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Real Data and Application-based Interactive Modules for Data Science Education in Engineering

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Conference

2021 ASEE Virtual Annual Conference Content Access

Location

Virtual Conference

Publication Date

July 26, 2021

Start Date

July 26, 2021

End Date

July 19, 2022

Conference Session

NSF Grantees Poster Session

Tagged Topic

NSF Grantees Poster Session

Page Count

11

Permanent URL

https://peer.asee.org/37640

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39

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Paper Authors

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Kerul Suthar Auburn University

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Kerul Suthar was born in Gujarat, India, in 1994. He received his B.S degree in chemical engineering from Dharmsinh Desai University in Gujarat, India in 2015. He is currently pursuing his Ph.D. degree in the department of chemical engineering at Auburn University, Auburn, Alabama.
From 2015 to 2016, he was an operations engineer at Gujarat Narmada Valley Fertilizers and Chemicals Ltd in India. His research interests include the modeling and monitoring of manufacturing processes, Artificial Intelligence (AI), application of Machine learning (ML) techniques for smart and sustainable manufacturing, and Internet of Things (IoT) based sensing systems in the manufacturing industry.

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Thomas Mitchell

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Anna Claire Hartwig Auburn University

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Jin Wang Auburn University Orcid 16x16 orcid.org/0000-0002-7638-8537

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Dr. Jin Wang is B. Redd Associate Professor in the Department of Chemical Engineering at Auburn University. She obtained her BS and PhD degrees in chemical engineering (specialized in biochemical engineering) from Tsinghua University in 1994, and 1999 respectively. She then obtained a PhD degree (specialized in control engineering) from the University of Texas at Austin in 2004. From 2002 to 2006 she was a development engineer and senior development engineer at Advanced Micro Devices, Inc. During her tenure at AMD, her R&D yielded 13 patents granted by USPTO. In addition, she received several prestigious corporate awards for being instrumental in developing effective advanced control solutions.
Dr. Wang joined Auburn University in 2006 as B. Redd Assistant Professor. She was promoted to Associate Professor and granted tenure in 2011. The central theme of her current research is to apply systems engineering, in particular, control engineering principles and techniques to understand, predict and control complex dynamic systems which cover both industrial processes and microbial organisms. Currently, she has extended her research focus to metabolic network modeling and analysis, as well as related experimentations. The system identification based framework for metabolic network analysis has been proving to be a highly effective tool to extract biological knowledge from complex, genome-scale metabolic network models, and has been successfully applied to understanding several industrial relevant microbes. She was the 2008 recipient of the Ralph E. Powe Junior Faculty Enhancement Awards from Oak Ridge Associated Universities (ORAU). Her graduate student also won the inaugural AIChE CAST Director’s Presentation Award in 2011. Her research is funded by various US federal and state funding agencies including NSF, USDA, Department of Education and DOT as well as private foundations. She has over 40 journal publications, plus additional conference proceedings (>40) and presentations (>70). Her recent publications mainly focus on biotechnology and bioengineering related modeling and experimental research.

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Shiwen Mao Auburn University

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Shiwen Mao received a Ph.D. in Electrical Engineering from Polytechnic University, Brooklyn, NY in 2004. He joined Auburn University, Auburn, AL in 2006 as an Assistant Professor in the Department of Electrical and Computer Engineering. He held the McWane Endowed Professorship from 2012 to 2015 and the Samuel Ginn Endowed Professorship from 2015 to 2020. Currently, he is a Professor and Earle C. Williams Eminent Scholar Chair, and Director of the Wireless Engineering Research and Education Center (WEREC) at Auburn University. Dr. Mao's research interest includes wireless networks, multimedia communications, and smart grid. He is a Distinguished Lecturer of IEEE Communications Society (2021-2022) and IEEE Council of RFID (2021-2022) , was a Distinguished Lecturer (2014-2018) and is a Distinguished Speaker (2018-2021) of IEEE Vehicular Technology Society. He received the IEEE ComSoc TC-CSR Distinguished Technical Achievement Award in 2019, the IEEE ComSoc MMTC Distinguished Service Award in 2019, the Auburn University Creative Research & Scholarship Award in 2018, the 2017 IEEE ComSoc ITC Outstanding Service Award, the 2015 IEEE ComSoc TC-CSR Distinguished Service Award, the 2013 IEEE ComSoc MMTC Outstanding Leadership Award, and the NSF CAREER Award in 2010. He is a co-recipient of the 2021 IEEE Communications Society Outstanding Paper Award, the IEEE Vehicular Technology Society 2020 Jack Neubauer Memorial Award, the 2018 IEEE ComSoc MMTC Best Journal Paper Award, the 2017 IEEE MMTC Best Conference Paper Award, IEEE SECON 2017 Best Demo Award, Best Paper Awards from IEEE GLOBECOM 2019, IEEE GLOBECOM 2016, IEEE GLOBECOM 2015, IEEE WCNC 2015, and IEEE ICC 2013, and the 2004 IEEE Communications Society Leonard G. Abraham Prize in the Field of Communications Systems. He is an Associate Editor-in-Chief of IEEE/CIC China Communications, an Area Editor of IEEE Transactions on Wireless Communications, IEEE Internet of Things Journal, IEEE Open Journal of the Communications Society, and ACM GetMobile, and an Associate Editor of IEEE Transactions on Cognitive Communications and Networking, IEEE Transactions on Network Science and Engineering, IEEE Transactions on Mobile Computing, IEEE Multimedia, IEEE Network, and IEEE Networking Letters. He is a Fellow of the IEEE, a member of the ACM, IET, Tau Beta Pi, and Eta Kappa Nu.

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Laura Parson North Dakota State University

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Peng Zeng Auburn University

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Department of Mathematics and Statistics, Auburn University

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Bo Liu

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Peter He Auburn University Orcid 16x16 orcid.org/0000-0002-2474-5950

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Dr. Q. Peter He is Associate Professor in the Department of Chemical Engineering at Auburn University. He obtained his BS degree in chemical engineering from Tsinghua University, Beijing, China, in 1996 and MS and PhD degrees in chemical engineering in 2002 and 2005 from the University of Texas, Austin. Besides engineering education, his current research interests are in the area of systems engineering enhanced data analytics with applications in manufacturing, renewable energy, food-energy-water nexus, and broad area of disease detection/diagnosis and operation of healthcare systems. .

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Abstract

It has been recognized that jobs across different domains is becoming more data driven, and many aspects of the economy, society, and daily life depend more and more on data. Undergraduate education offers a critical link in providing more data science and engineering (DSE) exposure to students and expanding the supply of DSE talent. The National Academies have identified that effective DSE education requires both appropriate classwork and hands-on experience with real data and real applications. Currently significant progress has been made in classwork, while progress in hands-on research experience has been lacking. To fill this gap, we have proposed to create data-enabled engineering project (DEEP) modules based on real data and applications, which is currently funded by the National Science Foundation (NSF) under the Improving Undergraduate STEM Education (IUSE) program. To achieve project goal, we have developed two internet-of-things (IoT) enabled laboratory engineering testbeds (LETs) and generated real data under various application scenarios. In addition, we have designed and developed several sample DEEP modules in interactive Jupyter Notebook using the generated data. These sample DEEP modules will also be ported to other interactive DSE learning environments, including Matlab Live Script and R Markdown, for wide and easy adoption. Finally, we have conducted metacognitive awareness gain (MAG) assessments to establish a baseline for assessing the effectiveness of DEEP modules in enhancing students’ reflection and metacognition. The DEEP modules that are currently being developed target students in Chemical Engineering, Electrical Engineering, Computer Science, and MS program in Data Science at xxx University. The modules will be deployed in the Spring of 2021, and we expect to have immediate impact to the targeted classes and students. We also anticipate that the DEEP modules can be adopted without modification to other disciplines in Engineering such as Mechanical, Industrial and Aerospace Engineering. They can also be easily extended to other disciplines in other colleges such as Liberal Arts by incorporating real data and applications from the respective disciplines. In this work, we will share our ideas, the rationale behind the proposed approach, the planned tasks for the project, the demonstration of modules developed, and potential dissemination venues.

Suthar, K., & Mitchell , T., & Hartwig, A. C., & Wang, J., & Mao, S., & Parson, L., & Zeng, P., & Liu , B., & He, P. (2021, July), Real Data and Application-based Interactive Modules for Data Science Education in Engineering Paper presented at 2021 ASEE Virtual Annual Conference Content Access, Virtual Conference. https://peer.asee.org/37640

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