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http://dx.doi.org/10.9708/jksci.2022.27.10.211

Analysis of Descriptive Lectures Evaluation using Text Mining: Comparative analysis pre and post COVID-19  

Lee, Sang-Chul (Dept. of G2 Big Data Management, Gangseo University)
Publication Information
Journal of the Korea Society of Computer and Information / v.27, no.10, 2022 , pp. 211-222 More about this Journal
Abstract
The purpose of this study is to indicate the direction of the future university classes in the post-COVID era, comparing and analyzing lecture evaluation of pre and post COVID-19. To this end, 4 yeard data were used from 2018 to 2019 for pre COVID-19 and form 2020 to 2021 data for post COVID-19. The results were as follows. In the case of liberal arts, "assignments" was the word with the highest frequency and degree centrality(DC) regardless of pre and post-COVID-19 In the major, "understanding" appeared as the most important word. The result of the ego network analysis indicated that "video lecture" and "non-face-to-face classes" were difficult and "interaction" between the professor and the students was important. As a results, it is important to reduce the weight of assignments and increase interaction with students in liberal arts classes. In the case of majors, it is necessary to operate face-to-face classes rather than non-face-to-face classes, and to organize the contents of videos without difficulty.
Keywords
Lecture Evaluation; Data Mining; Text Mining; Degree Centrality; Ego Network Analysis;
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Times Cited By KSCI : 1  (Citation Analysis)
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