2025 ASEE Annual Conference & Exposition

Understanding Interdisciplinary Energy Discourses: A Mixed-Methods Study Using LDA and Thematic Analysis

Presented at Liberal Education/Engineering & Society Division (LEES) Technical Session 6: LEES Works in Progress

In this paper, we explore how cross-disciplinary students characterize their engagement with energy topics and what frequent terms they use to describe energy-related considerations. We leveraged mixed method design-applying Latent Dirichlet Allocation (LDA) and thematic analysis on ten interview transcripts of engineering (n=2) and social science students (n=8) to analyze the students’ discussions on energy systems in the context of their backgrounds, career goals, challenges, learning, and knowledge. In this analysis, we identified that engineering students construct energy around technical terms and career development whereas social science students frame energy on policy and environmental issues. This dual approach also reveals deep intersecting themes such as technical learning, energy policy, energy career etc., that manual analysis might overlook. The focus of previous studies on energy literacy has been on a conceptual framework which includes theoretical knowledge and behavior. However, limited research has examined how the language differences of cross-disciplinary students influenced energy discourses. Understanding these language differences will improve communication and collaboration between interdisciplinary students for effective energy literacy solutions.

Authors

Sakshi Solanki University of Connecticut [biography]

Sakshi Solanki is a PhD student in the Chemical and Biomolecular Engineering Department at the University of Connecticut, specializing in Engineering Education. Her research investigates how sociotechnical frameworks and interdisciplinary energy-education strategies can foster student engagement in engineering. She uses a mixed-methods approach that integrates quantitative and qualitative analysis, applying natural language processing (NLP), topic modeling (e.g., Latent Dirichlet Allocation), and machine learning techniques to study patterns in educational discourse and social media data.
Achal Duhoon University of Connecticut [biography]

Achal Duhoon is a PhD student in the Department of Mechanical Engineering at the University of Connecticut. His research focuses on developing smart, biodegradable biomaterials at the nano- and micro-scale for applications in drug delivery, tissue engineering, and bioelectronics. He is also dedicated to Engineering Education, with a passion for inspiring and supporting the next generation of engineers.
Dr. Desen Sevi Özkan http://orcid.org/0000-0002-1996-7719 University of Connecticut [biography]

Desen is an assistant professor at the University of Connecticut in the Chemical and Biomolecular Engineering Department. She holds a Ph.D. in Engineering Education from Virginia Tech. Her research focuses on sociotechnical engineering education and how people make sense of complex sociotechnical energy infrastructure and systems.

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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