2025 ASEE Annual Conference & Exposition

GIFTS: Natural Language Processing with MATLAB

In this GIFTS paper (Great Ideas for Teaching, and Talking with, Students), we present a module around natural language processing (NLP), a branch of machine learning. It is the basis of many generative artificial intelligence (GAI) tools such as Chat GPT. Given the recent emphasis on GAI within universities and beyond, it has become important for first-year engineering students to develop a sense of how this technology functions. In this guide, we present a self-paced module for first-year engineering students focused on NLP. This lesson can be coupled with MATLAB, a common platform taught in the first year. The goal is for students to be able to articulate what natural language processing is and how it works, as well as to apply it to an example with real-world data. This module walks students through setting up the necessary MATLAB add-ons and addresses the following topics: loading in text, tokenization, removing stop words, lemmatization, sentiment analysis, bag of words, classifying text, and visualizing NLP. No prior experience with NLP is required to complete this module. Assessment is based on a challenge problem in which students select public data sets and apply the techniques learned through the lesson.

Authors

Michael Shteynberg Northeastern University
Dr. Andrew L Gillen http://orcid.org/0000-0001-8021-6108 Northeastern University [biography]

Andrew L. Gillen is an Assistant Teaching Professor at Northeastern University in the First Year Engineering Program and an affiliate faculty member to Civil and Environmental Engineering. He earned his Ph.D. in Engineering Education from Virginia Tech and B.S. in Civil Engineering from Northeastern University.
Dr. Kaylla Cantilina http://orcid.org/https://0000-0002-8403-8617 Northeastern University [biography]

Kaylla Cantilina is an Assistant Teaching Professor in the First Year Engineering program at Northeastern University. While her teaching is centered around supporting student holistic growth, culturally sustaining pedagogies, and intertwining sociotechnical content in engineering education, her research is motivated by design as a means for social justice, and making engineering more accessible and supportive for minoritized students. She has a deep interdisciplinary background with degrees in industrial design, political science, industrial operations engineering, and design science, and earned her Ph.D. from the University of Michigan.

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