EHR Core Research: Production Engineering Education and Research (ECR: PEER)

The EHR Core Research program (ECR) supports fundamental STEM education research. This includes both projects that undertake, and initiatives that build capacity to conduct, fundamental research on STEM learning and learning environments, broadening participation in STEM fields, and STEM workforce development. In September 2018, the National Science Foundation (NSF) and The Boeing Company announced a partnership through which Boeing invested $10 million to accelerate training in critical skill areas in science, technology, engineering and math (STEM) fields. As part of this partnership, the EHR Core Research: Production Engineering Education and Research (ECR: PEER) initiative was launched in FY19 to support foundational research arising from the design, development, and deployment of creative online curricula that provide learners at various levels with skills in five focal areas: model-based systems engineering, software engineering, mechatronics, data science, and artificial intelligence. ECR: PEER invited proposals to design, develop, deploy, and study the effectiveness of online courses in any one of these focal areas using the theories and tools of the learning sciences. Additionally, ECR: PEER welcomed proposals to convene experts in the academic, for-profit, and non-profit sectors to imagine the future of production engineering education for one of the five focal areas.

While no new proposals are being accepted under the ECR: PEER solicitation (NSF 19-557) at this time, NSF continues to seek new proposals and supplemental funding requests to support research that complements this effort to accelerate training in critical skill areas for the future U.S. workforce. As described in NSF’s Dear Colleague Letter: STEM Workforce Development Utilizing Flexible Personal Learning Environments (NSF 19-025), NSF seeks proposals that will broadly inform development of personalized learning systems or generalize the research results generated during the deployment of online courses. This could be accomplished either by using the data generated by those systems or by studying the systems themselves.