Benchmarking and Improving Makerspaces Using Quantitative Network Analysis

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This project aims to serve the national interest by improving students’ makerspace experiences so that all students can benefit from this learning environment. Previous work has shown that makerspaces can help engineering students learn and apply engineering concepts. As a result, 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 will use 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. This project will benefit engineering students from the high school level to graduate level, in small programs and at large universities. 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 relative importance of these components in the space will also be measured. The analyses will identify the system components that are frequently used when students work in the makerspace over multiple visits. The identification of important system components will inform the creation of new makerspaces where resources are limited, ensuring that equipment investments will have the largest impact on student learning. The key project objectives are to (1) use network analysis to understand the connection between makerspace structure and successful functioning of the space, (2) create design guidelines for both new and existing makerspaces, derived from the analyses of two successful makerspaces, and (3) identify potential barriers that prevent students, especially underrepresented minorities, from using makerspaces. 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. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.