2024 ASEE Annual Conference & Exposition

Theorizing Neuro-Induced Relationships Between Cognitive Diversity, Motivation, Grit and Academic Performance in Multidisciplinary Engineering Education Context

Presented at Multidisciplinary Division Technical Session 11

Nowadays, engineers need to tackle many unprecedented challenges that are often complex, and, most importantly, cannot be exhaustively compartmentalized into a single engineering discipline. In other words, most engineering problems need to be solved from a multidisciplinary approach. However, conventional engineering programs usually adopt pedagogical approaches specifically tailored to traditional, niched engineering disciplines, which become increasingly deviated from the industry needs as those programs are typically designed and taught by instructors with highly specialized engineering training and credentials. To reduce the gap, more multidisciplinary engineering programs emerge by systematically stretching across all engineering fibers, and challenge the sub-optimal traditional pedagogy crowded in engineering classrooms. To further advance future-oriented pedagogy, in this work, we hypothesized neuro-induced linkages between how cognitively different learners are and how the linkages would affect learners in the knowledge acquisition process. We situate the neuro-induced linkages in the context of multidisciplinary engineering education and propose possible pedagogical approaches to actualize the implications of this conceptual framework. Our study, based on the innovative concept of brain fingerprint, would serve as a pioneer model to theorize key components of learner-centered multidisciplinary engineering pedagogy which centers on the key question: how do we motivate engineering students of different backgrounds from a neuro-inspired perspective?

Authors

Prof. Duy Duong-Tran United States Naval Academy [biography]

Dr. Duong-Tran is currently an Assistant Professor of Mathematics at the United States Naval Academy (USNA). Before joining USNA, he was a Postdoctoral Research Fellow at Perelman School of Medicine at the University of Pennsylvania. He held a Ph.D. from Purdue University’s School of Industrial Engineering (IE) and a graduate certificate from Purdue’s School of Engineering Education in 2022. His main research interest is at the crossroads between data science, computational neuroscience and engineering education. In engineering education domain, Dr. Duong-Tran is interesting in exploring the neuro-induced relationship between cognitive diversity, equity, and inclusion (DEI) and multidisciplinary engineering pedagoy. He was the recipient of the Ross Doctorate Research Fellowship, Magoon Teaching Excellence Award, and Bilsland Dissertation Fellowship. He had served as a search consultant and Diversity Search Advocate in multiple search committees (e.g., the Purdue’s James Solberg School of IE Head search, USNA’s search for Distinguished Military Professor for Character). At Purdue IE, he also served as a chair of health and wellness for Purdue IE Graduate Student Organization.
Mr. Siqing Wei http://orcid.org/https://0000-0002-7086-5953 Purdue University, West Lafayette [biography]

Siqing Wei received B.S. and M.S. in Electrical Engineering and Ph.D. in the Engineering Education program at Purdue University. His research interests span on three major research topics, which are teamwork, cultural diversity, and international and Asian (American) student experiences. As a research assistant, he investigates how the cultural diversity of team members impacts team dynamics and outcomes, particularly for international and Asian students. He aims to help students improve intercultural competency and teamwork competency through interventions, counseling, pedagogy, and tool selection to promote DEI. In addition, he also works on many research-to-practice projects to enhance educational technology usage in engineering classrooms and educational research.
Li Shen University of Pennsylvania [biography]

Dr. Shen obtained his Ph.D. degree in Computer Science from Dartmouth College. He is a Professor of Informatics and Radiology in the University of Pennsylvania. His research interests include medical image computing, biomedical informatics, machine learning, trustworthy AI, NLP/LLMs, network science, imaging genomics, multi-omics and systems biology, Alzheimer’s disease, and big data science in biomedicine.

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