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

• Benjamin B. Hoar ^[1] ; Roshini Ramachandran ^[1] ; Marc Levis-Fitzgerald ^[1] ; Erin M. Sparck ^[1] ; Ke Wu ^[1] ; Chong Liu ^[1]
  1. [1] University of California Los Angeles

     University of California Los Angeles

     Estados Unidos

     
• Localización: Journal of chemical education, ISSN 0021-9584, Vol. 100, Nº 10, 2023, págs. 4085-4091
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • 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.