Search for: All records

Abstract As the popularity of makerspaces and maker culture has skyrocketed over the past two decades, numerous studies have been conducted to investigate the benefits of makerspaces for university students and how to best establish an inclusive, welcoming environment in these spaces on college campuses. However, unprecedented disruptions, such as the COVID-19 pandemic, have the potential to greatly affect the way that students interact with makerspaces and the benefits that result. In this study, a survey asking about prior makerspace involvement, tool usage, and student demographics was administered to students who use academic makerspaces at two large public universities. Survey data was collected for three semesters (Fall 2020, Spring 2021, and Spring 2022) and spanned both during and after the height of the COVID-19 pandemic. To quantify the differences between the semesters, nestedness and connectance metrics inspired by ecological plant-pollinator networks were utilized. These ecological metrics allow for the structure of the interactions of a network to be measured, with nestedness highlighting how students interact with tools and connectance with the quantity of student-to-tool interaction. The network analysis was used to better gauge the health of the makerspace and the type and frequency of interactions between tools. The raw survey data combined with the ecological metrics provided unique insight into the struggles the makerspaces encountered throughout the pandemic. It was found that nestedness, a measure of system stability, decreases with a decrease in tool usage. Additionally, the higher the connectance the more students interacted with the space. Utilizing metrics such as these and better understanding student tool interactions can aid makerspaces in monitoring their success and maintaining a healthy and welcoming space, as well as tracking the current health of the space. In combination with the survey results, a deep understanding of what challenges the space is facing can be captured. 

Prior research emphasizes the benefits of university makerspaces, but overall, quantitative metrics to measure how a makerspace is doing have not been available. Drawing on an analogy to metrics used for the health of industrial ecosystems, this article evaluates changes during and after COVID-19 for two makerspaces. The COVID-19 pandemic disturbed normal life worldwide and campuses were closed. When students returned, campus life looked different, and COVID-19-related restrictions changed frequently. This study uses online surveys distributed to two university makerspaces with different restrictions. Building from the analysis of industrial ecosystems, the data were used to create bipartite network models with students and tools as the two interacting actor groups. Modularity, nestedness, and connectance metrics, which are frequently used in ecology for mutualistic ecosystems, quantified the changing usage patterns. This unique approach provides quantitative benchmarks to measure and compare makerspaces. The two makerspaces were found to have responded very differently to the disruption, though both saw a decline in overall usage and impact on students and the space’s health and had different recoveries. Network analysis is shown to be a valuable method to evaluate the functionality of makerspaces and identify if and how much they change, potentially serving as indicators of unseen issues. 

Makerspaces continue to be a part of many university engineering programs. More work is needed to understand their impacts and how makerspaces should be implemented to maximize their impact for all students. Many of the available approaches to ascertain impact are highly effective but excessively time-intensive, especially for continuous monitoring of a space. This paper presents the use of bipartite network analysis of weighted and unweighted matrices of student tool usage to determine modularity as an easy-to-obtain metric to monitor space. To obtain the data needed, an end-of-the-semester survey asks students which tool they used in the space and how frequently. Data was collected in Spring 2021 and Spring 2022 as covid restrictions were being lifted, providing a data set where the modularity values should be changing. Prior work demonstrated unweighted modularity values as an effective tool for identifying changes in the health of a makerspace. Current work explores the inclusion of tool frequency use on the conclusion drawn from modularity analysis. Results show differing patterns of results between the weighted (includes frequency of use) and unweighted (only considers if a tool was used) modularity values. More work needs to explore the use of weighted bipartite network analysis and the benefits it may provide over the much simpler to obtain the unweighted analysis. Additional research is also needed on other methods to monitor the health of a makerspace and the benefits to all of its users. 

Over the past two decades, many studies have analyzed the extensive benefits of makerspaces towards student education, design-self efficacy, and community involvement. However, less work has been dedicated to examining the ways in which students interact within makerspaces. This study seeks to dive deeper into the patterns of tools that students are using and how this knowledge can inform makerspaces and make them more effective. Tool usage data was collected through end of semester surveys administered to students at two large public universities over the course of 5 semesters: Fall 2020, Spring 2021, Spring 2022, Fall 2022, and Spring 2023. The survey asked a variety of questions about prior makerspace experience, general and specific tool usage, and student demographics. The first three semesters of data were used to gain an understanding of how different student groups – defined based on categories such as major, demographic, or class taken – interact with various tools within the space. Combined semester analysis was used to understand how underrepresented minorities were utilizing the space while between semester analysis was used to see trends in makerspace usage over time. The onset of the COVID-19 pandemic at the start of the study provided ample opportunity to examine the effects of unprecedented disruptive events and the resulting restrictions on the health of makerspaces and student interactions. Results showed substantial differences in usage between schools and student groups as well as a decline in usage following the onset of COVID restrictions. In the final two semesters, a pilot study was conducted at both makerspaces to determine how hands-on, and tour-based workshops offered to students can be used to increase tool usage in makerspaces and more successfully welcome new students into the maker world. While there is insufficient data to make any conclusions from these interventions, they showed the potential for promising results if future work is performed. Finally, insights from this study are used to offer suggestions to makerspace administrators on how to address poor makerspace usage. 

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. 

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.