텍스트마이닝을 활용한 고교학점제의 사회적 인식변화 분석

Abstract

Objectives This study attempted to analyze the trend of changes in social perception ahead of the full implementation of the high school credit system in 2025. Methods To this end, text data for the past three years from 2019 to 2021 were collected and big data analysis using text-mining was conducted. For data analysis, TF and TF-IDF analysis, N-gram analysis, CONCOR analysis, and emotional word frequency analysis were performed using the ‘TEXTOM’ program. Results As a result of TF and TF-IDF analysis, words that continued to rank in the top 10 for three years were “high school, high school credit system, curriculum, education office, introduction, and student,” while words with high TF-IDF values were “research, implementation, education office, high school, curriculum.” Looking at the results of the N-gram analysis, negative words such as ‘promotion-communication, communication-insufficient, and insufficient-point out’ were extracted in 2021, and negative views on the implementation of the high school credit system increased. In addition, as a result of CONCOR analysis, it was classified into four clusters by each year, and it was found that there is a high interest in the 2022 revised curriculum ahead of the implementation of the high school credit system. As a result of analyzing the frequency of emotional words, it was found that positive responses were high until 2020, but negative responses increased in 2021. Conclusions This can be seen as an effect of growing anxiety across society as the time for the full implementation of the high school credit system is getting closer and online classes due to COVID-19 are also progressing. It is considered that such a change in social perception must be reflected in the establishment of policies for the establishment of a stable high school credit system.