Deeper Learning of Data Science (DLDS): Studying Real-world Experiences of Engineering Professionals to Prepare the Future Workforce

George Mason University, Fairfax VA

Investigators

Abstract

George Mason University will address the lack of experts trained in the processing, analysis and use of large-scale digital data by conducting a study of how engineers in different fields currently work with this type of data. If properly analyzed and utilized, digital data can lead to useful insights that can help empower people, companies, and government agencies across a range of efforts from education and healthcare, to the design of drones and aircrafts. The knowledge gained from this study will be used to design curricular materials to train future engineers to work with data. In particular, case studies of how data can be used for impact will be generated and tested within a class. These case studies will be available for use by others who want to provide similar training. A broad range of topics will be included in this work and the research study will learn from the perspectives of a diverse range of engineers, with particular emphasis on learning from those who are typically underrepresented in engineering. Data Science has been identified as a critical domain in which trained practitioners are hard to find. The intellectual merit of this project is its study of how engineering professional work on data-intensive projects in order to understand the knowledge, skills, and competencies required for their work. Case studies will be developed to train undergraduate engineering students. Two interrelated theoretical approaches 'Professional Vision' and 'Disciplined Perception' will be leveraged to conceptualize this study with the following research questions: 1) What contextual challenges do data professionals face while conducting data-intensive work and how do they overcome them; 2) What techniques, professional expertise, and domain-specific knowledge do they draw on for their work; 3) What knowledge do they transfer from prior experiences and what new knowledge do they learn of necessity and how do they acquire it? A mixed-methods field study comprised of interviews and surveys will be conducted. Thirty professionals will be interviewed twice over a period of two years (60 interviews) and a survey of 250 participants will be conducted. The research has promise for advancing student learning, by providing overall guidance on data science skills desired by the industry, and by advancing understanding of how professional engineering work has changed.