Gonzalo Bello
Department of Computer Science
Gonzalo Bello
Gonzalo Bello
Gonzalo Bello is a Clinical Associate Professor in the Department of Computer Science at the University of Illinois Chicago (UIC). He teaches courses on data science, discrete math, and theory of computation and has received multiple UIC awards for excellence in teaching, including the Harold A. Simon Award (2023) and the Silver Circle Award (2022). He is also the faculty advisor of the Latinx Organization for Growth in Computing and Academics (LOGiCA), a student organization at UIC that aims to provide academic and professional support to historically underrepresented students in computing. Before joining UIC, he earned a Ph.D. and M.S. in Computer Science from North Carolina State University and a B.E. in Systems Engineering from Universidad Metropolitana in Caracas, Venezuela.
Bello is a 2024-25 Action Research Scholar.
Gonzalo Bello
Gonzalo Bello
Increasing Awareness and Fostering a Sense of Belonging in Data Science among Undergraduate Students at UIC
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Abstract
Data science is one of the fastest-growing and most in-demand fields in the U.S. In Fall 2021, UIC started offering a new data science major with core courses from computer science, mathematics, and statistics, and concentrations in areas such as business, communication, bioinformatics, health, and public policy. However, many undergraduate students at UIC have had little exposure to this field and are unaware of the career opportunities offered by this new major. Furthermore, the current data science curriculum lacks any courses created specifically for this major, particularly at the 100-level.
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As a result, many first- and second-year data science students lack a sense of belonging in their major, which has been identified as a key reason for student attrition.
To address these limitations, we have developed a workshop and a course to introduce students with no prior knowledge or experience to the field of data science. In this project, we aim to evaluate the effectiveness of this workshop and course on increasing awareness and understanding of data science and fostering a sense of identity and belonging in this field among undergraduate students at UIC.
Project Information
Project Background and Rationale
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Data science is “an interdisciplinary field that studies and applies tools and techniques for deriving useful insights from data” [1]. Data science is also one of the fastest-growing and most in-demand fields in the U.S. with jobs projected to grow 36% from 2023 to 2033 [2]. In Fall 2021, UIC started offering a new data science major with core courses from computer science, mathematics, and statistics, and concentrations in areas such as business, communication, bioinformatics, health, and public policy. However, many undergraduate students at UIC have had little exposure to this field and are unaware of the career opportunities offered by this new major. Furthermore, the current data science curriculum lacks any courses created specifically for this major, particularly at the 100-level. Instead, first- and second-year data science students take mostly computer science and statistics courses. As a result, many of them lack a sense of belonging in their major, which has been identified in the literature as a key reason for student attrition [3, 4].
To address these limitations, we have developed two different interventions to introduce undergraduate students at UIC to the field of data science:
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- Workshop: for this intervention, students will participate in a one-day introductory data science workshop. This workshop will be open to all undergraduate students at UIC and no background in data science or computer science will be required or expected. During this workshop, students will learn to explore, visualize, and build models from data using a real-world dataset of socioeconomic variables and public health indicators by Chicago community area.
- Course: for this intervention, students will enroll in a section of CS 111 (Program Design I) specifically tailored towards data science. CS 111 is the first required course in the data science and computer science curriculum and it covers an introduction to programming. This section of the course will be open to all undergraduate students at UIC with no prerequisites. Unlike other introductory data science courses that focus on data analysis and statistics [5, 6, 7, 8], the primary objective of this course will be learning programming in Python. However, this will be accomplished through data science projects.
Both the workshop and the course will emphasize project-based learning to provide students with hands-on experience in data science and reinforce “soft skills” such as teamwork and communication.
This project aims to evaluate the effectiveness of these two interventions on increasing awareness and understanding of data science and fostering a sense of identity and belonging in this field among undergraduate students at UIC.
Questions Investigated
This project aims to evaluate the effectiveness of two different interventions designed to introduce students to data science: a workshop and a course. The primary questions investigated are:
- To what extent do these interventions increase awareness and understanding of data science among undergraduate students at UIC?
- To what extent do these interventions foster a sense of belonging and identity in data science among undergraduate students at UIC?
- Are students who participate in these interventions motivated to learn more about data science (for example, by participating in other related workshops or courses)?
References
- Tan, P. N., Steinbach, M., Karpatne, A., & Kumar, V. (2018). Introduction to Data Mining (2nd Edition). Pearson.
- Bureau of Labor Statistics, U.S. Department of Labor, Occupational Outlook Handbook, Data Scientists.
- Hoffman, M., Richmond, J., Morrow, J., & Salomone, K. (2002). Investigating “Sense of Belonging” in First-Year College Students. Journal of College Student Retention: Research, Theory & Practice, 4(3), 227-256.
- Jaiswal, A., Magana A. J., Ward, M. D. (2022). Characterizing the Identity Formation and Sense of Belonging of the Students Enrolled in a Data Science Learning Community. Sci., 12, 731.
- Baumer, B. (2015). A Data Science Course for Undergraduates: Thinking With Data. The American Statistician, 69(4), 334-342.
- Yavuz, F. G., & Ward, M. D. (2018). Fostering Undergraduate Data Science. The American Statistician, 74(1), 8-16. .
- Yan, D., Davis, G. E. (2019). A First Course in Data Science. Journal of Statistics Education, 27(2), 99-109. .
- Çetinkaya-Rundel, M., & Ellison, V. (2020). A Fresh Look at Introductory Data Science. Journal of Statistics and Data Science Education, 29(sup1), S16–S26.