How to Make Engineering Statistics More Appealing to Millennial Students

A one-semester calculus-based course in Engineering Statistics is taught in almost all engineering colleges, and is viewed as a “tools” course versus courses focused on engineering concepts and principles. Most current engineering faculty members were undergrads in 19702010 and graduate students 1975-2015. We argue that the way many of us learned probability and statistics, even as graduate students, does not support engagement and appeal to Millennial students. The purpose of this paper is to recommend adapting new pedagogical methods to the accepted topics in an introductory probability and statistics course for engineering undergraduates—methods that better match the learning characteristics of Millennial students in our courses. In a nutshell, those characteristics may be summarized as: (1) They want relevance to their major, and future engineering career; (2) They want rationale (for the textbook selected, and for specific course policies and assignments); (3) They revel in technology (to collect data, compute, communicate, and multi-task); (4) They want a relaxed, hands-on environment; (5) They prefer instructors who rotate among several classroom delivery methods. Considering the “Five R‟s” learning characteristics of Millennial students, and recommendations of highly respected engineering statistics educators, we suggest a modification of the introductory probability and statistics course for engineers, adapted as follows: (1) Use textbooks that have a plethora of examples and exercises from the students' major fields; (2) Establish student rapport and respect for experience of the textbook author and the instructor while avoiding authoritarian style; (3) Use a statistical package integrated into the textbook, with inclass tutorials and homework solutions that require use of the package; (4) Use of the Quincunx and Stat-a-pult® training devices, for in-class demonstrations; (5) Alternate between lecture, problem solving, software tutorial, and physical demonstration. There is a long history of articles similar to this one attempting to identify best practices in teaching probability and statistics to engineering students, who are often mixed together in sections with more than one major. Sixteen articles or books from the past 40 years are referenced to provide the foundation to support the course concept we recommend. Through review of the well-documented learning characteristics of today‟s Millennial students, the observations of well-known engineering educators, and our own experiences teaching engineering statistics courses the past 30 years, we have recommended a multi-faceted approach to modernize the introductory engineering statistics course. Hopefully other instructors, whether new or seasoned, can benefit from these recommendations.