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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. 

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.