Search for: All records

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. 

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. 

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. 

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. 

There has been dramatic growth in the number of makerspaces at educational institutions. More research is needed to understand student interactions in these spaces and how these spaces should be designed to support student learning. This project uses network analysis techniques to study the network of activities in a makerspace that lead to successful student experiences. The proposed analyses will model a makerspace as a network of interactions between equipment, staff, and students. Results from this study will help educators to 1) identify and remove previously unknown hurdles for students who rarely use the space, 2) design an effective space using limited resources, 3) understand the impact of new equipment or staff, and 4) create learning opportunities such as workshops and curriculum integration that increase student learning. The new knowledge produced by this project may be useful for maximizing equipment and support infrastructure, and for guiding new equipment purchases. Thus, the results will support further development of effective makerspaces. This project hypothesizes that network-level analyses and metrics can provide valuable insights into student learning in makerspaces and will support what-if scenarios for proposed changes in spaces. Systems modeling and analysis have been used successfully to understand complex human and biological networks. In the context of makerspaces, this technique will provide measures of interaction between system components such as students, staff, and equipment. The analyses will identify the system components that are frequently used when students work in the makerspace over multiple visits. The project will allow for the comparison of makerspaces that have different levels of integration with the curriculum and methods of student introduction (pop-up classes, tours, extra-curricular competitions, advertising, and bring a friend). Demonstration of the effectiveness of the analyses in characterizing makerspaces and the ease of data collection will help support the use of this approach in future work that compares makerspaces nationwide. Current results explore the order in which students choose to learn and use the tools in the space, which tools/features are used most frequently and how the data from the daily entry/exit surveys compares to the end-of-semester self-reports. A key question in this research, especially for making it adoptable by other universities, is if end-of-semester, self-reported data is accurate enough to create informative, actionable guidance from the network models without requiring the daily tool usage data.