01952nam a22002057a 4500 20240117155509.0 240116b ||||| |||| 00| 0 eng d 0021-9584 Hoar, Benjamin B. Enhancing the Value of Large-Enrollment Course Evaluation Data Using Sentiment Analysis (Journal Article) Washington DC : American Chemical Society , 2023 4085–4091p. Journal of Chemical Society , Volume 100: Number 10, October 2023 ***______{For Hard Copy, Please visit Library.}________*** Abstract: 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. Professional Development| Administration Issues| Student-Centered Learning| Machine Learning Ramachandran, Roshini | Levis-Fitzgerald, Marc | Sparck, Erin M. | Wu, Ke | Liu, Chong https://doi.org/10.1021/acs.jchemed.3c00258 PER 0 0 0 0 RIEBPL RIEBPL 2024-01-17 0 2024-01-17 00:00:00 2024-01-17 PER 45358 45357