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Journal of the Korean Society for Library and Information Science

ISSN : 1225-598X

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Vol.55 No.4
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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 / Journal of the Korean Society for Library and Information Science, (P)1225-598X; (E)2982-6292
2021, v.55 no.4, pp.267-291
https://doi.org/10.4275/KSLIS.2021.55.4.267


& (2021). 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, 55(4), 267-291, https://doi.org/10.4275/KSLIS.2021.55.4.267
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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.

keywords
코로나19, 학습만족도, 대학교육, 텍스트 마이닝, LDA 토픽 모델링, 토픽 분류, COVID-19, Learning Satisfaction, University education, Text Mining, LDA Topic Modeling, Topic Classification

Journal of the Korean Society for Library and Information Science

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