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Comparative analysis of Lecture Evaluation using Decision Tree: Ways to Improve University Classes after COVID-19

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2023, 28(4), pp.197-208
  • DOI : 10.9708/jksci.2023.28.04.197
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : April 4, 2023
  • Accepted : April 24, 2023
  • Published : April 28, 2023

Bok-Ju Jung 1 Sang-Chul Lee 1

1강서대학교

Accredited

ABSTRACT

In this study, we attempted to examine the changing ways of thinking about lecture evaluation before and after COVID-19. To this end, decision tree analysis(Decision Tree) was used among data mining techniques based on lecture evaluation data for liberal arts and major classes conducted before and after COVID-19 for A university. According to the results of the study, liberal arts changed from 'method' to 'content', and 'knowledge improvement' was an important factor both before and after majors. In particular, 'Assignment' was found to be an important factor after the COVID-19 in common in the evaluation of lectures in the liberal arts department, which means that in the future, professors will be provided with appropriate teaching methods during class, interaction with students, and feedback on assignments or test results, indicates the need for competence. Based on the results of this study, a plan to improve communication with students and activation of blended learning was suggested.

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