Board 358: Quantitative Network Analysis for Benchmarking and Improving Makerspaces

Claire Kaat; Pepito Thelly; Julie S Linsey; Astrid Layton

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Conference: 2024 ASEE Annual Conference & Exposition
Location: Portland, Oregon
Publication Date: June 23, 2024
Start Date: June 23, 2024
End Date: June 26, 2024
Conference Session: NSF Grantees Poster Session
Tagged Topic: NSF Grantees Poster Session
Page Count: 7
DOI: 10.18260/1-2--46941
Permanent URL: https://peer.asee.org/46941
Download Count: 71

Dr. Julie S. Linsey is a Professor in the George W. Woodruff School of Mechanical Engineering at the Georgia Institute of Technological. Her research focus is on design methods, theory, and engineering education with a particular focus on innovation and conceptual design.

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biography

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

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Dr. Astrid Layton is an assistant professor at Texas A&M University in the J. Mike Walker ’66 Department of Mechanical Engineering and a Donna Walker Faculty Fellow. She received her Ph.D. in Mechanical Engineering from Georgia Institute of Technology in Atlanta, Georgia. She was elected to serve as a member and chair of ASME’s Design Theory and Methodology technical committee 2020-23. She is also a guest editor for IEEE’s Open Journal of Systems Engineering and associate editor for ASME's Journal of Mechanical Design. She is the recipient of several awards, including a 2021 ASME International Design Engineering Technical Conferences and Computers & Information in Engineering (IDETC-CIE) best paper award. Her research uses interdisciplinary collaborations to solve large-scale system problems, developing knowledge that supports designers and decision-makers. Dr. Layton is an expert on bio-inspired systems design, with a focus on the use of biological ecosystems as inspiration for achieving sustainability and resilience in the design of complex human networks/systems/systems of systems. Examples include industrial resource networks, makerspaces, power grids, cyber-physical systems, supply chains, innovation processes, and water distribution networks.

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Abstract

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.

Citation
Format

Kaat, C., & Thelly, P., & Linsey, J. S., & Layton, A. (2024, June), Board 358: Quantitative Network Analysis for Benchmarking and Improving Makerspaces Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. 10.18260/1-2--46941

TY  - CPAPER
AB  - 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.
AU  - Claire Kaat
AU  - Pepito Thelly
AU  - Julie S Linsey
AU  - Astrid Layton
CY  - Portland, Oregon
DA  - 2024/06/23
PB  - ASEE Conferences
TI  - Board 358: Quantitative Network Analysis for Benchmarking and Improving Makerspaces
UR  - https://peer.asee.org/46941
DO  - 10.18260/1-2--46941
ER  -