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An analysis of relationships between course descriptions and student enrollment patterns

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Conference

2023 ASEE Annual Conference & Exposition

Location

Baltimore , Maryland

Publication Date

June 25, 2023

Start Date

June 25, 2023

End Date

June 28, 2023

Conference Session

Power Engineering & Curriculum Innovations

Tagged Division

Electrical and Computer Engineering Division (ECE)

Tagged Topic

Diversity

Page Count

11

DOI

10.18260/1-2--42607

Permanent URL

https://peer.asee.org/42607

Download Count

151

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Paper Authors

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Agoritsa Polyzou Florida International University

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Agoritsa Polyzou is an Assistant Professor at the Knight Foundation School of Computing and Information Sciences in Florida International University (FIU), Miami. Agoritsa received the bachelor’s degree in computer engineering and informatics from the University of Patras, Greece, and her Ph.D. degree in computer science and engineering from the University of Minnesota. Next, she was a Postdoctoral Fritz Family Fellow with the Massive Data Institute of McCourt School of Public Policy at Georgetown University, Washington, DC. She is involved in projects in the intersection of education, data mining, machine learning, ethics, and fairness. Her research interests include data mining, recommender systems, predictive models within educational contexts, and the fairness concerns that arise from their use. Her goal is to help students succeed using data and machine learning models.

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Joaquin Molto Florida International University

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Joaquin Molto is a Florida International University student who has earned his B.S. in Computer Science with a Minor in Mathematical Sciences. He is currently pursuing his M.S. in Computer Science and is passionate about Software Engineering, AI, and Machine Learning. Throughout his academic career, Joaquin has demonstrated a keen aptitude for programming, developing his skills in numerous programming languages, including Python, Java, C++, and C. He has also gained practical experience working on various software engineering projects, including designing and implementing efficient algorithms, creating user-friendly interfaces, and optimizing application performance. Joaquin is particularly interested in the applications of AI and machine learning to solve complex problems, and he has already started exploring these areas through his coursework, personal projects, and research.

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Nicholas Sean Gonzalez Florida International University

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Graduate AI/ML Researcher at Florida International University

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Trina L. Fletcher Florida International University Orcid 16x16 orcid.org/0000-0002-1765-5957

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Dr. Fletcher is currently an Assistant Professor at Florida International University. Her research focus equity and inclusion within STEM education, STEM at HBCUs and K-12 STEM education. Prior to FIU, Dr. Fletcher served as the Director of Pre-college Pr

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Sophia Tavio Perez

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Abstract

Over the last years, there have been great efforts to improve the diversity of engineering and computing education. Our work contributes to these efforts by following a data-driven approach to analyze easily accessible data and provide insights that could potentially lead to meaningful and impactful interventions. In particular, we focus on course selection and how we can balance the percentage of women in courses across engineering and computing courses. Deciding which courses to take every semester can be challenging for students. One of the factors influencing their decisions is the descriptions of the available courses. By reading these, the students get a first impression of the type and content of a course. This study reviews course descriptions offered by the college of engineering and computing. We employ natural language processing (NLP) approaches to identify patterns in the language used in course descriptions and how this relates to the student enrollment and descriptive characteristics of the different departments and courses. Our ultimate goal is to identify and quantify how different course descriptions are from different majors as these relate to the student gender distribution. Our language analysis indicates that adjectives and adverbs have the most significant impact on differentiating course descriptions and highlighting differences across the different programs and across the different variables of focus. Implications of this work and the impactful dissemination include sharing results with faculty and staff within the college during departmental and college-wide meetings to encourage meaningful course description changes for their courses. This research adds significantly to the literature as there is very little research on the impact of course descriptions on students’ course selection process.

Polyzou, A., & Molto, J., & Gonzalez, N. S., & Fletcher, T. L., & Tavio Perez, S. (2023, June), An analysis of relationships between course descriptions and student enrollment patterns Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--42607

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