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A Data-science Approach to Flagging Non-retention in Engineering Enrollment Data

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Conference

2020 ASEE Virtual Annual Conference Content Access

Location

Virtual On line

Publication Date

June 22, 2020

Start Date

June 22, 2020

End Date

June 26, 2021

Conference Session

Data-informed Approaches to Understanding Student Experiences and Outcomes

Tagged Division

Educational Research and Methods

Page Count

12

DOI

10.18260/1-2--33996

Permanent URL

https://peer.asee.org/33996

Download Count

61

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Paper Authors

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Mariem Boujelbene University of Louisville

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Mariem Boujelbene holds B.S. and M.S. degrees in Computer Science and Computer Engineering. She is currently pursuing a doctoral degree in Computer Science and Engineering and is a researcher at the Knowledge Discovery & Web Mining Lab, Department of Computer Science and Computer Engineering, University of Louisville. Her research interests are clustering, explainability and interpretability, and recommender systems.

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Khalil Damak University of Louisville

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Khalil Damak is a Ph.D. student in Computer Science and Engineering at the University of Louisville where he is also a researcher at the Knowledge Discovery and Web Mining Lab. He holds an M.S. degree in Computer Science and Engineering from University of Louisville and Bachelors from Tunisia Polytechnic School. His research mainly focuses on explainability in machine learning with applications to song explainable recommendation and sequence classification. His other research experience includes data science and machine learning on education data for student retention analysis and on autopsy and pediatric forensic reports for child abuse detection.

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Asuman Cagla Acun Sener University of Louisville

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Asuman Cagla Acun Sener holds B.S. and M.S. degrees in Computer Science and Computer Engineering. She is currently pursuing a doctoral degree in Computer Science at Knowledge Discovery and Web Mining Lab, Department of Computer Science and Computer Engineering, University of Louisville. She is also working as a graduate assistant. Her research interests are educational data mining, visualization, predictive modeling, and explainability.

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Jeffrey Lloyd Hieb University of Louisville

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Jeffrey L. Hieb is an Associate Professor in the Department of Engineering Fundamentals at the University of Louisville. He graduated from Furman University in 1992 with degrees in Computer Science and Philosophy. After 10 years working in industry, he returned to school, completing his Ph.D. in Computer Science Engineering at the University of Louisville’s Speed School of Engineering in 2008. Since completing his degree, he has been teaching engineering mathematics courses and continuing his dissertation research in cyber security for industrial control systems. In his teaching, Dr. Hieb focuses on innovative and effective use of tablets, digital ink, and other technology and is currently investigating the use of the flipped classroom model and collaborative learning. His research in cyber security for industrial control systems is focused on high assurance field devices using microkernel architectures.

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Campbell R. Bego University of Louisville Orcid 16x16 orcid.org/0000-0002-8125-3178

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An instructor and postdoctoral researcher in engineering education, Campbell R. Bego, PhD, PE, is interested in improving STEM student learning and gaining understanding of STEM-specific learning mechanisms through controlled implementations of evidence-based practices in the classroom. Dr. Bego has an undergraduate Mechanical Engineering degree from Columbia University, a Professional Engineering license in the state of NY, and a doctorate in Cognitive Science.

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Patricia A. Ralston University of Louisville

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Dr. Patricia A. S. Ralston is Professor and Chair of the Department of Engineering Fundamentals at the University of Louisville. She received her B.S., MEng, and PhD degrees in chemical engineering from the University of Louisville. Dr. Ralston teaches undergraduate engineering mathematics and is currently involved in educational research on the effective use of technology in engineering education, the incorporation of critical thinking in undergraduate engineering education, and retention of engineering students. She leads a research group whose goal is to foster active interdisciplinary research which investigates learning and motivation and whose findings will inform the development of evidence-based interventions to promote retention and student success in engineering. Her fields of technical expertise include process modeling, simulation, and process control.

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Olfa Nasraoui University of Louisville

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Olfa Nasraoui is Professor of Computer Engineering and Computer Science, Endowed Chair of e-commerce, and the founding director of the Knowledge Discovery and Web Mining Lab at the University of Louisville. She received her Ph.D. in Computer Engineering and Computer Science from the University of Missouri-Columbia in 1999. From 2000 to 2004, she was an Assistant Professor at the University of Memphis. Her research activities include Data Mining/ Machine Learning, Web Mining, Information Retrieval and Personalization, in particular in problems involving large multiple domain, high dimensional data, such as text, transactions, and social network data. She is the recipient of the National Science Foundation CAREER Award, and the winner of two Best Paper Awards, a Best Paper Award in theoretical developments in computational intelligence at the Artificial Neural Networks In Engineering conference (ANNIE 2001) and a Best Paper Award at the Knowledge Discovery and Information Retrieval conference in Seville, Spain (KDIR 2018). She has more than 200 refereed publications, including over 50 journal papers and book chapters and 12 edited volumes. Her research has been funded notably by NSF and NASA. She serves since 2019 as the PI of University of Louisville’s NSF funded ATHENA ADVANCE initiative. Between 2004 and 2008, she has co-organized the yearly WebKDD workshops on User Profiling and Web Usage Mining at the ACM KDD conference. She has served on the program committee member, track chair, or senior program committee of several Data mining, Big Data, and Artificial Intelligence conferences, including ACM KDD, WWW, RecSys, IEEE Big Data, ICDM, SDM, AAAI, etc. In summer 2015, she served as Technical Mentor/Project Lead at the Data Science for Social Good Fellowship, in the Center for Data Science and Public Policy at the University of Chicago. She is a member of ACM, ACM SigKDD, senior member of IEEE and IEEE-WIE. She is also on the leadership team of the Kentucky Girls STEM collaborative network.

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Abstract

This research paper discusses a new, data-driven metric for measuring retention. First and second year retention and retention rates are now well established as metrics in the engineering education research landscape, with many research studies exploring the impact of individual performance, noncognitive, and preparation characteristics on retention in engineering. Researchers at the University of Louisville, a large Research Institution in the Midwest, have compiled survey results and enrollment data for students in the engineering college since 2010, with the intention of conducting retrospective studies of engineering retention using this data. Using “degree earned in six years or less” to label students as retained makes over half the dataset unusable. First and second year retention are options, but these can have both false positives and false negatives. Using a data science pipeline, we analyzed the number of consecutive non-enrolled terms, referred to as enrollment gaps, and found that the best short-term criteria is “three consecutive semesters not enrolled in engineering.” With this criterion, we can reliably label a given student as not-retained. The proposed retention threshold approach has the following advantages: It does not rely on the requirement of earning a degree in engineering and could be applied across a variety of fields of study, it is not based on enrollment at a fixed point in time, and it can be used as the data set continues to grow. Most importantly, while other common heuristics use grades, success in certain consecutive courses, or even demographics; our method only uses enrollment (and hence enrollment gap) data. This is a significant advantage given that the enrollment data is always available; whereas other commonly used feature heuristics for retention determination are not always available or may only apply to subsets of students.

Boujelbene, M., & Damak, K., & Acun Sener, A. C., & Hieb, J. L., & Bego, C. R., & Ralston, P. A., & Nasraoui , O. (2020, June), A Data-science Approach to Flagging Non-retention in Engineering Enrollment Data Paper presented at 2020 ASEE Virtual Annual Conference Content Access, Virtual On line . 10.18260/1-2--33996

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