Collaborative Research (EAGER): Data Ecosystem for Catalyzing Transformative Research in Engineering Education

协作研究（EAGER）：促进工程教育变革性研究的数据生态系统

• 批准号: 1408674
• 负责人: Aditya Johri
• 金额: $ 15万
• 依托单位: George Mason University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-12-15 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1408674&HistoricalAwards=false
• 关键词: Collaborative; Research; EAGER; Data; Ecosystem

The engineering education research (EER) community is engaged in a mission to improve the education of engineers across their lifespan. EER researchers working on federally funded projects are now being asked to become progressively transparent about the nature of data that they collect and to share that data with other researchers. This drive for transparency has its genesis both in the need for accountability and verifiability of research results, as well as the realization that advances in data sharing capabilities are essential for a field to conduct transformative research and to impact potential audiences. Although data management and sharing are seen as advantageous, no examination as yet has been done of data sharing practices and capabilities among EER researchers. It is important to understand current practices, adoption patterns and motivations, as well as future community needs related to data to fully leverage the benefits of data sharing and create a core knowledge base for the community as well as eventual creation of large datasets that can benefit other decision-makers from students and parents to administrators and policy makers. Sharing data across projects is more likely to provide a representative picture as well as contextual variation in findings, lead to useful meta-analyses, and help avoid repetitive research and policy making. This EAGER project will develop a data ecosystem for the EER community and bring together two major areas -- engineering education and data sharing cyberinfrastructure (i.e. "big data") -- that have not been funded together so far. Our proposed work will (a) Understand the culture of data creation, exchange, and use that exists within the community of engineering education researchers as well as the consumers of this research; and (b) Identify a promising collection of available data sharing mechanisms to seed an initial development effort and present guidelines for the uptake of identified mechanisms. We will collect data through interviews and focus groups (N=100) and surveys (N=300) with a representative sample of the research community. We will supplement these efforts with secondary data collection and targeted understanding of large-scale efforts. We will then examine currently existing data sharing mechanisms that exist and can be utilized by the EER community. The final product of this work will be guidelines for improving data sharing including design requirements, analysis of existing mechanisms, and an initial framework for a cyberinfrastructure to support such activities.This research will benefit the entire engineering education community by providing a rubric for sharing data that reflects community-driven priorities, best practices, and design principles that can form the foundation of a data sharing practice and system for engineering education research. This project will potentially impact hundreds of faculty and students engaged in engineering education research and teaching. The project is broadly inclusive with the goal to involve stakeholders from a diverse range of institutions with a variety of backgrounds. An infrastructure for data sharing has the potential to infuse a fundamental perspective change in how knowledge is shared and used and, in the long-term, we expect this project to bridge the communities of researchers and practitioners.

工程教育研究(EER)社区致力于改善工程师终身教育的使命。从事联邦资助项目的EER研究人员现在被要求对他们收集的数据的性质逐步变得透明，并与其他研究人员共享这些数据。这种对透明度的追求源于对研究结果进行问责和可核查的需要，以及认识到数据共享能力的进步对于一个领域进行变革性研究和影响潜在受众至关重要。尽管数据管理和共享被认为是有利的，但到目前为止还没有对EER研究人员之间的数据共享做法和能力进行检查。重要的是要了解当前的做法、采用模式和动机，以及与数据相关的未来社区需求，以充分利用数据共享的好处，为社区创建核心知识库，并最终创建大型数据集，使其他决策者(从学生和家长到管理人员和政策制定者)受益。跨项目共享数据更有可能提供具有代表性的图景以及研究结果的背景差异，导致有用的荟萃分析，并有助于避免重复研究和政策制定。这个迫切的项目将为EER社区开发一个数据生态系统，并将两个主要领域--工程教育和数据共享网络基础设施(即“大数据”)--结合在一起，这两个领域迄今尚未获得资金。我们拟议的工作将(A)了解工程教育研究人员社区内存在的数据创建、交换和使用的文化以及这项研究的消费者；以及(B)确定可用数据共享机制的有前景的集合，以便为初步开发工作提供种子，并为采用已确定的机制提供指导方针。我们将通过访谈和焦点小组(N=100)和调查(N=300)收集数据，并对研究界具有代表性的样本进行调查。我们将以二次数据收集和对大规模努力的有针对性的了解来补充这些努力。然后，我们将检查EER社区现有的和可以利用的现有数据共享机制。这项工作的最终成果将是改善数据共享的指导方针，包括设计要求、对现有机制的分析，以及支持此类活动的网络基础设施的初步框架。这项研究将通过提供反映社区驱动的优先事项、最佳实践和设计原则的数据共享准则，使整个工程教育界受益，这些原则可以构成工程教育研究的数据共享实践和系统的基础。该项目可能会影响数百名从事工程教育、研究和教学的教职员工和学生。该项目具有广泛的包容性，目标是让具有不同背景的不同机构的利益攸关方参与进来。数据共享的基础设施有可能为知识的共享和使用方式带来根本性的视角变化，从长远来看，我们希望这个项目能够在研究人员和从业人员之间架起一座桥梁。