Building on the foundations set by the AP Computer Science (CS) Principles course, this project seeks to dramatically expand access, especially for high school girls, to the most exciting and emerging frontiers of computing, such as distributed computation, the internet of things (IoT), cybersecurity, and machine learning, as well as other 21st century skills required to productively leverage computational methods and tools in virtually every profession.

Computational Modeling for Integrating Science and Engineering Design (CMISE) will conduct a series of experiments to systematically compare different computational modeling activities on 5th and 6th grade students’ engineering design processes, their understanding of engineering, science and computational thinking concepts, as well as science teachers’ confidence and ability to implement integrated STEM and computing curricula.

With support from the NSF Improving Undergraduate STEM Education Program: Education and Human Resources (IUSE: EHR), this project aims to serve the national interest by improving undergraduate understanding of data science. It will accomplish this goal by incorporating data science concepts and skill development in undergraduate courses in biology, computer science, engineering, and environmental science.

Automated design processes, especially using Machine Learning/AI techniques, require proposed systems to be evaluated across all relevant attributes, requirements, and concerns.  Traditionally, teams create models in a set of engineering tools for design evaluation data.