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Analyzing Students’ Non-face-to-face Course Evaluation by Topic Modeling and Developing Deep Learning-based Classification Model

  • Journal of the Korean Society for Library and Information Science
  • 2021, 55(4), pp.267-291
  • DOI : 10.4275/KSLIS.2021.55.4.267
  • Publisher : 한국문헌정보학회
  • Research Area : Interdisciplinary Studies > Library and Information Science
  • Received : October 18, 2021
  • Accepted : November 15, 2021
  • Published : November 30, 2021

Hanjiyeong 1 Heo Go Eun 1

1연세대학교

Excellent Accredited

ABSTRACT

Due to the global pandemic caused by COVID-19 in 2020, there have been major changes in the education sites. Universities have fully introduced remote learning, which was considered as an auxiliary education, and non-face-to-face classes have become commonplace, and professors and students are making great efforts to adapt to the new educational environment. In order to improve the quality of non-face-to-face lectures amid these changes, it is necessary to study the factors affecting lecture satisfaction. Therefore, This paper presents a new methodology using big data to identify the factors affecting university lecture satisfaction changed before and after COVID-19. We use Topic Modeling method to analyze lecture reviews before and after COVID-19, and identify factors affecting lecture satisfaction. Through this, we suggest the direction for university education to move forward. In addition, we can identify the factors of satisfaction and dissatisfaction of lectures from multiangle by establishing a topic classification model with an F1-score of 0.84 based on KoBERT, a deep learning language model, and further contribute to continuous qualitative improvement of lecture satisfaction.

Citation status

* References for papers published after 2022 are currently being built.