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Understanding Data Science Instruction in Multiple STEM Disciplines

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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

20

Permanent URL

https://peer.asee.org/37955

Download Count

22

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Paper Authors

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Caitlin Snyder Vanderbilt University

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Caitlin Snyder is a PhD student in the department of Computer Science at Vanderbilt University. Her research focuses on understanding how students work collaboratively in open-ended learning environments with the end goal of developing multimodal, semi-automated analysis tools for researchers and teachers.

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Dawit M. Asamen North Carolina Agricultural and Technical State University

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Mohammad Yunus Naseri Virginia Polytechnic Institute and State University Orcid 16x16 orcid.org/0000-0002-4537-6509

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My name is Mohammad Yunus Naseri and I am currently a first-year Ph.D. student in civil engineering at Virginia Tech. I did my master's degree also in civil engineering at Virginia Tech. Before joining Virginia Tech as a graduate student, I was a teacher with five years of productive experience. My Ph.D. research interest focus is on the uses of data science in engineering education and water use in different industrial sectors.

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Niroj Aryal North Carolina Agricultural and Technical State University

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Dr. Niroj Aryal is an assistant professor of Biological Engineering at the Department of Natural Resources and Environmental Design at the North Carolina A and T State University. His academic background includes a bachelor’s in Agricultural Engineering from Tribhuvan University, a postgraduate diploma in Environmental Education and Sustainable Development from Kathmandu University, a master’s in Biosystems Engineering from Michigan State University and a dual-major doctorate in Biosystems Engineering and Environmental Engineering from Michigan State. Dr. Aryal’s research interests are in water quality, hydrology, phytoremediation, agricultural conservation practices, urban best-management practices (BMPs), and ecological engineering. Pertaining to education, his interests are in innovative instructional techniques to enhance student motivation and learning.

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Gautam Biswas Vanderbilt University Orcid 16x16 orcid.org/0000-0002-2752-3878

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Gautam Biswas is a Cornelius Vanderbilt Professor of Computer Science, Computer Engineering, and Engineering Management in the EECS Department and a Senior Research Scientist at the Institute for Software Integrated Systems (ISIS) at Vanderbilt University. He has an undergraduate degree in Electrical Engineering from the Indian Institute of Technology (IIT) in Mumbai, India, and M.S. and Ph.D. degrees in Computer Science from Michigan State University in E. Lansing, MI.
Prof. Biswas conducts research in Intelligent Systems with primary interests in hybrid modeling, simulation, and analysis of complex embedded systems, and their applications to diagnosis, prognosis, and fault-adaptive control. He is also involved in developing simulation-based environments for learning and instruction. In his research, he has exploited the synergy between computational thinking ideas and STEM learning to develop systems that help students learn science and math concepts by building simulation models. He has also developed innovative educational data mining techniques for studying students’ learning behaviors and linking them to metacognitive strategies. Prof. Biswas is a Fellow of the IEEE and the PHM society.

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Abhishek Dubey Vanderbilt University

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Erin Henrick Vanderbilt University

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Erin R. Hotchkiss Virginia Polytechnic Institute and State University

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Dr. Erin R. Hotchkiss is an Assistant Professor in the Department of Biological Sciences and a Faculty Affiliate of the Global Change Center at Virginia Tech. She received her Ph.D. in Ecology from the University of Wyoming, a M.Sc. in Zoology and Physiology from the University of Wyoming, and a B.Sc. in Environmental Studies from Emory University. Prior to joining Virginia Tech, she worked as a postdoctoral fellow at Umeå University, Sweden and Université du Québec à Montréal, Canada. Ongoing research in the Hotchkiss Lab (www.hotchkisslab.com) is using environmental sensors, stable isotope tracers, whole-ecosystem experiments, and process-based modeling to explore how environmental change, land-water interactions, and ecosystem processes shape the transport, transformation, and fate of carbon, nutrients, and pollutants in freshwaters.

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Manoj K. Jha North Carolina Agricultural and Technical State University Orcid 16x16 orcid.org/0000-0003-1156-2992

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Dr. Manoj K Jha is an associate professor in the Civil, Architectural, and Environmental Engineering department at the North Carolina A&T State University. His research interests include hydrology and water quality studies for water resources management under land use change and climate change. His educational research interests include critical thinking and active learning.

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Steven X. Jiang North Carolina Agricultural and Technical State University

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Dr. Steven Jiang is an Associate Professor in the Department of Industrial and Systems Engineering at North Carolina A&T State University. His research interests include Human Systems Integration, Visual Analytics, and Engineering Education.

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Emily C. Kern

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Vinod K. Lohani Virginia Polytechnic Institute and State University

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Dr. Vinod K. Lohani is a Program Director at the National Science Foundation and his portfolio includes the NSF Research Traineeship (NRT), Innovations in Graduate Education (IGE), and CAREER programs. Dr. Lohani is on leave from Virginia Tech where he is a Professor of Engineering Education. During 2016-19, he served as the Director of education and global initiatives at an interdisciplinary research institute called the Institute for Critical Technology and Applied Science (ICTAS) at Virginia Tech. He is the founding director of an interdisciplinary lab called Learning Enhanced Watershed Assessment System (LEWAS) at VT. He received a Ph.D. in civil engineering from VT. His research interests are in the areas of computer-supported research and learning systems, hydrology, engineering education, and international collaboration. He has served as a PI or co-PI on 30 projects, funded by the National Science Foundation, with a $8.4 million research funding participation from external sources. He directed/co-directed an NSF/Research Experiences for Undergraduates (REU) Site on interdisciplinary water sciences and engineering at VT during 2007-19. This site has 100+ alumni to date. He also led an NSF/Research Experiences for Teachers (RET) site on interdisciplinary water research during 2016-19 with 30+ alumni. He also led an NSF-funded cybersecurity education project and served as a co-PI on two International Research Experiences for Students (IRES) projects funded by the NSF. He has published over 90 papers in peer-reviewed journals and conferences.

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Landon Todd Marston Virginia Polytechnic Institute and State University Orcid 16x16 orcid.org/0000-0001-9116-1691

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Christopher P. Vanags Vanderbilt University

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Kang Xia Virginia Polytechnic Institute and State University

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Kang Xia received her Ph.D. from the University of Wisconsin-Madison (1997), M.S. from Louisiana State University (1993), and B.S. from Beijing Agricultural University (1989). She was a Postdoctoral Researcher at the University of Wisconsin-Madison (1997-1998), an Assistant Professor at Kansas State University (1998-2001), University of Georgia (2002-2005), and Assistant Professor, Dept. of Chemistry, Mississippi State University (2006-2010), an Associate Professor at Mississippi State University (2010-2011) and at Virginia Tech (2011-2016). She also served as Director for Re-search Division and Industrial and Agricultural Services Division, Mississippi State Chemical Laboratory (2006-2011). She is currently a Professor at Virginia Tech (2016-present). She has served as adhoc reviewer for a number of scientific journals and funding agencies. She served as associate editor for the Journal of Environmental Quality and the Soil Science Society of America Journal. She is an expert on method development for analysis of organic chemicals in environmental matrixes and environmental occurrence, fate, and impact of organic chemicals. She has successfully managed and accomplished close to $11 million federal and state funded interdisciplinary environmental projects. She has published 67 peer-reviewed papers, 6 book chapters, and given 126 professional presentations. She holds membership of the American Chemical Society , the Soil Science Society of America, and SigmaXi.

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Abstract

As technology advances, data driven work is becoming increasingly important across all disciplines. Data science is an emerging field that encompasses a large array of topics including data collection, data preprocessing, data visualization, and data analysis using statistical and machine learning methods. As undergraduates enter the workforce in the future, they will need to “benefit from a fundamental awareness of and competence in data science”[9]. This project has formed a research practice partnership that brings together STEM+C instructors and researchers from three universities and an education research and consulting group. We aim to use high frequency monitoring data collected from real-world systems to develop and implement an interdisciplinary approach to enable undergraduate students to develop an understanding of data science concepts through individual STEM disciplines that include engineering, computer science, environmental science, and biology. In this paper, we perform an initial exploratory analysis on how data science topics are introduced into the different courses, with the ultimate goal of understanding how instructional modules and accompanying assessments can be developed for multidisciplinary use. We analyze information collected from instructor interviews and surveys, student surveys, and assessments from five undergraduate courses (243 students) at the three universities to understand aspects of data science curricula that are common across disciplines. Using a qualitative approach, we find commonalities in data science instruction and assessment components across the disciplines. This includes topical content, data sources, pedagogical approaches, and assessment design. Preliminary analyses of instructor interviews also suggest factors that affect the content taught and the assessment material across the five courses. These factors include class size, students’ year of study, students’ reasons for taking class, and students’ background expertise and knowledge. These findings indicate the challenges in developing data modules for multidisciplinary use. We hope that the analysis and reflections on our initial offerings has improved our understanding of these challenges, and how we may address them when designing future data science teaching modules. These are the first steps in a design-based approach to developing data science modules that may be offered across multiple courses.

Snyder, C., & Asamen, D. M., & Naseri, M. Y., & Aryal, N., & Biswas, G., & Dubey, A., & Henrick, E., & Hotchkiss, E. R., & Jha, M. K., & Jiang, S. X., & Kern, E. C., & Lohani, V. K., & Marston, L. T., & Vanags, C. P., & Xia, K. (2021, July), Understanding Data Science Instruction in Multiple STEM Disciplines Paper presented at 2021 ASEE Virtual Annual Conference Content Access, Virtual Conference. https://peer.asee.org/37955

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