EAGER: Building Human Social Networks to Develop Data-Driven Approaches that Expand Lifelong Learning Opportunities

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Industry demands more highly qualified graduates who can adapt to a rapidly changing work environment, while government demands higher quality in undergraduate education. These demands raise questions about how higher education institutions can meet such needs. Unfortunately, government, industry, and higher education do not naturally come together often enough to achieve the needed student outcomes. The recent focus on ?big data? is further raising questions about how the government, industry, and higher education capture vast amounts of information about student learning. This project will explore the intersection of big data with educational opportunities across a learner?s life, the opportunities and challenges created by these intersections, and what roles different institutions (e.g. K-12, industry, universities, government) will or should play in improving undergraduate education. Through a series of workshops, this project aims to facilitate interactions among constituents from industry, the government, and higher education institutions. The goal is to identify the kinds of big data that can tell us how to expand learning opportunities across a student?s lifetime. Through these workshops, participants will be guided to use big data to investigate trends in undergraduate engineering education, including: redefining engineering education in relation to the skills needed to be a working engineer; exploring how real-time feedback can promote learning; increasing the efficiency at which engineers acquire new knowledge and skills; understanding the interconnections across the educational systems; and rethinking the shift towards more automation, which may radically change the skills needed for employability. Big data and other trends will fundamentally change how education is obtained, who has access to various forms of education (e.g., on-line vs in person courses), and the activities that define a high-quality engineering education. By exploring the implications of big data on education, this project has the potential to transform engineering education specifically and STEM education more generally.More specifically, this project will involve a series of four workshops that are designed to achieve the following: (1) map the engineering education ecosystem from the perspective of how the affordances created by big data will impact processes, cultures, and structures in higher education; (2) explore opportunities at productive interfaces within the ecosystem; (3) understand resource allocation and flow; and (4) develop heuristics to allow various stakeholders to navigate cultural and structural shifts. Additionally, the project will integrate data collection through ethnography and survey methodologies adapted for complex, dynamic systems to more broadly capture perspectives across engineering education and explicitly focuses on network building and disseminating materials to help various entities across engineering education navigate change. This project is designed to advance knowledge of how different sectors within the engineering education ecosystem capitalize on, adapt to, or are threatened by education and workforce trends broadly driven by big data. The workshops will draw a broad audience from many sectors, including both industry and university representatives with broad expertise, and networks of people involved in personalized learning. Advances in information technology and the ability to monitor and analyze the interactions of millions of learners with online content has the potential to lead to transformative shifts in education. While these technologies have the capacity to create more equity and access to education, without opportunities for the range of stakeholders to communicate and build robust networks, such a transformative outcome cannot be assured. The opportunities for networking and relationship building in this project are supported scenario analysis and systems thinking, in a framework that has been shown to affect participant narratives.