Full Paper: Future-Ready Students: Survey Analysis Utilizing Natural Language Processing
- Conference
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15th Annual First-Year Engineering Experience Conference (FYEE)
- Location
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Boston, Massachusetts
- Publication Date
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July 28, 2024
- Start Date
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July 28, 2024
- End Date
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July 30, 2024
- Page Count
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7
- DOI
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10.18260/1-2--48593
- Permanent URL
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https://peer.asee.org/48593
- Download Count
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41
Paper Authors
Toluwani Collins Olukanni Norwich University
Majd Khalaf Norwich University
Majd Khalaf is a senior undergraduate student at Norwich University, majoring in Electrical and Computer Engineering. He is deeply passionate about DevOps engineering and machine learning. Majd has contributed to various projects and research in natural language processing (NLP) and computer vision. Currently, he is a Site Reliability Engineering intern at Walmart ASR and a Senior AI Researcher at Norwich University's Artificial Intelligence Center.
Michael Cross Norwich University
Michael Cross is an Assistant Professor of Electrical and Computer Engineering teaching classes in the areas of circuits, electronics, energy systems, and engineering design. Cross received degrees from the Rochester Institute of Technology and the University of Vermont.
David M. Feinauer P.E. Virginia Military Institute
Dr. Feinauer is a Professor of Electrical and Computer Engineering at Virginia Military Institute. His scholarly work spans a number of areas related to engineering education, including the first-year engineering experience, incorporating innovation and entrepreneurship practice in the engineering classroom, and P-12 engineering outreach. Additionally, he has research experience in the areas of automation and control theory, system identification, machine learning, and energy resilience. He holds a PhD and BS in Electrical Engineering from the University of Kentucky.
Ali Al Bataineh Norwich University
Abstract
First-year Electrical and Computer Engineering students from two institutions engaged in a collaborative project to develop a smart home device using sensors and actuators learned in their introductory courses. They reflected on the project, and their feedback was analyzed using unsupervised and Natural Language Processing techniques like K-means clustering and Latent Dirichlet Allocation. Key methods included data preprocessing and cleaning. AI tools like TF-IDF vectorization and ChatGPT helped identify key themes such as “PROJECT,” “PARTNER,” “WORK,” and “LEARNED.” This study highlights NLP's role in enhancing educational strategies and understanding student experiences.
Olukanni, T. C., & Khalaf, M., & Cross, M., & Feinauer, D. M., & Al Bataineh, A. (2024, July), Full Paper: Future-Ready Students: Survey Analysis Utilizing Natural Language Processing Paper presented at 15th Annual First-Year Engineering Experience Conference (FYEE), Boston, Massachusetts. 10.18260/1-2--48593
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