2023 ASEE Annual Conference & Exposition

Understanding students’ sentiments are crucial as they impact their motivation and regulation strategies. However, limited work has been devoted to understanding students’ sentiments with respect to their classroom experiences in STEM courses, particularly in courses with conceptually difficult concepts. In this regard, this work-in-progress study used Natural Language Processing (NLP) algorithm to analyze the sentiments of the engineering students’ written reflections and then understand the change in their sentiments during the semester. In this course, students were introduced to computer programming with MATLAB, which is considered a difficult undertaking for novice learners. Specifically, this study will be guided by two research questions: 1) What kind of student sentiments occur when students reflect on the interesting and confusing aspect of the lecture? and 2) How do students’ sentiments change over the semester? To inform the study, we gathered students’ written reflections using the CourseMIRROR application in the multiple sections of the introductory engineering course (N = 300 students). This application asked students to reflect on the interesting and confusing aspects of the lecture after the end of each lecture throughout the semester. For sentiment analysis, we will use the Valence Aware Dictionary for Sentiment Reasoning (VADER) to assess sentiments in students’ reflections by generating a normalized sentiment score ranging from + 1 (extreme positive) to -1 (extreme negative). Based on the sentiment analysis results, we will employ descriptive statistics to inform the first research question by counting the frequency of the sentiments found in the students’ reflections. For the second research question, we will use one-way repeated measure ANOVA by splitting the lectures (N=21) into three equal time points and seeing the change of sentiment scores across these time points. We hypothesize that the students would have positive sentiments associated with reflections on interesting aspects of the lecture and negative sentiments while reflecting on confusing aspects of the lecture. Due to the nature of the course (i.e., programming concepts become gradually complex), we expect to see declining positive sentiments over time. The findings of this study will provide insights into students’ sentiments and provide suggestions to improve students’ engagement in engineering courses.