2024 ASEE Annual Conference & Exposition

Makerspaces on university campuses have seen tremendous growth and investments in recent years. Growing empirical data demonstrates the significant learning benefits to engineering students. Makerspaces are a new tool in the engineering educators’ toolbox, and as such much more needs to be done to ensure these spaces effectively grow and meet their full potential. This grant has been developing a novel network analysis technique for makerspaces, to enable the underlying makerspace network structure to be understood in terms of its connection to the successful and impactful functioning of makerspaces. The work has uncovered some basic structural building blocks of makerspace networks, known as modules, and the tools and students that make up those modules. This network-level understanding of the space enables actions such as effectively removing previously undiscovered hurdles for students who are underutilizing spaces, guiding the design of an effective makerspace from the ground up at locations with fewer resources, and creating effective events or course components that introduce students to the space in such a way that increases their chances of returning. A deep understanding of the network structure that creates a successful makerspace also provides guidance to educators on things like the impact of adding particular learning opportunities through workshop or curriculum integration, and insight into the network-level impacts of the addition of new tools or staff. The work done over the past 3 years has tried to address the following key objectives: (1) Understand the role that network analysis can play in both understanding the connection between the structure and successful functioning of a makerspace. (2) Create design guidelines for both new makerspaces and the growth of existing makerspaces, derived from modularity analyses of two successful makerspace case studies. (3) Identify potential roadblocks that prevent students, especially underrepresented minority students, from feeling comfortable in and using makerspaces. Benefits of network analysis techniques include the ability to break down a seemingly complex and chaotic makerspace into actors interacting with tools. This data was obtained through short end-of-semester student surveys. Early work found that these end-of-semester surveys provide sufficient data for the proposed analyses and are comparable to survey information provided by students as they enter and exit the space.