빅데이터 분석을 활용한 한국어 교육의 현황과 인식에 관한 연구

Abstract

Recently, the numbers of learners and teachers have been rapidly increased due to the recent rise in the status of Korean language education. This study dealt with exploring the trends and issues related to Korean language education based on the web contents related with Korean language education. The target data set included heterogenous contents such as blogs, news, and academic materials that were accumulated between May 1, 2018 and April 30, 2020 in Naver and Daum sites. For the analysis of semantic networks, the important words were selected, refined, and made into matrix data by calculating their frequencies in terms of text mining. As a result, a total of 37,135 words related to Korean language education were collected, of which the top 70 words were extracted. Data containing the keyword ‘Korean language education’ showed that words such as ‘Korean language, education, certification, foreigner, recruitment, research, Korean language education, acquisition, student, instructor, recruitment’ were highly visible. And the following words such as ‘multicultural, Vietnamese, international, overseas, Chinese, Korean, cultural’ or ‘multicultural, marriage and multicultural family support centers’ were also noted regarding Korean language learners. In sequence, the collected data were analyzed by CONCOR(CONvergence of generated CORrelations) method using UCINET 6. The newly formed 4 clusters were named as ‘Qualification test, Teacher recruitment information, Korean language class, Policy.’ The four categories showed the diverse user groups of Korean language education-related web contents and their main interests. The research results provided the basis for the follow-up research reflecting the diversification of Korean language users.