AstroGro STEM K-12 Education Program for I-Corps L

California Institute Of Technology, Pasadena CA

Investigators

Abstract

Developed by a team of Caltech scientists and engineers, AstroGro is a NASA award-winning 3D-printed smart pod to grow fresh food in space. It is now being expanded into a project-based learning platform to revolutionize K-12 STEM education. Several states have adopted Next Generation Science Standards (NGSS) to update K-12 STEM education. However, there is not only a deficit of tools and curriculum that will satisfy these new standards, but also a dearth of knowledge on how to teach STEM fields properly. While meeting these new standards, AstroGro technology is a platform for students to learn interdisciplinary skills (e.g. 3D printing, electrical engineering, computer programming, environmental sciences, and plant biology) and to also be enriched in core subjects (e.g. math, statistics, physics, and life sciences). Students will conduct hypothesis-driven testing and data-driven analysis through the AstroGro pod and platform which include a series of sensors that detect plant growth and its environment. The AstroGro platform also enables big-data science and the creation of citizen scientists at the K-12 level who will contribute to the big data. Not only will students learn about the real-world applications of their scientific contributions, they will further contribute to the solving of such issues. This project will involve the customer discovery process to determine viability of scaling and sustaining the educational applications of AstroGro. The pedagogical knowledge for teaching project-based interdisciplinary learning will be developed. The learning combines cutting-edge technologies based upon NASA award-winning technology. More importantly, these teaching practices will be assessed through both formative and summative assessment. New assessment methodologies will be tested. Secondly, students will act as citizen scientists in determining plant optimality through the variation of growth conditions and growth measurement. This will produce an accessible data set for various species of plants. This will enable students to engage in scientific discovery. The information from the data set can be used for improving agricultural methods.