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Multi-Institution Study of Student Demographics and Stickiness of Computing Majors in the USA

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Conference

2021 CoNECD

Location

Virtual - 1pm to 5pm Eastern Time Each Day

Publication Date

January 24, 2021

Start Date

January 24, 2021

End Date

January 28, 2021

Conference Session

CoNECD Session : Day 1 Slot 3 Technical Session 1

Tagged Topics

Diversity and CoNECD Paper Submissions

Page Count

10

Permanent URL

https://peer.asee.org/36110

Download Count

28

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Paper Authors

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Leila Zahedi Florida International University Orcid 16x16 orcid.org/0000-0002-7325-1025

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Leila Zahedi is a Ph.D. Candidate in the School of Computing and Information Science (SCIS) at Florida International University. She has two Master's degrees in Information Systems Management and Computer Science from Yazd University and Florida International University. She received her Bachelor's degree in Computer Engineering from the University of Isfahan in 2010. Her research interests include educational data science, machine learning, and deep learning.

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Hossein Ebrahiminejad Purdue University at West Lafayette (COE)

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Hossein Ebrahiminejad is a Ph.D. student in Engineering Education at Purdue University. He completed his M.S. in Biomedical Engineering at New Jersey Institute of Technology (NJIT), and his B.S. in Mechanical Engineering in Iran. His research interests include student pathways, educational policy, and quantitative research methods.

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Monique S. Ross Florida International University Orcid 16x16 orcid.org/0000-0002-6320-636X

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Monique Ross earned a doctoral degree in Engineering Education from Purdue University. She has a Bachelor’s degree in Computer Engineering from Elizabethtown College, a Master’s degree in Computer Science and Software Engineering from Auburn University, eleven years of experience in industry as a software engineer, and three years as a full-time faculty in the departments of computer science and engineering. Her interests focus on broadening participation in engineering through the exploration of: 1) race, gender, and identity in the engineering workplace; 2) discipline-based education research (with a focus on computer science and computer engineering courses) in order to inform pedagogical practices that garner interest and retain women and minorities in computer-related engineering fields. 

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Matthew W. Ohland Purdue University at West Lafayette (COE) Orcid 16x16 orcid.org/0000-0003-4052-1452

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Matthew W. Ohland is Associate Head and the Dale and Suzi Gallagher of Professor of Engineering Education at Purdue University. He has degrees from Swarthmore College, Rensselaer Polytechnic Institute, and the University of Florida. His research on the longitudinal study of engineering students, team assignment, peer evaluation, and active and collaborative teaching methods has been supported by the National Science Foundation and the Sloan Foundation and his team received for the best paper published in the Journal of Engineering Education in 2008, 2011, and 2019 and from the IEEE Transactions on Education in 2011 and 2015. Dr. Ohland is an ABET Program Evaluator for ASEE. He was the 2002–2006 President of Tau Beta Pi and is a Fellow of the ASEE, IEEE, and AAAS.

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Stephanie J. Lunn Florida International University

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Stephanie J. Lunn is a Ph.D. candidate in the School of Computing and Information Sciences at Florida International University (FIU). Her research interests span the fields of computing education, human computer interaction, data science, and machine learning. Previously, Stephanie received her B.S. and M.S. degrees in Neuroscience from the University of Miami, in addition to a B.S. degree in Computer Science from FIU.

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Abstract

Graduation and participation rates in science, technology, engineering, or mathematics (STEM) careers are a worldwide concern because of the shortage of STEM professionals in STEM fields. When you disaggregate and look even further at computing fields it is clear that while there is a high need for computer professionals in the industry, enrollment in computing programs has not kept pace with that demand. This is further exacerbated when the data is disaggregated on the basis of race and gender. Exploring patterns regarding race/ethnicity and gender can help education researchers and the computing community reveal the hidden stories that help them provide guidelines, strategies, or mechanisms that lead to enhancing the persistence of underrepresented minority students in these fields. This study is a Work-In-Progress (WIP) that was conducted using a subset of a larger longitudinal database - Multiple-Institution Database for Investigating Engineering Longitudinal Development (MIDFIELD) to determine the stickiness of students in computing fields across multiple U.S. institutions. In a degree program, “stickiness” measures the tendency for a program to retain students in the program until they graduate. Stickiness is distinct from the notion of performance. For the purposes of this study, stickiness measures the likelihood of graduation for students who have been enrolled in a computing program (the fraction that “stick” to the program or persist). In this study, we used the MIDFIELD database, which includes more than one and half million undergraduate students among 22 partner institutions across the U.S. This subset includes 50 thousand students among 14 partner institutions. We only included students who had the opportunity to graduate within 6 years of matriculation and identified students who at some point were enrolled in one of a set of computing disciplines; namely computer engineering, software engineering, computer science, information systems, and information technology. We then disaggregated students based on their race/ethnicity and gender to calculate their stickiness for each of these groups. Preliminary findings confirm variations in disciplinary stickiness by race/ethnicity and gender in computing majors, for example, Asian males and females, as well as White males, tend to have the highest stickiness. Meanwhile, Black males and females, as well as Hispanic females tend to have the lowest stickiness. Regardless of the different persistent rates among different race/ethnicity groups mentioned in this paper, the stickiness rate of computing majors is below the average stickiness of other STEM majors. This is an indication to revisit these majors to not only seek solutions to overcome the race/ethnicity and gender gaps, but also to investigate solutions to increase the stickiness rate for these majors. We anticipate findings from this ongoing research to be beneficial to the computing and education community, as well as education researchers. Computing students show different patterns of persistence from engineering students, so it is important to explore the pathways of computing students specifically. This research will help these groups to better understand the relative successes of computing students, which will be of interest to communities such as Grace Hopper Celebration of Women in Computing (GHC), the TAPIA Conference, the American Society of Engineering Education (ASEE) and etc.

Zahedi, L., & Ebrahiminejad, H., & Ross, M. S., & Ohland, M. W., & Lunn, S. J. (2021, January), Multi-Institution Study of Student Demographics and Stickiness of Computing Majors in the USA Paper presented at 2021 CoNECD, Virtual - 1pm to 5pm Eastern Time Each Day . https://peer.asee.org/36110

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