Blair, S. E., & Banks, H. D., & Linsey, J. S., & Layton, A. (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

\bibitem{asee_peer_36750}
  Blair, S. E., \& Banks, H. D., \& Linsey, J. S., \& Layton, A. (2021, July), \emph{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

Samuel Enrique Blair, Henry David Banks, Julie S Linsey, and Astrid Layton.  "Bipartite Network Analysis Utilizing Survey Data to Determine Student and Tool Interactions in a Makerspace".  2021 ASEE Virtual Annual Conference Content Access, Virtual Conference, 2021, July.  ASEE Conferences, 2021.  https://peer.asee.org/36750 Internet.  15 Jan, 2025

\bibitem{asee_peer_36750}
  Samuel Enrique Blair, Henry David Banks, Julie S Linsey, and Astrid Layton.
  "Bipartite Network Analysis Utilizing Survey Data to Determine Student and Tool Interactions in a Makerspace".
  \emph{2021 ASEE Virtual Annual Conference Content Access, Virtual Conference, 2021, July}.
  ASEE Conferences, 2021.
  https://peer.asee.org/36750 Internet.  15 Jan, 2025

@INPROCEEDINGS{asee_peer_36750
author = "Samuel Enrique Blair, Henry David Banks, Julie S Linsey, and Astrid Layton"
title = "Bipartite Network Analysis Utilizing Survey Data to Determine Student and Tool Interactions in a Makerspace"
booktitle = "2021 ASEE Virtual Annual Conference Content Access"
year = "2021"
month = "July"
address = "Virtual Conference"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/36750}
number = {10.18260/1-2--36750}
}

TY  - CPAPER
AB  - 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. 

AU  - Samuel Enrique Blair
AU  - Henry David Banks
AU  - Julie S Linsey
AU  - Astrid Layton
CY  - Virtual Conference
DA  - 2021/07/26
PB  - ASEE Conferences
TI  - Bipartite Network Analysis Utilizing Survey Data to Determine Student and Tool Interactions in a Makerspace
UR  - https://peer.asee.org/36750
DO  - 10.18260/1-2--36750
ER  -