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Machine learning enables the development of new, supplemental, and empoweringtools that can either expand existing technologies or invent new ones. Ineducation, space exists for a tool that supports generic student course reviewformats to organize and recapitulate students' views on the pedagogicalpractices to which they are exposed. Often, student opinions are gathered witha general comment section that solicits their feelings towards their courseswithout polling specifics about course contents. Herein, we show a novelapproach to summarizing and organizing students' opinions via analyzing theirsentiment towards a course as a function of the language/vocabulary used toconvey their opinions about a class and its contents. This analysis is derivedfrom their responses to a general comment section encountered at the end ofpost-course review surveys. This analysis, accomplished with Python, LaTeX, andGoogle's Natural Language API, allows for the conversion of unstructured textdata into both general and topic-specific sub-reports that convey students'views in a unique, novel way.

Summative Student Course Review Tool Based on Machine Learning Sentiment Analysis to Enhance Life Science Feedback Efficacy