Abstract
Objectives This study aimed to analyze research trends of non-face-to-face education in higher education during COVID-19 pandemic using text mining techniques.
 Methods A total of 195 research articles published in Korea from 2020 to August 2021 were collected, then Korean abstracts were analyzed through term frequency and topic modeling adopting text mining techniques.
 Results The term-frequency analysis revealed that keywords such as class, learners, analysis, learning, instructors, etc. were prominent ones. The topic modeling based on latent dirichlet allocation (LDA) generated ten topics: instructional methods, learner experiences, interaction, instructional design, presence, self-efficacy and motivation, feedback system, digital learning contents, self-directed learning, and stress.
 Conclusions The results of this study have a significant implication on understanding the direction of non-face-to-face education at universities in the future.
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