The Education of the Human Factors Engineer in the Age of Data Science. (December 2020)

• Record Type: Journal Article
• Authors: Hannon, Daniel; Rantanen, Esa; Sawyer, Ben; Hughes, Ashley; Darveau, Katherine; O'Donnell, Robert; Intriligator, James; Lee, John D.
• Is Part Of: Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Volume 64:Part 1(2020)
• Page Start: 480
• Page End: 484
• Publication Date: 2020-12
• Subjects: Human engineering -- Congresses; 620.8205
• DOI: 10.1177/1071181320641109
• Languages: English
• ISSNs: 1541-9312

Abstract

The continued advances in artificial intelligence and automation through machine learning applications, under the heading of data science, gives reason for pause within the educator community as we consider how to position future human factors engineers to contribute meaningfully in these projects. Do the lessons we learned and now teach regarding automation based on previous generations of technology still apply? What level of DS and ML expertise is needed for a human factors engineer to have a relevant role in the design of future automation? How do we integrate these topics into a field that often has not emphasized quantitative skills? This panel discussion brings together human factors engineers and educators at different stages of their careers to consider how curricula are being adapted to include data science and machine learning, and what the future of human factors education may look like in the coming years.