Benchmarking and Improving Makerspaces Using Quantitative Network Analysis

该项目旨在通过改善学生的制造空间体验来服务于国家利益，使所有学生都能从这种学习环境中受益。以前的工作已经表明，makerspace可以帮助工科学生学习和应用工程概念。其结果是，教育机构的创业者空间数量急剧增加。需要更多的研究来了解这些空间中的学生互动，以及这些空间应该如何设计来支持学生学习。这个项目将使用网络分析技术来研究在Makerspace中导致学生成功体验的活动网络。拟议的分析将把创客空间建模为设备、员工和学生之间的互动网络。这项研究的结果将帮助教育工作者1)为很少使用该空间的学生识别并消除以前未知的障碍，2)利用有限的资源设计一个有效的空间，3)了解新设备或人员的影响，4)创造学习机会，如研讨会和课程整合，以增加学生的学习。该项目将使工程专业的学生从高中水平到研究生水平、小型项目和大型大学受益。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，并有助于指导新设备的采购。因此，这一结果将支持有效的makerspace的进一步发展。本项目假设，网络水平的分析和度量可以为学生在makerspace中的学习提供有价值的见解，并将支持提议的空间变化的假设情景。系统建模和分析已被成功地用于理解复杂的人类和生物网络。在Makerspace的背景下，这项技术将提供系统组件(如学生、教职员工和设备)之间的交互测量。还将衡量这些组成部分在空间中的相对重要性。这些分析将确定当学生在makerspace多次访问时经常使用的系统组件。确定重要的系统组件将有助于在资源有限的情况下创建新的制造空间，确保设备投资将对学生的学习产生最大影响。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制定设计指南，以及(3)找出阻止学生，特别是未被充分代表的少数族裔使用创客空间的潜在障碍。该项目将对与学生介绍课程和方法(弹出式课程、参观、课外竞赛、广告和“带朋友”)具有不同程度整合的MakerSpace进行比较。证明分析在描述创业者空间特征方面的有效性和数据收集的简便性，将有助于支持在今后比较全国创业者空间的工作中使用这一方法。NSF IUSE：EHR计划支持研究和开发项目，以提高所有学生的STEM教育的有效性。通过参与的学生学习路径，该计划支持有前景的实践和工具的创建、探索和实施。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.