2025 ASEE Annual Conference & Exposition

Mapping Essential Competencies for Entry-Level Electrical Engineers: A Hybrid NLP and Thematic Analysis Study

Presented at DSAI Technical Session 9: Student Reflections, Metacognition, and Competency Mapping

Background: This study outlines the research methods and some key findings from a broader investigation aimed at identifying the specific competencies that employers seek in entry-level electrical engineering roles, with a particular emphasis on the southeastern United States. Aligning engineering education with the evolving needs of the industry is crucial for producing highly-skilled graduates prepared for the workforce.
Purpose: This research aims to identify and categorize the essential knowledge, skills, abilities, and dispositions (KSADs) employers look for in this region, providing valuable insights for curriculum development and enhancement strategies to better prepare future graduates.
Methodology/Approach: The study employs a hybrid approach integrating natural language processing (NLP) and thematic analysis. A meticulously curated dataset of 4,585 entry-level electrical engineering job postings from five prominent U.S. job sites ("LinkedIn", "Indeed", "Glassdoor", "CareerBuilder", and "SimplyHired") was analyzed. Machine learning techniques, specifically Latent Dirichlet Allocation (LDA) topic modeling and supervised automatic topic annotation, were utilized to extract and categorize competency-related keywords from the job postings. In parallel, a thematic analysis, grounded in established KSAD frameworks, was conducted to provide a nuanced understanding of the data and capture context-specific insights. This involved manually reviewing the data to understand themes and emerging patterns related to hard (technical) skill, soft (professional) skills and dispositions.
Results: The analysis identified ten distinct competency themes prevalent in southeastern U.S. entry-level electrical engineering jobs. These themes encompassed a wide array of technical skills (e.g., circuit design, programming, power systems), professional skills (e.g., communication, teamwork, problem-solving), and dispositions (e.g., proactiveness, adaptability, and a commitment to continuous learning).
Implications: The findings have substantial implications for electrical engineering curriculum design and teaching practices, providing a data-driven foundation for ensuring alignment with current industry needs in the southeastern United States. The identified KSADs can guide educators in developing targeted courses, workshops, and learning experiences that equip students with the specific skills and attributes sought by employers in the region. Additionally, the study's outcomes can inform career counseling efforts, enabling students to make more informed decisions about specialization and professional development opportunities.
Conclusion: This study underscores the value of integrating NLP and thematic analysis to extract comprehensive competency information from job postings, advancing data-informed practices in engineering education. By providing a detailed analysis of in-demand competencies for entry-level electrical engineering positions in the southeastern U.S., this research empowers educators, policymakers, and industry stakeholders to make informed decisions regarding curriculum development, workforce training, and talent acquisition strategies.

Authors

Taiwo Raphael Feyijimi http://orcid.org/0009-0009-5752-4175 University of Georgia [biography]

Taiwo is a highly skilled AI Engineer, Researcher, and Doctoral Student at the University of Georgia who completed his MS in Electrical and Computer Engineering in the College of Engineering. He is currently leveraging AI to tackle simple and longstanding problems in engineering education.

With over a decade of industry experience as a Technology Strategist and Technical Lead, he has established himself as a forward-thinking innovator in AI and EdTech.

His expertise spans Exploratory Data Analysis (EDA), Machine Learning (ML), Natural Language Processing (NLP), and Prompt Engineering Techniques (PETs) with Large Language Models (LLMs). Taiwo is known for his ability to collaborate effectively within and across organizations to meet project goals and drive transformative results. He excels in leading technical teams, offering strategic IT consultations, and implementing solutions that enhance productivity.
VARUN KATHPALIA University of Georgia [biography]

Varun Kathpalia, born and raised in northern part of India, joined EETI as a PhD student in the Spring of 2024.
He completed his undergraduate degree in Mechanical Engineering from Chitkara Institute of Engineering and Technology (Punjab Technical University, India) and master’s degree in Mechanical Engineering, specializing in Manufacturing & Materials Science Engineering, from the Indian Institute of Technology, Kanpur, India. He has over 4 years of corporate experience with companies such as Hindustan Coca-Cola Beverages Pvt. Ltd. and Saint-Gobain India Pvt. Ltd. (Research & Development). His interest in areas such as improvement in instructional techniques, faculty perspectives and teaching methodologies, drove him towards the domain of Engineering Education. Specifically, the question of how engineering education can be made more effective and engaging fascinated and motivated him to pursue research in this domain.

He is working with his major professor on an NSF funded project dealing with communities and relationships that enable and empower faculty and students in engineering.
Ms. Sarah Jane Bork http://orcid.org/0000-0002-4344-554X University of Georgia [biography]

Dr. Sarah Jane (SJ) Bork is an Assistant Professor in Electrical and Computer Engineering with an emphasis on engineering education research. Dr. Bork’s research has focused on examining the mental health experiences of engineering graduate students. She has studied different areas (e.g., social factors, engineering culture, etc.) using a variety of research methods (e.g., regression analysis, photovoice, factor analysis, interview data, etc.). Dr. Bork earned her doctorate degree from the University of Michigan’s Engineering Education Research Program. Prior to this, she earned both a Bachelor’s and Master’s degree in Electrical Engineering from The Ohio State University.

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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