2025 ASEE Annual Conference & Exposition

Course-Job Fit: Understanding the Contextual Relationship Between Computing Courses and Employment Opportunities

Presented at Software Engineering Division (SWED) Technical Session 4

In today’s world, where higher education is increasingly vital, aligning curricula with industry demands is essential. This paper explores the contextual relationship between computing courses and technical jobs using various transformer models to encode course
syllabi and job descriptions into high-quality fixed-sized vector spaces (embeddings), enabling efficient and nuanced comparisons that reveal deeper contextual relationships.

Our research makes multiple unique contributions that address gaps in existing work. First, we gather a large, recent data set of 197,296 jobs in five technical fields. Secondly, we perform in-depth analysis between courses and job postings using advanced transformer models, offering clear and deeper insights into how well academic content aligns with the industry. Third, we investigate salary trends to identify courses and skills linked to high-paying jobs. Fourth, we examine core and elective courses separately to provide insights for curriculum development and assist students in choosing elective courses considering industry demands.

Our findings show that top-ranking courses emphasize a combination of technical skills and professional skills like communication and team work. Moreover, skills like cloud technologies and databases are prevalent across multiple fields, highlighting their importance as essential skills for success in various technical domains. We found that the core courses mandated by the curriculum for all students to complete generally align better with job market demands compared to elective courses. Interestingly, undergraduate courses exhibit stronger alignment with job postings overall, while graduate courses improve their alignment specifically with higher-paying jobs. This highlights the importance of considering career goals when deciding whether to pursue graduate education. Overall, this paper introduces a replicable methodology for analyzing curricula and demonstrates its application through a case study of one institution’s computing programs.

Authors

Christopher Lukas Kverne http://orcid.org/0009-0007-9614-1068 Florida International University [biography]

Christopher Lukas Kverne is an undergraduate researcher pursuing a B.S in Computer Science, plus a minor in Mathematics, with a cumulative GPA of 3.86. His research interests lie Deep learning, Optimization and Quantum Machine learning where his goal is to optimize training processes and reduce the computing power needed to develop powerful models. Christopher has led multiple projects in ML, Systems and Quantum research and will continue his studies in graduate school.
Federico Monteverdi Florida International University [biography]

Federico Monteverdi is an IT Applications Programmer Associate at Progressive Insurance, where he focuses on backend technologies and large-scale system integration. He earned his B.A. in Computer Science from Florida International University, graduating Magna Cum Laude with a 3.89 GPA, and completed consecutive internships in software engineering and testing before starting full-time in 2025. During his undergraduate studies, Federico received a full-ride research scholarship at the EPSI Lab, where he developed full-stack prototypes to support Ph.D. research in wireless power transfer. He later joined the DaMRL Lab, contributing to the core applications behind the paper Course-Job Fit: Understanding the Contextual Relationship Between Computing Courses and Employment Opportunities. He also served as a teaching assistant for Discrete Structures, with academic interests spanning systems design, applied machine learning, and translating research into scalable industry solutions.
Agoritsa Polyzou Florida International University [biography]

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.
Dr. Christine Lisetti Florida International University [biography]

Christine Lisetti is an Associate Professor at Florida International University (FIU) in the Knight Foundation School of Computing and Information Sciences , and the Director of the Virtual Intelligent Social AGEnts (VISAGE) Laboratory. Her long-term research goal is to create engaging virtual social agents (VISAGEs) that can help humans in a variety of contexts by interacting with them in innovative ways, through natural expressive multimodal interaction (e.g. in digital health interventions, cybertherapy, health counseling, educational serious games, cyberlearning, simulation-based social skill training systems). She conducts basic research at the intersection of human-computer interaction (HCI), affective computing (I was on the founding Editorial Board of the IEEE Transactions on Affective Computing), human-centered artificial intelligence (AI), and virtual reality (VR) in order to discover design principles for VISAGESs. She also conducts applied research by applying these principles to different application domains (e.g. healthcare, medicine, education, social skill training).
Janki Bhimani http://orcid.org/0000-0002-4421-9923 Florida International University [biography]

Dr. Janki Bhimani is the Director of the Data Management Research Lab and a Rising Scholar Professor in Computer Science at Florida International University (FIU), as well as the founding CEO of LegalPro+. She holds a Ph.D. and M.S. in Computer Engineering from Northeastern University. Her research spans system design, storage systems, computer architecture, EDA, ML, cloud and quantum computing, HPC, and computer education. She is a distinguished scholar and recipient of several prestigious honors, including the NSF CAREER Award, FIU Top Scholar Award, and the In the Company of Women Award.

Note

The full paper will be available to logged in and registered conference attendees once the conference starts on June 22, 2025, and to all visitors after the conference ends on June 25, 2025

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