TY  - BOOK
AU  - Hoar, Benjamin B. 
AU  - Ramachandran, Roshini | Levis-Fitzgerald, Marc | Sparck, Erin M. | Wu, Ke | Liu, Chong 
TI  - Enhancing the Value of Large-Enrollment Course Evaluation Data Using Sentiment Analysis  : (Journal Article)
SN  - 0021-9584
PY  - 2023///
CY  - Washington DC 
PB  - : American Chemical Society 
KW  - Professional Development| Administration Issues| Student-Centered Learning| Machine Learning
N1  - ***______{For Hard Copy, Please visit Library.}________***


N2  - 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
UR  - https://doi.org/10.1021/acs.jchemed.3c00258
ER  -