SBIR Phase II: Building Mathematical Thinking and Problem-solving Skills Together Through Play

Award ID: 2200238

This SBIR Phase I project will develop a machine learning-assisted family math preparation program that will help and encourage parents to provide early math instruction to young children, ages 4-8. Along with reading, developing mathematical proficiency is arguably one of the most important skills for young children to acquire, particularly with the growing importance of STEM (science, technology, engineering, and math) in the world today. Yet standardized test scores in math remain alarmingly low, with just 40% of US fourth grade students performing at grade level. A considerable body of research now shows that these gaps often appear before a child first enters kindergarten. Strong parental engagement in a child's mathematics learning is often cited as a key indicator of future academic success, but ways to bring about this engagement remain elusive. Grounded both in computational social science and constructivist models of learning, this project aims to develop a scaleable means of targeting parental engagement and promoting positive math learning experiences for families, goals which are fundamentally aligned with NSF's mission to promote the progress of science and advance national welfare, and which could have significant social and economic impact for years to come. This project will introduce several innovations, particularly in the areas of artificial intelligence and data modeling. A mobile learning application will be developed that provides users with a range of on- and off-screen math activities for parent and child to engage in together. Data will be collected from short surveys repeated at regular intervals and these will be collated together with information provided by the app (learning data, time-on-task, etc.). Deep learning approaches will be leveraged to explore relationships between content, engagement, and learning measures and to suggest follow-on activities predicted to improve these outcomes. While the data set will be limited during Phase I research, the technology will be able to identify an increasing number of connections as participation becomes more and more robust over time. In Phase II, these and other instruments will describe and detect family engagement practices at more than just one point in time, with differing socio-economic groups, and in a variety of different geographies. 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.

NSF Program Director: Rajesh Mehta