Summarizing Students' Free Responses for an Introductory Algebra-Based Physics Course Survey Using Cluster and Sentiment Analysis

Abstract

In Physics Higher Education (PHE), Student Evaluation of Teaching (SET) surveys are widely used to collect students' feedback on courses and instructions. In our research, we propose a more efficient way to summarize students' free responses from the Student Assessment of their Learning Gains (SALG) survey, a form of the SET survey, of an algebra-based introductory physics course at a large Canadian research university. Specifically, we use cluster and sentiment analysis methods such as K-means and Valence Aware Dictionary for sEntiment Reasoning (VADER) to summarize students' free responses. For cluster analysis, we extract popular keywords and summaries of responses in different clusters that reflect students' dominant opinions toward each aspect of the course. Notably, we obtain an average silhouette coefficient of 0.480. In addition, we analyze sentiments in students' free responses that are determined through applying VADER. Intriguingly, we see that VADER (micro F1 = 0.57, macro F1 = 0.55) can better classify responses with positive (F1 = 0.62) and neutral sentiment (F1 = 0.59). However, evident disagreements arise with negative sentiment responses (F1 = 0.42). In addition, our research suggests that some Likert-scale summaries deviate from the sentiment of free response summaries due to the limitations of Likert-scale responses. By creating various visualizations, we discover that Natural Language Processing (NLP) methods, such as cluster and sentiment analysis, effectively summarize students' free responses, with several limitations.