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 |
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| Language: | English |
| Authors: | Benjamin B. Hoar, Roshini Ramachandran (ORCID |
| 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 |
| 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. |
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| ISSN: | 0021-9584 1938-1328 |
| DOI: | 10.1021/acs.jchemed.3c00258 |
