Enhancing the Value of Large-Enrollment Course Evaluation Data Using Sentiment Analysis (Record no. 45358)
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| fixed length control field | 01952nam a22002057a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240117155509.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 240116b ||||| |||| 00| 0 eng d |
| 022 ## - INTERNATIONAL STANDARD SERIAL NUMBER | |
| ISSN | 0021-9584 |
| 100 ## - MAIN ENTRY--AUTHOR NAME | |
| Personal name | Hoar, Benjamin B. |
| 245 ## - TITLE STATEMENT | |
| Title | Enhancing the Value of Large-Enrollment Course Evaluation Data Using Sentiment Analysis |
| Remainder of title | (Journal Article) |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication | Washington DC |
| Name of publisher | : American Chemical Society |
| Year of publication | , 2023 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | 4085–4091p. |
| 440 ## - SERIES STATEMENT/ADDED ENTRY--TITLE | |
| Title | Journal of Chemical Society |
| Volume number/sequential designation | , Volume 100: Number 10, October 2023 |
| 505 ## - FORMATTED CONTENTS NOTE | |
| Formatted contents note | ***______{For Hard Copy, Please visit Library.}________***<br/><br/> |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | 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. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Professional Development| Administration Issues| Student-Centered Learning| Machine Learning |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Ramachandran, Roshini | Levis-Fitzgerald, Marc | Sparck, Erin M. | Wu, Ke | Liu, Chong |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1021/acs.jchemed.3c00258 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Periodicals |
| Lost status | Damaged status | Home library | Current library | Date acquired | Koha item type |
|---|---|---|---|---|---|
| RIE BPL Library | RIE BPL Library | 17.01.2024 | Periodicals |