2023 ASEE Annual Conference & Exposition

A Preliminary Factor Analysis on the Success of Computing Major Transfer Students

Presented at Peer Mentorship, Cross-Race Mentoring Relationships, Race, Gender, Student Success, and Career Outcomes

In STEM education, many 4-year colleges and universities now get most of their students from community colleges. Students who transfer from community colleges, especially those who are underrepresented, often face problems, such as deciding whether or not to transfer, getting academic and non-academic support during the transfer, and finding a job. Also, program advisors at both 2-year and 4-year colleges and universities face problems because they need to know how their students make transfer decisions and how to help them be successful post-transfer. A data-driven and survey-based study will help establish a solid understanding of the underlying elements contributing to these challenges. In this paper, the researchers first conduct a literature review to identify the critical personal, academic, and behavioral factors that influence the transfer decision, particularly for students from traditionally disadvantaged groups. Secondly, an exploratory analysis of these factors was performed by inviting a small group of computing major students from both community colleges and universities to participate in a survey that includes a wide range of questions, from demographics and pre-transfer decisions to post-transfer performance. Thirdly, the historical enrollment data from two institutions was analyzed to reveal the correlations among demographic information, financial status, and academic performance. The preliminary findings indicated that financial challenges, university reputation, university location, job prospects, and family expectations are the primary factors influencing student transfer decisions. Moreover, the median GPA of non-underrepresented transfer students is higher than the underrepresented transfer students. Underrepresented students who receive some form of financial aid perform better than those who do not receive any aid, but this scenario is not generalizable, as there are other factors, such as the income status of the student and their gender. The findings of the study can be beneficial to underrepresented transfer students, their advisors, and other stakeholders in higher education.

Authors

Xiwei Wang Northeastern Illinois University [biography]

Xiwei Wang is an Associate Professor and the Department Chair of Computer Science at Northeastern Illinois University. He earned his Ph.D. in Computer Science from the University of Kentucky. His primary research interests include recommender systems, data privacy, data mining, and machine learning. He has served as an associate editor, editorial board member and reviewer of international journals. He also served as a technical program committee member, session chair, and reviewer for many international conferences.
Shebuti Rayana http://orcid.org/https://0000-0001-9602-5234 SUNY, Old Westbury [biography]

Shebuti Rayana is an Assistant Professor of Computer and Information Sciences at the State University of New York at Old Westbury (SUNY OW). She earned her PhD from the Department of Computer Science at Stony Brook University. Before moving to the United States for higher studies, she completed BSc from Computer Science and Engineering at Bangladesh University of Engineering and Technology (BUET). Shebuti Rayana’s research is to build a safe and secure digital world with the help of cutting-edge Data Mining techniques. During her PhD, she was involved in several projects funded by National Science Foundation (NSF), Defense Advanced Research Projects Agency (DARPA), and R&D grant from Northrop Grumman to develop Anomaly Mining algorithms and apply them to solve real-world problems. She also worked as a Research Intern in the Information Security team at IBM Thomas J. Watson Research Center. She has been awarded two NSF: Computer and Information Science and Engineering - Minority Serving Institution (CISE-MSI) grants as a Co-PI, (1) to increase the research capacity at SUNY OW by creating the infrastructure for big data research, incorporating course embedded undergraduate research experience, and training undergraduate students in big data research through seminars, workshops, and summer bridge programs, (2) to design an AI-driven counseling system for underrepresented transfer students in collaboration with UTEP, NEIU, UHV, and Cal Poly Humboldt. Moreover, she is working on several projects on misinformation, stigma, hate speech, and cyberbullying detection from social media platforms.
Dr. Sherrene Bogle California Polytechnic, Humboldt [biography]

Dr. Sherrene Bogle is a Fulbright Scholar and alumna of the University of Georgia, USA, where she earned her PhD in Computer Science. She is currently an Associate Professor of Computer Science and Program Lead for the BS Software Engineering at Cal Poly Humboldt. Dr. Bogle has a passion for sharing and helping students to improve the quality of their lives through education, motivation and technology. She has published two book chapters, two journal articles and several peer reviewed conference papers in the areas of Machine Learning, Time Series Predictions, Predictive Analytics, Multimedia in Education and E-Learning Technologies.
Dr. Palvi Aggarwal University of Texas, El Paso [biography]

Dr. Aggarwal is an Assistant Professor in the Department of Computer Science at the University of Texas at El Paso (UTEP). Dr. Aggarwal has focused on socio-technical aspects of cybersecurity using human experiments, machine learning, and cognitive modeling. She is currently leading an interdisciplinary research lab, i.e., Psyber Security Lab at UTEP, that focuses on improving cyber defense by understanding human decision-making processes. At UTEP, Dr. Aggarwal teaches courses on Computer Security, Behavioral Cybersecurity, and Applied Computational Cognitive Modeling to undergraduate and graduate students. Dr. Aggarwal has strong interdisciplinary collaborations with various universities and such collaboration will be beneficial for this project. Dr. Aggarwal published her research work in various conferences including HFES, HICSS, ICCM, GameSec, and journals including Human Factors, Topics in Cognitive Science, and Computers & Security. Her papers in HICSS-2020 and GameSec-2020 received the best paper awards. Her professional activities include journal reviews for Computers & Security, Cybersecurity, Frontiers in Psychology, and conference reviews for HFES, AHFE, HICSS, Euro S&P, and CyberSA. She is also an advocate for the Cybersecurity Community of Practice at UTEP and a member of the Special Cyber Operations Research and Engineering (SCORE) Interagency Working Group.
Yun Wan http://orcid.org/0000-0002-9038-5607 University of Houston, Victoria [biography]

Yun Wan is a Professor of Computer Information Systems in the University of Houston- Victoria. His current research includes electronic commerce and information systems in STEM education. His other research includes text analytics, decision support systems, and enterprise systems development. His research is funded by the National Science Foundation (NSF). He serves as senior editor for Electronic Markets and an editorial board member for several journals, such as the Journal of Electronic Commerce in Organizations. He received his Ph.D. in Management Information Systems from the University of Illinois at Chicago.

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