뉴스 빅데이터를 활용한 대학평생교육 토픽 분석

Abstract

This study analyzes the policy and social discourse on university lifelong education by applying the topic modeling analysis using news big data. In particular, the National Lifelong Education Enhancing Plans(NLEEP) was taken as the main context for the analysis. A total of 22,493 news articles were collected through BigKinds, the DB operated by the Korea Press Foundation. As a result of the analysis, the words that appeared the most were ‘Student’, ‘Support’, ‘Business’, ‘Operation’, and ‘Region’. And the topic that showed the most weight was 'Lifelong education through industry-university cooperation', followed by 'Participation in university lifelong education support project' and 'Distance learning and degree acquisition' in that order. Second, most of the derived topics appeared repeatedly throughout the entire cycle. Third, the topics classified at the individual university level show the highest frequency, followed by the local community linkage and the national development level. Based on the results of this analysis, the limitations of the study and follow-up studies were suggested along with suggestions such as the efforts to promote communication among the participants in lifelong education and the development and sharing of its success models.