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Bipartite Network Analysis Utilizing Survey Data to Determine Student and Tool Interactions in a Makerspace

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Conference

2021 ASEE Virtual Annual Conference Content Access

Location

Virtual Conference

Publication Date

July 26, 2021

Start Date

July 26, 2021

End Date

July 19, 2022

Conference Session

NSF Grantees Poster Session

Tagged Topics

Diversity and NSF Grantees Poster Session

Page Count

18

DOI

10.18260/1-2--36750

Permanent URL

https://peer.asee.org/36750

Download Count

30

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Paper Authors

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Samuel Enrique Blair Texas A&M University

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Samuel Blair is a Graduate student in Mechanical Engineering program at Texas A&M University in College Station, TX. His research interest include bio-inspired design of complex systems for human networks.

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Julie S Linsey Georgia Institute of Technology

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Dr. Julie S. Linsey is an Associate Professor in the George W. Woodruff School of Mechanical Engineering at the Georgia Institute of Technological. Dr. Linsey received her Ph.D. in Mechanical Engineering at The University of Texas. Her research area is design cognition including systematic methods and tools for innovative design with a particular focus on concept generation and design-by-analogy. Her research seeks to understand designers’ cognitive processes with the goal of creating better tools and approaches to enhance engineering design. She has authored over 150 technical publications including over forty journal papers, and ten book chapters.

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Astrid Layton Texas A&M University Orcid 16x16 orcid.org/0000-0003-3790-2916

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Astrid Layton is an assistant professor at Texas A&M University in the Mechanical Engineering department and received her Ph.D. from Georgia Institute of Technology in Atlanta, Georgia. She is interested in bio-inspired system design problems and is currently working at the intersection of ecology and engineering for the design of complex human networks and systems.

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Henry David Banks Georgia Institute of Technology

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Henry Banks is a Masters student at Georgia Tech. He conducts research on Makerspaces, specifically looking at ways to model them and ultimately better inform their design. In undergrad, he focused on functional modeling and systems thinking.

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Abstract

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 uses 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. 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 analyses will identify the system components that are frequently used when students work in the makerspace over multiple visits. 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. Current results explore the order in which students choose to learn and use the tools in the space, which tools/features are used most frequently and how the data from the daily entry/exit surveys compares to the end-of-semester self-reports. A key question in this research, especially for making it adoptable by other universities, is if end-of-semester, self-reported data is accurate enough to create informative, actionable guidance from the network models without requiring the daily tool usage data.

Blair, S. E., & Linsey, J. S., & Layton, A., & Banks, H. D. (2021, July), Bipartite Network Analysis Utilizing Survey Data to Determine Student and Tool Interactions in a Makerspace Paper presented at 2021 ASEE Virtual Annual Conference Content Access, Virtual Conference. 10.18260/1-2--36750

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