2023 ASEE Annual Conference & Exposition

Exploring Student Learning Experience of Systems Engineering Course Developed for Manufacturing and Industrial Engineering Graduates

Presented at Redefining Manufacturing Education Practices

This paper describes the introduction to the concepts and methodology of Systems Engineering to the students of a graduate Manufacturing and Industrial Engineering program in the University of xxxxxx. This graduate course was initially developed to be a part of traditional face to face lecture-based curriculum, however with the onset of COVID-19 pandemic, it was restructured to be discoursed in an online format. This paper discusses on course structure used to enforce online systems engineering learning over a period of 14 weeks. This included addressing the basic concepts of systems engineering to provide the students’ knowledge to facilitate transformation of operational needs to a well-defined system. Further, students reviewed the iterative design process of problem formulation, analysis, optimization, design synthesis, system integration, and testing along with developing an ability to compare systems engineering life cycle models from INCOSE, Department of Defense, and NASA. To measure the student understanding and the ability to translate the concepts learning to real world applications, student teams were tasked to use CanSat 2021-22 competition as a case study. The survey instruments used over the course timeline to understand student learning experience are explained.

Authors

Dr. Aditya Akundi http://orcid.org/0000-0002-8656-7002 The University of Texas Rio Grande Valley [biography]

Aditya Akundi is currently affiliated to the Manufacturing and Industrial Engineering Department, at the University of Texas Rio Grande Valley. He earned a Bachelor of Technology in Electronics and Communication Engineering from Jawaharlal Nehru Technological University, India. He earned a Master of Science in Electrical and Computer Engineering at the University of Texas at El Paso (UTEP). and a Ph.D. in Electrical and Computer Engineering, with a concentration in Industrial and Systems Engineering (ISE) in 2016. His research is focused on understanding Complex Technical and Socio-Technical Systems from an Information Theoretic approach. He has worked on a number of projects in the field of Electrical & Computer Engineering, Systems Engineering, Additive Manufacturing and Green Energy Manufacturing. His research interests are in Systems Engineering & Architecture, Complex systems, Systems testing and Application of Entropy to Complex Systems.
Dr. Immanuel Edinbarough P.E. The University of Texas Rio Grande Valley [biography]

Immanuel A. Edinbarough received his B.Sc. (Applied Sciences) degree from PSG College of Technology, University of Madras, India, his B.E.. (M.E.) degree from the Institution of Engineers, India, M.E. (Production Engineering) degree from PSG College of Te
Dr. Md Fashiar Rahman http://orcid.org/0000-0002-0437-2587 The University of Texas at El Paso [biography]

Dr. Md Fashiar Rahman is an Assistant Professor of the Industrial, Manufacturing and Systems Engineering (IMSE) Department at The University of Texas at El Paso. He holds a Ph.D. degree in Computational Science Program. He has years of research experience in different projects in the field of image data mining, machine learning, deep learning, and computer simulation for industrial and healthcare applications. In addition, Dr. Rahman has taught various engineering courses in industrial and manufacturing engineering. His research covers advanced quality technology, AI applications in smart manufacturing, health care applications, and computational intelligence/data analytics.
Dr. Amit J. Lopes [biography]

Dr. Lopes' research interests focus on additive manufacturing and its applications. His research also includes Service Systems Engineering applications to additive manufacturing, healthcare, and energy systems. He is also interested in the application of
Sergio Luna http://orcid.org/0000-0003-3058-410X University of Texas at El Paso

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