Author(s):
Need: Makerspaces are a new and impactful tool in the engineering educators’ toolbox. Ensuring these spaces effectively grow and meet their full potential requires a careful understanding of students-tool interactions. General trends of tool usage and data concerning student perceptions have been identified, however a contemporary understanding of how makerspaces are being used is still needed. This work leverages network analysis techniques to model and measure the underlying network structure connected to successful and impact. The approach provides a deeper understanding of the creation of a successful space, which can provide guidance to educators for things like learning opportunities (workshops or curriculum integration) and network level impacts of new tools or staff.Guiding Questions: This work aims to quantitatively answer – how and who is using makerspaces? and Is a network analysis useful as a method for quantitatively understanding makerspaces? The effectiveness of these spaces’ in engineering education can be inferred from this answer when coupled with a network analysis. Our hypotheses about makerspace improvement, generated by a network model, will be evaluated in later semesters. These hypotheses include that a network model of the student-tool usages occurring in makerspaces can identify for removal non-obvious hurtles for students who are underutilizing the space, can guide the design of an effective makerspace from the ground up when resources are scarce, and can create events or course components that introduce students to identified underutilized tools.Outcomes: A network analysis of student and tool interactions was found to illuminate makerspace design and management, including characteristics that are difficult or impossible to see from conventional surveys and analyses. This work also found that the modularity analysis results can be validated, finding them to be consistent with more conventional quantitative methods. This work presents the results from a study conducted at makerspaces at two different universities, which surveyed students regarding their usage habits of their respective makerspaces. Data includes entry/exit survey of tool usage, tool log in/out data, end of semester surveys, student demographics, and the resultant network built from this data. Analyzing this network using modularity and nestedness analyses can identify more curriculum-driven makerspace usage and tools that form hubs in the space. The student-run makerspace was found to be more nested than the staff-run, class-focused space, with the staff-run space showing a stronger modular organization. The analysis also highlights the importance of social activities within the makerspace, as well as identifying opportunities to enhance equity with respect to race and gender. Broader Impacts: While methods to apply and interpret the results of these network analyses in the context of makerspaces are still being developed, the results thus far demonstrate that modeling makerspaces as networks is a powerful tool for assessing the health of makerspaces and characterizing more specifically how a makerspace is being used. Furthermore, the modularity and nestedness analyses used here allow educators responsible for makerspace design and management to easily compare the performance of their makerspace to others, providing a deeper understanding of how certain makerspace design decisions influence space use.
Coauthors
Samuel Blair, Texas A&M University, College Station, TX; Henry Banks, Georgia Institute of Technology, Atlanta, GA; Garrett Hairston, Texas A&M University, College Station, TX; Morgan Weaver, Georgia Institute of Technology, Atlanta, GA; Julie Linsey, Georgia Institute of Technology, Atlanta, GA; Astrid Layton, Texas A&M University, College Station, TX
