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Enhancing the Value of Large-Enrollment Course Evaluation Data Using Sentiment Analysis

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Title: Enhancing the Value of Large-Enrollment Course Evaluation Data Using Sentiment Analysis
Language: English
Authors: Benjamin B. Hoar, Roshini Ramachandran (ORCID 0000-0002-2559-4656), Marc Levis-Fitzgerald, Erin M. Sparck, Ke Wu, Chong Liu (ORCID 0000-0001-5546-3852)
Source: Journal of Chemical Education. 2023 100(10):4085-4091.
Availability: Division of Chemical Education, Inc. and ACS Publications Division of the American Chemical Society. 1155 Sixteenth Street NW, Washington, DC 20036. Tel: 800-227-5558; Tel: 202-872-4600; e-mail: eic@jce.acs.org; Web site: http://pubs.acs.org/jchemeduc
Peer Reviewed: Y
Page Count: 7
Publication Date: 2023
Sponsoring Agency: National Science Foundation (NSF), Division of Chemistry (CHE)
Contract Number: 2247426
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Chemistry, Science Instruction, Large Group Instruction, Teaching Methods, Course Evaluation, Language Usage, Computer Software, Feedback (Response), Programming Languages, Barriers, College Faculty, STEM Education
DOI: 10.1021/acs.jchemed.3c00258
ISSN: 0021-9584
1938-1328
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.
Abstractor: As Provided
Entry Date: 2024
Accession Number: EJ1445133
Database: ERIC
FullText Text:
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  Data: Enhancing the Value of Large-Enrollment Course Evaluation Data Using Sentiment Analysis
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  Data: <searchLink fieldCode="AR" term="%22Benjamin+B%2E+Hoar%22">Benjamin B. Hoar</searchLink><br /><searchLink fieldCode="AR" term="%22Roshini+Ramachandran%22">Roshini Ramachandran</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-2559-4656">0000-0002-2559-4656</externalLink>)<br /><searchLink fieldCode="AR" term="%22Marc+Levis-Fitzgerald%22">Marc Levis-Fitzgerald</searchLink><br /><searchLink fieldCode="AR" term="%22Erin+M%2E+Sparck%22">Erin M. Sparck</searchLink><br /><searchLink fieldCode="AR" term="%22Ke+Wu%22">Ke Wu</searchLink><br /><searchLink fieldCode="AR" term="%22Chong+Liu%22">Chong Liu</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-5546-3852">0000-0001-5546-3852</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+Chemical+Education%22"><i>Journal of Chemical Education</i></searchLink>. 2023 100(10):4085-4091.
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  Data: Division of Chemical Education, Inc. and ACS Publications Division of the American Chemical Society. 1155 Sixteenth Street NW, Washington, DC 20036. Tel: 800-227-5558; Tel: 202-872-4600; e-mail: eic@jce.acs.org; Web site: http://pubs.acs.org/jchemeduc
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  Data: Y
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  Data: 7
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  Data: 10.1021/acs.jchemed.3c00258
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  Data: 0021-9584<br />1938-1328
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  Data: 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.
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  Data: 2024
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  Data: EJ1445133
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