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

• URL: http://arxiv.org/abs/2301.06173v1
• Date: Sun, 15 Jan 2023 19:56:56 GMT
• Title: Summative Student Course Review Tool Based on Machine Learning Sentiment Analysis to Enhance Life Science Feedback Efficacy
• Authors: Ben Hoar, Roshini Ramachandran, Marc Levis, Erin Sparck, Ke Wu, Chong Liu
• Abstract summary: We show a novel approach to summarizing and organizing students' opinions via analyzing their sentiment towards a course as a function of the language/vocabulary used. This analysis is derived from their responses to a general comment section encountered at the end of post-course review surveys.
• Score: 4.518390136757588
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Machine learning enables the development of new, supplemental, and empowering tools that can either expand existing technologies or invent new ones. In education, space exists for a tool that supports generic student course review formats to organize and recapitulate students' views on the pedagogical practices to which they are exposed. Often, student opinions are gathered with a general comment section that solicits their feelings towards their courses without polling specifics about course contents. Herein, we show a novel approach to summarizing and organizing students' opinions via analyzing their sentiment towards a course as a function of the language/vocabulary used to convey their opinions about a class and its contents. This analysis is derived from their responses to a general comment section encountered at the end of post-course review surveys. This analysis, accomplished with Python, LaTeX, and Google's Natural Language API, allows for the conversion of unstructured text data into both general and topic-specific sub-reports that convey students' views in a unique, novel way.

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