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Work-in-Progress: Internet of Things Enabling Remote Student Learning

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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

Experimentation and Laboratory-Oriented Studies Division Technical Session 5: Remote, Hands-On Laboratories

Page Count

9

DOI

10.18260/1-2--41520

Permanent URL

https://peer.asee.org/41520

Download Count

185

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Paper Authors

biography

Lifford McLauchlan Texas A&M University - Kingsville

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Dr. Lifford McLauchlan is an Associate Professor and Interim Chair in the Electrical Engineering and Computer Science Department at Texas A&M University - Kingsville, and has also worked for Raytheon, Microvision, AT&T Bell Labs, and as an ONR Distinguished Summer Faculty at SPAWAR San Diego, CA. He has over 55 publications covering areas such as adaptive and intelligent controls, robotics, an ocean wave energy converter, green technology, education, wireless sensor networks and image processing. He is a co-inventor on 3 US patents related to control systems. Dr. McLauchlan is a member of ASEE and was the 2012-2014 Chair of the Ocean and Marine Engineering Division. He is also a member of IEEE (senior member), SPIE, Eta Kappa Nu, ACES and Tau Beta Pi, and has served on the IEEE Corpus Christi Section Board in various capacities such as Chair, Vice Chair, Secretary and Membership Development Officer. Dr. McLauchlan has received the Dean’s Distinguished Service Award twice and the Dean’s Outstanding Teaching Award once for the College of Engineering at Texas A&M University-Kingsville.

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biography

David Hicks

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David Hicks is an Associate Professor in the Electrical Engineering and Computer Science Department at Texas A&M University-Kingsville. Previously he served as Associate Professor and Department Head at Aalborg University in Esbjerg, Denmark. He has also held positions in research labs in the U.S. as well as Europe, and spent time as a researcher in the software industry.

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Mehrube Mehrubeoglu Texas A&M University - Corpus Christi

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Dr. Mehrubeoglu received her B.S. degree in Electrical Engineering from The University of Texas at Austin. She earned her M.S. degree in Bioengineering and Ph.D. degree in Electrical Engineering from Texas A&M University. She is currently a Professor and Program Coordinator at the Department of Engineering at Texas A&M University-Corpus Christi. She is interested in multidisciplinary research in imaging applications using a variety of imaging modalities, including thermal imaging, hyperspectral imaging, and other digital imaging technologies that engage targeted sensors, spatial and spectral data processing, pattern recognition and classification. She has engaged in research in data analytics and image segmentation using AI, and more recently, in IoT implementation of sensors and actuators. She also has a special interest in pedagogical methods in teaching and learning.

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G. Beate Zimmer Texas A&M University - Corpus Christi

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Beate Zimmer received her PhD in Mathematics from the University of Illinois Urbana Champaign in 1994, initially working in Model Theory and Functional Analysis. Since 2003 she has been an Associate Professor of Mathematics at Texas A&M University - Corpus Christi. She now works in machine learning, image analysis and environmental modeling and teaches a range of course including Calculus, Analysis and Numerical Methods.

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Abstract

With advances in sensor, computing and communication technologies, Internet of Things (IoT) enabled devices are becoming more prevalent. COVID-19 forced many educational institutions to move their courses online, at least temporarily. While this rapid shift in teaching formats impacted all courses, it was especially challenging for lab-based courses in which students require physical access to equipment in order to complete their exercises and assignments. As universities transition back to “normal“ instruction formats, many have continued to offer an increased amount of online course content, including lab based courses. IoT technologies can be utilized to enable hands-on learning opportunities for students, especially those who are learning remotely.

To support remote student learning, IoT-based labs have been planned as part of the senior capstone design courses in computer science and electrical engineering at Texas A&M University-Kingsville, a Minority Serving Institution. These planned assignments will utilize a basic IoT learning kit comprised of a Raspberry Pi board (or similar basic processor board) along with a collection of sensors. The kits are available to be checked out to students, especially those who are participating in remote learning. The IoT-based lab topics include an introduction to IoT technology, connecting and reading data from sensors and logging it to a website, and remote access/control to an IoT enabled device via the internet. Utilizing the IoT learning kits, these exercises keep students engaged and involved with hands-on learning. Through this introduction to and experience with applications utilizing IoT devices and technology, students will gain a better understanding of and have the opportunity to integrate IoT technology in their senior capstone design projects.

McLauchlan, L., & Hicks, D., & Mehrubeoglu, M., & Zimmer, G. B. (2022, August), Work-in-Progress: Internet of Things Enabling Remote Student Learning Paper presented at 2022 ASEE Annual Conference & Exposition, Minneapolis, MN. 10.18260/1-2--41520

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