PFI-TT: Using Big Data Analytics to Empower K-12 Teachers for Instructional Improvement

University Of Washington, Seattle WA

Investigators

Abstract

The broader impact/commercial potential of this Partnerships for Innovation - Technology Translation (PFI-TT) project is to develop an online platform for collaborative learning and analytics for global education. The proposed technology uses the latest developments in educational big data and machine learning techniques to empower K-12 teachers for instructional improvement. K-12 school systems have become the latest frontier in big data, as the COVID-19 pandemic has accelerated the digital transformation of teacher and student learning with artificial intelligence playing a key role. Despite a great societal need, K-12 systems remain one of the areas least transformed by technology. Teachers currently lack the kind of technological tools that can efficiently support their lesson plan, reflection, and learning. The proposed technology aims to address these needs and may have potential to generate economic value, as education technology places a critical role in advancing the human capital needed to drive economic growth. This team, led by women and people of color, will broaden participation by piloting the initial prototype in schools that serve large proportions of students of color from low-income families. The proposed project uses a participatory design-based implementation approach to co-design with and for teachers. The proposed technology will include libraries of high-quality instructional materials that teachers can adapt and incorporate into their own lesson plans. The platform leverages machine learning analytics to allow teachers to efficiently conduct self-learning and be mentored and coached by other teachers. Moreover, the development of this technology will incorporate a rigorous, quasi-experimental approach that combines both quantitative and qualitative evidence to examine its effectiveness on improving teaching and student outcomes, as well as factors that moderate its usefulness in schools. The proposed technology will be supported by a team that represents the ecosystems necessary for its ongoing success, including expertise in education and data science research, product development and technology transfer, and marketing and business strategy. 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.