Engineering Online Learning Pathways in Advanced Manufacturing and Data Science

Colorado School Of Mines, Golden CO

Investigators

Abstract

This project will contribute to the national need for well-educated engineers and technicians in production engineering. It will do so by supporting the design, deployment, and evaluation of modular, online data science courses for advanced manufacturing. Big data sets are now available to describe workflows, properties of materials, and manufacturing and automation processes used in advanced manufacturing. However, few manufacturing process engineers understand how to tap into this data, organize and analyze it, and use it to improve manufacturing processes. This project will develop online educational pathways of modules and courses to teach practicing engineers and other learners how to apply data science in the production manufacturing environment. The courses will be developed by the Colorado School of Mines, in collaboration with the Red Rocks Community College, Colorado Community College Online, and multiple industrial partners. Individuals from these organizations will form a convergent team with expertise in advanced manufacturing and data science, industry needs and perspectives, and educational design and learning science. This project has the potential to nurture and grow the advanced manufacturing workforce, as well as to enhance the infrastructure for online education and educational research. The project will use a convergence approach to course design by bringing together experts from university, community college, and industry, who have broad disciplinary expertise and skills in data science, advanced manufacturing, and engineering education. The resulting courses will focus on data science tools, data science methodology, foundations of advanced manufacturing, applying data science to advanced manufacturing, and ways of learning and working in the modern era. The courses will be organized into multiple educational pathways designed to serve individual learners, as well as learners in industries, two-year colleges, and four-year colleges and universities. The primary objectives of the project are to: 1. Create pathways that encourage and support diverse learners to become proficient in using data science to solve advanced manufacturing problems; 2. Use an Engineering Learning Framework developed by the Principal Investigator, to design, develop, deploy, evaluate, and disseminate sell-assessment tools, modules, and courses aligned to form learning pathways that empower diverse learners to reskill or enhance their skills to tackle advanced manufacturing problems through data science. 3. Conduct project evaluation that will guide instructional design of the courses and modules. 4. Engage industry partners and two-year institutions to provide guidance on workforce needs as well as to pilot assessments, modules, and courses. 5. Complete a research study to explore how psychological and demographic characteristics affect learner performance. Project outcomes will be presented at national conferences and the materials will be freely available on the project website. This project is funded by NSF's EHR Core Research: Production Engineering Education and Research (ECR: PEER) program, which seeks to improve the education of future and current professionals in production engineering. It also aims to study how effective the innovative educational strategies adopted by these projects are in producing their desired objectives. 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.