SBIR Phase II: A STEM toolkit enabling global air quality experiments

This SBIR Phase II project seeks to develop an internet-of-things (IoT) data collection and analysis platform for collaborative STEM and big data research and education, that enables collaborative, geographically dispersed collection of data from internet enabled scientific instruments. STEM (science, technology, engineering, and math) jobs are on the rise: the U.S. Department of Commerce predicts that occupations in these sectors will grow by 8.9 % from 2014 to 2024. Yet the U.S. currently faces a critical shortage of workers and students who are proficient in math and science subjects. In part, this shortage is due to a lack of interest in STEM-related fields by minorities and women. Engaging, relevant and hands on experiences are needed to encourage interest amongst these populations. This project fulfills the requests of the federal government and leading-edge STEM educators that both secondary and post-secondary institutions teach science in a way that engages students with real-world problems. The expectation is that this project will make big data accessible, while providing rewarding and appealing hands-on learning opportunities that will increase data literacy; increase scientific collaboration in education across geographic and interdisciplinary lines; and increase scientific literacy and interest across demographics, thus increasing the likelihood that students will continue to pursue scientific careers. The proposed technology will be the first collaborative educational IoT STEM platform to be developed, and is innovative in the field of Educational Technology, which has yet to adopt web-connected sensors that generate big data on a global scale. At present, there is no mechanism for schools to collect and share real data between classrooms and schools in an organized way. The proposed innovation allows users to communicate via a global network and is capable of being paired with an unlimited variety of scientific instruments and data sources, to support versatile, engaging, coordinated, multi-school experiments and data sharing. Data science now impacts virtually every profession in some way, and the platform will uniquely expose students to big data analytics in an engaging and relevant way. In Phase 1, feasibility of approach was firmly established. Phase II objectives will be to expand the educational platform developed in Phase I to optimize national/global impact and support applicability to big data research as well as a range of sensors. Goals include to expand tools to view and analyze data, refine and expand curriculum, develop an Application Programming Interface and create software tools to manipulate and share data/curriculum. The platform's ability to promote greater learning will also be evaluated. 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.