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.

该项目旨在通过改善学生的创客空间体验来服务于国家利益，以便所有学生都能从这种学习环境中受益。以前的工作表明，创客空间可以帮助工程专业的学生学习和应用工程概念。因此，教育机构的创客空间数量急剧增长。需要更多的研究来了解学生在这些空间中的互动，以及如何设计这些空间来支持学生的学习。该项目将使用网络分析技术来研究创客空间中的活动网络，从而获得成功的学生体验。拟议的分析将创客空间建模为设备，员工和学生之间的互动网络。这项研究的结果将有助于教育工作者1）识别和消除以前未知的障碍，为学生谁很少使用的空间，2）设计一个有效的空间使用有限的资源，3）了解新设备或工作人员的影响，和4）创造学习机会，如研讨会和课程整合，提高学生的学习。该项目将使工程专业的学生受益，从高中到研究生，在小型项目和大型大学。该项目产生的新知识可能有助于最大限度地利用设备和支持基础设施，并指导新设备的采购。 因此，结果将支持有效的makerspaces.This项目的进一步发展假设，网络级的分析和指标可以提供有价值的见解，学生在makerspaces学习，并将支持什么，如果场景中提出的空间变化。系统建模和分析已成功地用于理解复杂的人类和生物网络。在创客空间的背景下，这种技术将提供系统组件（如学生，员工和设备）之间的交互措施。还将测量这些组件在空间中的相对重要性。这些分析将确定学生在多次访问创客空间时经常使用的系统组件。重要系统组件的识别将为在资源有限的地方创建新的创客空间提供信息，确保设备投资对学生学习产生最大的影响。该项目的主要目标是（1）利用网络分析来了解创客空间结构与空间成功运作之间的联系，（2）根据对两个成功的创客空间的分析，为新的和现有的创客空间创建设计指南，以及（3）确定阻止学生，特别是代表性不足的少数民族，使用创客空间的潜在障碍。该项目将允许比较与课程和学生介绍方法（弹出式课程，图尔斯，课外比赛，广告和“带朋友”）有不同程度融合的创客空间。证明分析在表征创客空间方面的有效性以及数据收集的便利性将有助于支持在未来比较全国创客空间的工作中使用这种方法。NSF IUSE：EHR计划支持研究和开发项目，以提高所有学生STEM教育的有效性。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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