Benchmarking and Improving Makerspaces Using Quantitative Network Analysis

使用定量网络分析对创客空间进行基准测试和改进

• 批准号: 2013505
• 负责人: Julie Linsey
• 金额: $ 24.41万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2013505&HistoricalAwards=false
• 关键词: Benchmarking; Improving; Makerspaces; Using; Quantitative

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.

该项目旨在通过改进学生，以便所有学生都可以从中受益。需要更多的研究来了解这些空间中的互动，而howese则可以在成功的学生体验中进行学习。使用有限的资源设计有效的空间，3）新设备或员工的影响，这是从高中TE级别的工程学生，在小型课程和大型大学中由此基础设施和设备购买。水平的分析和指标可以在Makerspaces中提供学生学习，并在空间中的Willt-fifor SED变化。系统。成功运作空间，为新的Makerspace创建设计指南，从两个成功的制造商的分析中，（3）识别阻止学生使用Makerspaces的潜在障碍。与NT介绍的Currention and Adrive of Comper and Tours，课外竞赛，广告和“带来朋友”的融合。在全国范围内，Compass Makerspace toture toture rk。该奖项反映了NSF'shas Beanend Demed值得支持Thalugh评估USIS的胜利和更广泛的影响审查标准。

项目成果

Makerspace Network Analysis for Identifying Student Demographic Usage

用于识别学生人口统计使用情况的 Makerspace 网络分析

• 发表时间: 2022
• 期刊: 6th International Symposium on Academic Makerspaces
• 作者: Blair, Samuel;Hairston, Garrett;Banks, Henry;Linsey, Julie;Layton, Astrid
• 通讯作者: Layton, Astrid

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

双向网络分析利用调查数据确定创客空间中的学生和工具交互

• DOI: 10.18260/1-2--36750
• 发表时间: 2021
• 期刊: 2021 ASEE Virtual Annual Conference
• 作者: Blair, Samuel;Banks, Henry;Linsey, Julie;Layton, Astrid
• 通讯作者: Layton, Astrid

Modularity Analysis of Makerspaces to Determine Potential Hubs and Critical Tools in the Makerspace

创客空间的模块化分析以确定创客空间中的潜在中心和关键工具

• 发表时间: 2022
• 期刊: ASEE 2022 Annual Conference & Exposition
• 作者: Blair, Samuel E.;Banks, Henry D.;Hairston, Garrett;Linsey, Julie S.;Layton, Astrid
• 通讯作者: Layton, Astrid