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

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2022, 27(10), pp.211-222
  • DOI : 10.9708/jksci.2022.27.10.211
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : September 23, 2022
  • Accepted : October 14, 2022
  • Published : October 31, 2022

Sang-Chul Lee 1

1강서대학교

Accredited

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.

Citation status

* References for papers published after 2023 are currently being built.

This paper was written with support from the National Research Foundation of Korea.