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- W4386774286 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." @default.
- W4386774286 created "2023-09-16" @default.
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- W4386774286 date "2023-09-15" @default.
- W4386774286 modified "2023-10-16" @default.
- W4386774286 title "Enhancing the Value of Large-Enrollment Course Evaluation Data Using Sentiment Analysis" @default.
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- W4386774286 doi "https://doi.org/10.1021/acs.jchemed.3c00258" @default.
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