Asee peer logo

Exploring the Quality of Course Deployment in Engineering Education: A Quantitative Assessment using Quality Function Deployment

Download Paper |

Conference

2022 ASEE Annual Conference & Exposition

Location

Minneapolis, MN

Publication Date

August 23, 2022

Start Date

June 26, 2022

End Date

June 29, 2022

Conference Session

Technology Integration in Manufacturing Curriculum

Page Count

12

DOI

10.18260/1-2--41845

Permanent URL

https://peer.asee.org/41845

Download Count

202

Paper Authors

biography

Aditya Akundi The University of Texas Rio Grande Valley

visit author page

ADITYA AKUNDI is an assistant professor in the department of Informatics and Engineering Systems at theUniversity of Texas Rio Grande Valley (UTRGV). Dr. Akundi received hisPhDat the University of Texas at El Paso (UTEP) in 2016. In his doctoral thesis, he investigated the use of information theory to understand and assess complex socio-technical systems. Before joining UTRGV, he worked as a research assistant professor in the Industrial Manufacturing and Systems Engineering department at
UTEP for a period of three years from 2016 to 2019. Dr. Akundi published several papers in the field of systems modeling, systems testing, assessing INCOSE Handbook, model-based systems engineering, and engineering education. His research has received funding from the National Science Foundation (NSF) and is currently an I-DREAM4D Department of Defense (D0D) Fellow at UTRGV.He is a member of INCOSE and ASEE. He received the outstanding junior faculty award from the ASEE Manufacturing division in 2017 and 2018 and currently serves as the program chair of the ASEE manufacturing division.

visit author page

author page

Tzu-liang Tseng University of Texas at El Paso

biography

Md Fashiar Rahman University of Texas at El Paso

visit author page

Dr. Md Fashiar Rahman is a Research Assistant Professor of industrial applied research at The University of Texas at El Paso Department of Industrial, Systems and Manufacturing. He holds a PhD degree in Computational Science Program. He has years of research experience in different projects in the field of image data mining, machine learning and deep learning for industrial and healthcare applications. In addition, Dr. Rahman has taught many different engineering courses in industrial and manufacturing engineering. His research area covers advanced quality technology, AI application in smart manufacturing, health care applications, and computational intelligence/data analytics.

visit author page

biography

Richard Chiou Drexel University

visit author page

Dr. Richard Y. Chiou is a Full Professor within the Engineering Technology Program in the Department of Engineering, Society, and Leadership at Drexel University, Philadelphia, USA. His educational background is in manufacturing with an emphasis on mechatronics. In addition to his many years of industrial experience, he has taught many different engineering and technology courses at undergraduate and graduate levels. His tremendous research experience in manufacturing includes environmentally conscious manufacturing, Internet based robotics, and Web based quality. In the past years, he has been involved in sustainable and digital manufacturing for maximizing energy and material recovery while minimizing environmental impact.

visit author page

Download Paper |

Abstract

Due to the rapid changes of the industrial landscape, engineering education is becoming more dynamic in meeting the needs of the 21st century. Many industries may likely prefer special skills over traditional degrees, which necessitates the to keep updating our course curricula in response to the required skillsets. At the same time, it is very important to understand students’ perceptions of this rapidly changing educational portfolio. This paper attempts to explore how our rapidly changing course curricula can develop students’ skillsets while maintaining their expectations and adaptability. To do so, we conduct a well-organized anonymous student survey on the different aspects of a particular course and evaluate using the Quality Function Deployment (QFD) tool, subsequently. The course titled “Design for Manufacturability” (MFG 5311) is used as the case study in this study, where 17 students enrolled in this course were considered as the study population. The course was offered as one of the core courses of the Industrial, Manufacturing, and Systems Engineering (IMSE) department at the University of Texas at El Paso (UTEP) in the Spring 2021 Semester. From this study, we extract several key findings regarding curricular enhancement, students’ expectations, and technical skillsets development from students’ perspectives.

Akundi, A., & Tseng, T., & Rahman, M. F., & Chiou, R. (2022, August), Exploring the Quality of Course Deployment in Engineering Education: A Quantitative Assessment using Quality Function Deployment Paper presented at 2022 ASEE Annual Conference & Exposition, Minneapolis, MN. 10.18260/1-2--41845

ASEE holds the copyright on this document. It may be read by the public free of charge. Authors may archive their work on personal websites or in institutional repositories with the following citation: © 2022 American Society for Engineering Education. Other scholars may excerpt or quote from these materials with the same citation. When excerpting or quoting from Conference Proceedings, authors should, in addition to noting the ASEE copyright, list all the original authors and their institutions and name the host city of the conference. - Last updated April 1, 2015