기계 번역과 자연어처리를 활용한 튜터링 활동 분석

Abstract

This is a follow-up study by Yoon, Kim, Choi, and Kim (2021). In the current study, the same text as the previous study was machine-translated into English, and then the topics derived through a statistical model, which were compared with the categories derived from the Korean text of the previous study. By doing this, rather than simply evaluating the quality of machine translation, the present study examined 1) the natural language processing model applied to text, 2) the role of human researchers in the process of interpreting the results after fitting the model, and 3) the harmony between human and machine translation, The texts to be analyzed were collected from the liberal arts education tutoring program of D university. A theoretical description of the Latent Dirichlet Allocation model, which is often used for topic modeling, and an explanation of the use of the R library ‘topicmodels’ were added. The analyses revealed a somewhat different pattern of topics from the previous study, and the authors’ interpretation was presented. Finally, while acknowledging the explosive growth of natural language processing and machine translation as a branch of it that cannot be reversed, the room for human translators to complement machine translation was suggested. This study presents a new approach to liberal arts education research by combining data analysis techniques using natural language processing and machine translation discussions.