2025 ASEE Annual Conference & Exposition

Initial Results of Chemical and Electrical & Computer Engineering Mixed Reality Lab Modules – Work in Progress

Presented at WiP: Gen AI, Mixed Reality, and Simulations

This Work in Progress paper describes initial findings from an educational innovation project which deploys mixed reality (MR) modules in Engineering instruction. The NSF-funded undertaking is based on findings from a pilot study for chemical engineering completed with institutional resources. Under the NSF grant, we have expanded the scope and have now developed MR assets for both Chemical and Electrical & Computer Engineering instruction. The project research focuses on learning and engagement impacts from use of MR modules in classroom and lab settings for both disciplines.
The Chemical Engineering MR asset is based on the fluid flow in pipes lab, featuring a pipeline network having different pipe diameters, lengths, roughness and fittings. The students will be able to measure pressure drop as a function of fluid flow rate through the different runs of pipe. The Electrical and Computer Engineering MR asset is based on a combination of breadboard, power supply, voltage/amp meter, resistors and circuit connection cables. The students will be able to understand breadboard basics, along with series and parallel concepts and Thevenin, Norton and superposition theorems.
With both MR assets, manipulation, adjustment, and experimentation is possible simulating real world processes and outcomes. Data capture and analysis functions also are present. Students in a Chemical Engineering course and an Electrical and Computer Engineering lab completed two pre- and post-instruction assessments specific to the course topics for which MR assets were used. The first was a short quiz intended to measure learning in the topic and the second was a self-assessment designed to gauge perceived skill in relevant areas. Data analysis consisted of t and Wilcox Wilcoxon tests, as applicable, and constant comparative analysis for theme identification with qualitative input. Findings by course and assessment are discussed with potential implications for Engineering education, grounded in the original literature-based theory.

Authors

Dr. Keisha C.A. Antoine Prairie View A&M University (DUPE) [biography]

Keisha Antoine is an Assistant Professor in the chemical engineering department at Prairie View AM University where she has worked since 2019. Prior to her foray into academia, she worked as a scientist and engineer working on glass processes and functionalization, carbon footprint and energy analyses at Corning Incorporated, a Fortune 500 technology company. Keisha also has experience in providing technical solutions through her own firm, Antoine Technical Consulting LLC, where she she continues to offer process design, scale-up and water management solutions to manufacturers principally in the chemical process industries. Keisha is the holder of two patents and is a registered professional engineer in the state of Texas.
Dr. Kazeem B Olanrewaju Prairie View A&M University [biography]

Kazeem B. Olanrewaju is an assistant professor in the department of chemical engineering at Prairie View A & M University. His more than 10 years of experience in the chemical engineering profession both in the public and private sectors includes positions as Chemical/Cement Laboratory Engineer at State Highway Administration, Maryland Department of Transport., Associate Consultant at ChemProcess Technology (CPT) LLC., and Process Safety and Risk Analyst Co-op at Akzo Nobel Functional Chemical, Stenungsund, Sweden. He is a Recipient of the AIChE-Minority Affairs Committee (MAC) 30 years anniversary James Wei award and 2021 MAC distinguished service award. He is also a Recipient of the 2009 Philip Hubbard Uncommon Leadership Award, College of Engineering, at University of Iowa. He received his BS in Chemical Engineering from Obafemi Awolowo University, Ile-Ife, Nigeria and MS in Environmentally Sustainable Process Technology from Chalmers University of Technology, Goteborg, Sweden. He received another MS degree and a PhD in Chemical and Biochemical Engineering from University of Iowa, Iowa, USA.
Dr. Justin Foreman Prairie View A&M University [biography]

Dr. Foreman is an instructor at Prairie View A&M University in Electrical Engineering and at Lone Star College in Applied Technology. His areas of interest include FPGA's, fiber optics, and microwave engineering for sensing applications.
Dr. Penrose Cofie Prairie View A&M University [biography]

Dr. Penrose Cofie is a professor in Electrical and Computer Engineering at Prairie View A&M University, College of Engineering, Texas. His research interests are in Power Systems including Renewable Power Supplies, Power Electronics, Controls and Motor Drives. He is currently working on Renewable Energy Generation, Micro Grid and Advanced Electric Vehicle Technology Systems.
Dr. Michael Preuss http://orcid.org/0000-0001-8659-6164 Exquiri Consulting, LLC [biography]

Michael Preuss, EdD, is the Co-founder and Lead Consultant for Exquiri Consulting, LLC. His primary focus is providing assistance to grant project teams in planning and development, through research and external evaluation, and as publication support. Most of his work is completed for Minority-Serving Institutions and he publishes regarding findings on a regular basis.
Jorge Federico Gabitto Prairie View A&M University

Note

The full paper will be available to logged in and registered conference attendees once the conference starts on June 22, 2025, and to all visitors after the conference ends on June 25, 2025

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