Enhancing the Value of Large-Enrollment Course Evaluation Data Using Sentiment Analysis

In education, space exists for a tool that valorizes generic student course evaluation formats by organizing and recapitulating students' views on the pedagogical practices to which they are exposed. Often, student opinions about a course are gathered using a general comment section that does not solicit feedback concerning specific course components. Herein, we show a novel approach to summarizing and organizing students' opinions as a function of the language used in their course evaluations, specifically focusing on developing software that outputs actionable, specific feedback about course components in large-enrollment STEM contexts. Our approach augments existing course review formats, which rely heavily on unstructured text data, with a tool built from Python, LaTeX, and Google's Natural Language API. The result is quantitative, summative sentiment analysis reports that have general and component-specific sections, aiming to address some of the challenges faced by educators when teaching large physical science courses.