000			01952nam a22002057a 4500
005			20240117155509.0
008			240116b ||||| |||| 00| 0 eng d
022			_a0021-9584
100			_aHoar, Benjamin B.
245			_aEnhancing the Value of Large-Enrollment Course Evaluation Data Using Sentiment Analysis _b(Journal Article)
260			_aWashington DC _b: American Chemical Society _c, 2023
300			_a4085–4091p.
440			_aJournal of Chemical Society _v, Volume 100: Number 10, October 2023
505			_a***______{For Hard Copy, Please visit Library.}________***
520			_aAbstract: 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.
650			_aProfessional Development| Administration Issues| Student-Centered Learning| Machine Learning
700			_aRamachandran, Roshini | Levis-Fitzgerald, Marc | Sparck, Erin M. | Wu, Ke | Liu, Chong
856			_uhttps://doi.org/10.1021/acs.jchemed.3c00258
942			_cPER
999			_c45358 _d45357