Creating Data-Driven Undergraduate Student Engineering Typologies to Shape the Future of Work

David Pistrui; Nassif E. Rayess

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Conference: 2021 ASEE Virtual Annual Conference Content Access
Location: Virtual Conference
Publication Date: July 26, 2021
Start Date: July 26, 2021
End Date: July 19, 2022
Conference Session: College Industry Partnerships Division Poster Session
Tagged Division: College Industry Partnerships
Page Count: 15
DOI: 10.18260/1-2--36873
Permanent URL: https://peer.asee.org/36873
Download Count: 520

Paper Authors

biography

David Pistrui University of Detroit Mercy

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David Pistrui, Ph.D., is an executive, entrepreneur, and educator with over 30 years of experience serving the corporate, nonprofit, and education sectors. In 1993, David founded Acumen Dynamics, LLC, a global advisory firm that serves the public and private sectors.

David has held faculty appointments at University of Detroit Mercy, Fayetteville State University, and Illinois Institute of Technology, He has co-authored over 60 publications in the areas of growth strategies, family business, and engineering.

David has held corporate leadership positions with VideoCart, MediaOne, Parade Publications, Time Inc., and Purex Industries. He has worked with a wide range of organizations including Tenneco, KPMG, Motorola, Wrigley, IBM, Comarch, GrubHub, Minnetronix, Cleversafe, Siemans, and Dentsu, among many others.

David holds a Ph.D. (Cum Laude) in Applied Economics, Entrepreneurship and Strategy, from Universitat Autonoma de Barcelona, a Ph.D., in Sociology from the University of Bucharest, a Master of Arts in Liberal Studies from DePaul University, and a Bachelor of Business Administration, in Marketing and Economics from Western Michigan University.

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biography

Nassif E. Rayess University of Detroit Mercy

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Nassif Rayess is Professor and Chair of Mechanical Engineering at University of Detroit Mercy. He was part of the efforts to introduce entrepreneurially minded learning to the University as part of the KEEN Network and Engineering Unleashed. He is also directly involved in the curricular elements of the co-op program at the University, and teaches the professional development courses that bookends the co-op semesters. He received his Ph.D. from Wayne State University and joined Detroit Mercy in 2001.

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Abstract

Creating Data-Driven Undergraduate Student Engineering Typologies to Shape the Future of Work

The engineering profession is overwhelmingly practiced in complex organizational settings. An engineering student graduating today will likely have to navigate an interdisciplinary and intergenerational maze. Compounding matters is that engineering curricula have struggled to keep pace with the acceleration of emerging technologies. Thus, it is not inconceivable that graduating engineers could find themselves working with people from three other generations (Baby Boomers to Gen Y) from different disciplines (engineering and otherwise) on technologies that are not part of their core training. Thus, there is little room for misalignment in personal characteristics within workgroups. This can be best remedied if engineering students develop a deep understanding of themselves and, more importantly, if they are able to articulate that understanding during the hiring, onboarding processes.

There are a number of ways where a student can gain such deep understanding. In this work, the TTI TriMetrix® DNA assessment suite was used. The TTI TriMetrix® DNA assessment suite is designed to increase the understanding of an individual's talents in three distinct areas: competencies, motivators and behavioral traits. There is extensive information in the individual TTI reports (over fifty dimensions) that informs and guides the student toward a deep understanding. However, that can be a double-edged sword in that it is hard to communicate out and even harder for engineering programs and hiring organizations to find it actionable.

It is the need for actionability that has driven the necessity to distill the TTI dimensions into typologies that are the central part of this work. For engineering education programs, these typologies will make it possible to devise curricular and co-curricular elements to help students improve themselves and ultimately become effective in the workplace in a shorter period of time. They could also better inform a student’s choice of core-curriculum courses, electives and selecting minors or micro credentialing. For hiring organizations, these typologies will better inform their hiring decision and, more importantly, the placements of undergraduate engineering students where they can be most successful, productive and create work life balance.

The typologies are statistically derived from the TTI reports of over 200 students who took the assessment suite between 2017 and 2020. These typologies are matched with generalized categories of engineering jobs to provide new insights and techniques for strengthening the engineering talent pipeline and proactively help shape the future of work.

Citation
Format

Pistrui, D., & Rayess, N. E. (2021, July), Creating Data-Driven Undergraduate Student Engineering Typologies to Shape the Future of Work Paper presented at 2021 ASEE Virtual Annual Conference Content Access, Virtual Conference. 10.18260/1-2--36873

TY - CPAPER
AB - Creating Data-Driven Undergraduate Student Engineering Typologies to Shape the Future of Work

The engineering profession is overwhelmingly practiced in complex organizational settings. An engineering student graduating today will likely have to navigate an interdisciplinary and intergenerational maze. Compounding matters is that engineering curricula have struggled to keep pace with the acceleration of emerging technologies. Thus, it is not inconceivable that graduating engineers could find themselves working with people from three other generations (Baby Boomers to Gen Y) from different disciplines (engineering and otherwise) on technologies that are not part of their core training. Thus, there is little room for misalignment in personal characteristics within workgroups. This can be best remedied if engineering students develop a deep understanding of themselves and, more importantly, if they are able to articulate that understanding during the hiring, onboarding processes.

There are a number of ways where a student can gain such deep understanding. In this work, the TTI TriMetrix® DNA assessment suite was used. The TTI TriMetrix® DNA assessment suite is designed to increase the understanding of an individual&#39;s talents in three distinct areas: competencies, motivators and behavioral traits. There is extensive information in the individual TTI reports (over fifty dimensions) that informs and guides the student toward a deep understanding. However, that can be a double-edged sword in that it is hard to communicate out and even harder for engineering programs and hiring organizations to find it actionable.

It is the need for actionability that has driven the necessity to distill the TTI dimensions into typologies that are the central part of this work. For engineering education programs, these typologies will make it possible to devise curricular and co-curricular elements to help students improve themselves and ultimately become effective in the workplace in a shorter period of time. They could also better inform a student’s choice of core-curriculum courses, electives and selecting minors or micro credentialing. For hiring organizations, these typologies will better inform their hiring decision and, more importantly, the placements of undergraduate engineering students where they can be most successful, productive and create work life balance.

The typologies are statistically derived from the TTI reports of over 200 students who took the assessment suite between 2017 and 2020. These typologies are matched with generalized categories of engineering jobs to provide new insights and techniques for strengthening the engineering talent pipeline and proactively help shape the future of work.

AU - David Pistrui
AU - Nassif E. Rayess
CY - Virtual Conference
DA - 2021/07/26
PB - ASEE Conferences
TI - Creating Data-Driven Undergraduate Student Engineering Typologies to Shape the Future of Work
UR - https://peer.asee.org/36873
DO - 10.18260/1-2--36873
ER -