텍스트마이닝을 이용한 국내의 디지털 평생교육 연구 동향 분석

Abstract

The purpose of this study is to empirically analyze how changes in social and policy perceptions and approaches to digital lifelong education are reflected in academic discourse through text mining techniques. For this research purpose, academic journals on digital lifelong education or similar concepts published by RISS from 2010 to 2022 that featured keywords such as digital lifelong education, distance lifelong education, and online lifelong education in their titles, abstracts, and keywords were analyzed. This period was divided into three segments and text mining techniques were applied, including TF-IDF word analysis, N-gram analysis, and CONCOR analysis, to explore trends and patterns.
 The results of the analysis were as follows: First, looking at the trends in the publication of papers on digital lifelong education in Korea, initially, there was a steady increase from 2010 to 2016. This was followed by a decline in 2017, but then another increase in publications until 2022. Second, in the frequency and importance analysis, the top 10 words included those related to digital distance learning, followed by words associated with lifelong education support development and programs, and words related to class performance and effectiveness ranked next. Third, in the CONCOR analysis based on the semantic network, the first period emphasized the construction and support of a cyber university education system. In the second period, there was a shift in emphasis towards the actual operation process, motivation for participation in education, formation of a learning community, and analysis of effects. In the third period, there was a notable emphasis on various policies and projects related to digital lifelong education. Additionally, there was a strong focus on discussing the effectiveness of these initiatives, particularly in relation to specific outcomes such as working hours and employment indicators. Accordingly, this study proposed future digital lifelong education policy directions regarding each analysis result, and these are significant in that they present basic data that sets the direction for follow-up research and suggests specific tasks.