What do Transfer Students Have to Say: An Analysis of the Experience of Transfer Students through Topic Modeling
- Conference
- Location
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Portland, Oregon
- Publication Date
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June 23, 2024
- Start Date
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June 23, 2024
- End Date
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July 12, 2024
- Conference Session
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Voices of Diversity: Perspectives and Experiences in STEM Education
- Tagged Division
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Minorities in Engineering Division(MIND)
- Permanent URL
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https://peer.asee.org/48267
Paper Authors
Claire MacDonald The University of Texas at El Paso
Claire MacDonald is a research assistant at the University of Texas at El Paso and she is currently conducting data analysis using Natural Language Processing on online surveys. She likes to visit and explore the National Parks nearby her hometown of El Paso, Texas.
Palvi Aggarwal The University of Texas at El Paso
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.
Xiwei Wang Northeastern Illinois University
Xiwei Wang is the Department Chair and an Associate Professor of Computer Science at Northeastern Illinois University. He earned his Ph.D. in Computer Science from the University of Kentucky and. 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.
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.
Shebuti Rayana
The State University of New York at Old Westbury
orcid.org/0000-0001-9602-5234
Shebuti Rayana is an Assistant Professor of Computer and Information Sciences at the State University of New York at Old Westbury (SUNY OW). She received 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 and sentiment towards chatGPT from social media platforms.
Rudy Caraballo
Sherrene Bogle Cal Poly Humboldt
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.
Abstract
In recent years, there has been a notable rise in an alternative route to achieving higher education: a growing number of students are transitioning from 2-year colleges to 4-year institutions to complete their undergraduate degrees. Transfer students are a minority among the 4-year institution student population, many being first-generation, low-income, and racial minorities. To understand how to assist these underrepresented students, the question is: what are the most significant factors influencing the decision to attend a 2-year institution and transfer instead of immediately attending a 4-year institution? An online survey, which was anonymous and confidential, of 161 students in computing majors provides invaluable information about the transfer process for underrepresented students. This paper analyzed the demographic information along with the five open-ended questions asked to the participants of the survey. Participants’ responses reveal the influence of their families, social media, and advisors and how aspects of their identity have affected their decisions. To gain a deeper understanding of this data, NLTK and Pandas libraries are used to clean the data, WordCloud library is used to generate word clouds and three topic modeling algorithms including unsupervised (i.e., Latent Dirichlet Allocation (LDA)), semi-supervised (i.e., Correlation Explanation (CorEx)), and pre-trained (i.e., Bidirectional Encoder Representations from Transformers(BERTopic)) models are used to identify critical issues regarding students’ transfer decision. Responses are first cleaned, aggregated, and visualized into word clouds; separate word clouds are generated for each question to reveal critical factors. With the aggregated analysis of word clouds and topic-modeling results, it becomes evident that cost, career opportunities, financial aid, distance from home, and guidance from family are the key factors influencing the decision between 2-year and 4-year institutions. The biggest challenges in the participants’ transition were transferring credits, difficult classes, working while attending school and overall adjusting to a 4-year institution. These findings can be used to help transfer students succeed in their 4-year institution and beyond in their careers.
MacDonald, C., & Aggarwal, P., & Wang, X., & Wan, Y., & Rayana, S., & Caraballo, R., & Bogle, S. (2024, June), What do Transfer Students Have to Say: An Analysis of the Experience of Transfer Students through Topic Modeling Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. https://peer.asee.org/48267
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