Data-Science Perceptions: A Textual Analysis of Reddit Posts from Non-Computing Engineers

Nicolas Leger; Maimuna Begum Kali; Stephanie Jill Lunn

Download Paper | Permalink

Conference: 2024 ASEE Annual Conference & Exposition
Location: Portland, Oregon
Publication Date: June 23, 2024
Start Date: June 23, 2024
End Date: July 12, 2024
Conference Session: DSA Technical Session 5
Tagged Topic: Data Science & Analytics Constituent Committee (DSA)
Permanent URL: https://peer.asee.org/47108

Request a correction

Paper Authors

biography

Nicolas Leger Florida International University

visit author page

Nicolas Léger is currently an engineering and computing education Ph.D. student in the School of Universal Computing, Construction, and Engineering Education (SUCCEED) at Florida International University. He earned a B.S. in Chemical and Biomolecular Engineering from the University of Maryland at College Park in May 2021 and began his Ph.D. studies the following fall semester. His research interests center on numerical and computational methods in STEM education and in Engineering Entrepreneurship.

visit author page

biography

Maimuna Begum Kali Florida International University orcid.org/0000-0003-1770-7363

visit author page

Maimuna Begum Kali is a Ph.D. candidate in the Engineering and Computing Education program at the School of Universal Computing, Construction, and Engineering Education (SUCCEED) at Florida International University (FIU). She earned her B.Sc. in Computer Science and Engineering from Bangladesh University of Engineering and Technology (BUET). Kali's research interests center on exploring the experiences of marginalized engineering students, with a particular focus on their hidden identity, mental health, and wellbeing. Her work aims to enhance inclusivity and diversity in engineering education, contributing to the larger body of research in the field.

visit author page

biography

Stephanie Jill Lunn Florida International University orcid.org/0000-0003-3840-1822

visit author page

Stephanie Lunn is an Assistant Professor in the School of Universal Computing, Construction, and Engineering Education (SUCCEED) and the STEM Transformation Institute at Florida International University (FIU). She also has a secondary appointment in the Knight Foundation School of Computing and Information Sciences (KFSCIS). Previously, Dr. Lunn earned her doctorate in computer science from the KFSCIS at FIU, with a focus on computing education. She also holds B.S. and M.S. degrees in computer science from FIU and B.S. and M.S. degrees in neuroscience from the University of Miami. In addition, she served as a postdoctoral fellow in the Wallace H. Coulter Department of Biomedical Engineering at the Georgia Institute of Technology, with a focus on engineering education. Her research interests span the fields of computing and engineering education, human-computer interaction, data science, and machine learning.

visit author page

Download Paper | Permalink

Abstract

U.S. national reports constantly emphasize the need for qualified engineers to enter the workforce to solve present and future challenges for society. Such advancements often require an understanding and application of data science, a field that combines areas like mathematics, statistics, programming, analytics, and artificial intelligence. Despite its rapid growth and increasing integration across topics and industries, data science is not often incorporated directly into engineering curricula. Understanding when and how to utilize data science methodologies can provide non-computing engineers with a competitive edge professionally, offering valuable insights, improving decision-making, and driving innovation in their respective domains. Given the benefits of learning and employing data science, we explored the various approaches, uses, and views of non-computing engineers and how they may influence their attitudes and practices. We defined non-computing engineers as individuals focused on an engineering field who are not pursuing computer science or computer engineering-specific formal education or degrees. To assess varying perspectives, we conducted a study utilizing Reddit posts. Reddit is a platform where many engineering students and practitioners may talk openly about different topics. We collected data using web scraping and analyzed it using a couple of Natural Language Processing (NLP) techniques, including Latent Dirichlet Allocation (LDA). Using the top keywords, we then took a manual approach using whole posts for context to perform thematic analysis to derive the topics. Our findings suggest that non-computing engineers are generally positive towards data science and its potential applications. They see it as especially important for 1) Career Prospects and Opportunities, 2) Ongoing Professional Development and Upskilling, and 3) Practical Applications. As such, it can provide opportunities for career preparedness, fostering new competencies, and a need to gain hands-on experience using data science to create value and solve problems. The results of this work can have important implications for educators, administrators, and professionals looking to incorporate data science into engineering praxis.

Citation
Format

Leger, N., & Kali, M. B., & Lunn, S. J. (2024, June), Data-Science Perceptions: A Textual Analysis of Reddit Posts from Non-Computing Engineers Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. https://peer.asee.org/47108

TY - CPAPER
AB - U.S. national reports constantly emphasize the need for qualified engineers to enter the
workforce to solve present and future challenges for society. Such advancements often require an understanding and application of data science, a field that combines areas like mathematics, statistics, programming, analytics, and artificial intelligence. Despite its rapid growth and increasing integration across topics and industries, data science is not often incorporated directly into engineering curricula. Understanding when and how to utilize data science methodologies can provide non-computing engineers with a competitive edge professionally, offering valuable insights, improving decision-making, and driving innovation in their respective domains. Given the benefits of learning and employing data science, we explored the various approaches, uses, and views of non-computing engineers and how they may influence their attitudes and practices.
We defined non-computing engineers as individuals focused on an engineering field who are not pursuing computer science or computer engineering-specific formal education or degrees. To assess varying perspectives, we conducted a study utilizing Reddit posts. Reddit is a platform where many engineering students and practitioners may talk openly about different topics. We collected data using web scraping and analyzed it using a couple of Natural Language Processing (NLP) techniques, including Latent Dirichlet Allocation (LDA). Using the top keywords, we then took a manual approach using whole posts for context to perform thematic analysis to derive the topics. Our findings suggest that non-computing engineers are generally positive towards data science and its potential applications. They see it as especially important for 1) Career Prospects and Opportunities, 2) Ongoing Professional Development and Upskilling, and 3) Practical Applications. As such, it can provide opportunities for career preparedness, fostering new competencies, and a need to gain hands-on experience using data science to create value and solve problems. The results of this work can have important implications for educators, administrators, and professionals looking to incorporate data science into engineering praxis.
AU - Nicolas Leger
AU - Maimuna Begum Kali
AU - Stephanie Jill Lunn
CY - Portland, Oregon
DA - 2024/06/23
PB - ASEE Conferences
TI - Data-Science Perceptions: A Textual Analysis of Reddit Posts from Non-Computing Engineers
UR - https://peer.asee.org/47108
ER -