This project will contribute to the national need for well-educated engineers and technicians in production engineering. It will do so by developing modular courses in data science for production engineering. These courses will be developed by Northeastern University in collaboration with MassBay Community College, four Manufacturing USA institutes, and three industry partners. The overall goal of the project is to design, develop, and deploy sustainable, online courses and curricula that bridge the production engineering-oriented data science skills gap of incumbent professional engineers and entering engineers and technicians. The program will address four groups of learners: working professionals who need to upgrade their data science skills; career-transitioning learners without manufacturing backgrounds; undergraduates who wish to minor in data science for production engineering; and two-year community college students preparing for either entering the workforce or a four-year college program. The project plans to develop: 1. A modular production engineering-oriented data science curriculum with seven courses that, in turn, are comprised of modules; 2. An online course/module recommendation system to help students determine which course or module best meets their needs and current skillset; 3. Credentials including certificates and a minor in data science for production engineering. The project aims to address the production engineering-oriented data science skills gap, thus helping to meet the demand for workers in manufacturing, which is estimated to have at least two million unfilled positions between 2018 and 2028.
The project has the following objectives: 1. Identify the data science skills gap of current and future production engineering workforce; 2. Develop modular courses enabled by interactive multimedia content with active learning via on-line labs; 3. Develop a course-module recommender system; 4. Deploy developed courses through the Open edX platform and Jupyter Notebooks; 5. Study the effectiveness of online courses using theories and tools of learning science; and 6. Rigorously evaluate program objectives and outcomes via the expertise of an external evaluator. The project will address research questions that align with the project objectives, such as whether learners? prior knowledge helps or hinders learning and what mechanisms best help learners acquire self-directed learning skills. The research component of the project will include a Design Based Research approach, which includes iterative formative assessment that drives ongoing curricular improvements. Research data will include results from assessments of the fidelity of the courses/curricula in meeting industry needs, student performance data, and data from surveys and psychometric assessments. The curriculum will be designed to accommodate students with differing needs by providing multiple credentialing opportunities from course auditing, certificates in data science for production engineering and applied data science for production engineering, and a college-level minor.
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 the effectiveness of the innovative educational strategies adopted by these projects.
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.
