering education. This paper utilizes a modularity analysis in combination with student surveys to analyze and understand the space as a network of student-tool interactions. The results show that a modularity analysis is able to identify the roles of different tool groupings in the space by measuring how well tool groups are connected within their own “module” and their connection to tools outside of their module. A highly connected tool in both categories is considered a hub that is critical to the network. Poorly connected tools indicate insignificance or under utilization. Makerspaces at two universities were investigated: School A with a full-time staff running the makerspace and School B run by student-volunteers. The results show that 3D printers and metal tools are hubs at School A and 3D printers, metal tools, and laser cutters are hubs at School B. School B was also found to have a higher overall interaction with all the tools in the space. The modularity analysis results are validated using two-semesters worth of student self-reported survey data. The results support the use of a modularity analysis as a way to analyze and visualize the complex network interactions occurring within a makerspace, which can support the improvement of current makerspaces and development of future makerspaces. 

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2. Tool Usage Patterns of Mechanical Engineering Students in Academic Makerspaces

   https://doi.org/10.1109/FIE58773.2023.10342990  

   Blair, Samuel; Crose, Claire; Linsey, Julie; Layton, Astrid (October 2023, 2023 IEEE Frontiers in Education Conference (FIE))

   Academic makerspaces have continued to rise in popularity as research shows the diverse benefits they provide to students. More and more engineering curriculums are incorporating makerspaces and as such there is a need to better understand how their student users can best be served. Surveys administered to makerspace users at a public research university in the Southwest United States during Fall 2020, Spring 2021, Spring 2022, and Fall 2022 tracked student tool usage trends with academic career stages. The survey asked questions about prior experience, motivation, tool usage, and demographics. Analyzed results for mechanical engineering student users provide insight into how curriculum and class year affect the specific tools used and the percentage of students who used a particular tool. The survey results also create a bipartite network model of students and tools, mimicking plant-pollinator type mutualistic networks in ecology. The bipartite network models the student interactions with the tools and visualizes how students interact with the tools. This network modeling enables ecological network analysis techniques to identify key makerspace actors quantitatively. Ecological modularity, for example, identifies divisions in the student-tool makerspace network that highlight how students from different majors (here we investigate mechanical) utilize the makerspace's tools. Modularity is also able to identify “hub” tools in the space, defined as tools central to a student's interaction within the space, based on student-tool connectivity data. The analysis finds that tools commonly used for class by mechanical engineering students, such as the 3D printer or laser cutter, act as gateway tools that bring users into the space and help spark interest in the space's other tools. Using the combined insights from the survey results and the network analysis, ecological network metrics are shown here to be a promising route for informing makerspace policy, tool purchases, and curriculum development. The results can help ensure that the space is set up to give students the best learning opportunities. 

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3. A Bio-Inspired Network Analysis Approach to Improve Understanding of Engineering Makerspaces

   Blair, Samuel Enrique (May 2023, Graduate and Professional School of Texas A&M University)

   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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4. Makerspace Network Analysis for Identifying Student Demographic Usage

   Blair, Samuel; Hairston, Garrett; Banks, Henry; Linsey, Julie; Layton, Astrid (October 2022, 2022 International Symposium on Academic Makerspaces (ISAM))

   Makerspaces provide unparalleled hands-on experiences for students. Understanding the interactions that occur in these spaces is critical to improving engineering education. This work represents the first time that demographic-based modularity analysis has been conducted on university makerspaces. While largely dependent on the survey data used to make the bipartite networks, the results serve as an example of how this technique could offer a novel means of viewing these makerspaces. At the broadest level, this approach provides insight into the ways in which different subsets of students use the space, both in terms of raw usage statistics and in terms of the module assignments for both student and tool groupings. When looking at the network from a major perspective, the desired change in modularity is less apparent, and more work will need to be done to see whether increasing the modularity helps with system resilience (maintaining high levels of makerspace operation despite failures of certain tools) or if higher modularity represents an undesirable separation in the space between different majors and the tools they tend to use. 

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5. Bio‐inspired human network diagnostics: Ecological modularity and nestedness as quantitative indicators of human engineered network function

   https://doi.org/10.1002/sys.21756  

   Blair, Samuel; Hairston, Garrett; Banks, Henry; Kaat, Claire; Linsey, Julie; Layton, Astrid (April 2024, Systems Engineering)

   Clifford Whitcomb (Ed.)

   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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