A Study on the Analysis of Related Keywords on the Perception of Untact Coding Education in the Post-COVID Era Using Big Data Analysis

초록

The novel infectious disease COVID-19, which started in December 2019, has plunged the world into a pandemic era. Accordingly, countries around the world are concerned about the spread of COVID-19, and have promoted large-scale gatherings and education through immigration control and social distancing. In particular, from March 15, 2020, elementary, secondary, high school, and university have conducted an unprecedented online opening of school in Korea's school education operation, and distance education classes have been conducted starting with the third year of middle and high schools across the country. In this process, students, faculty, and parents were faced with unexpected non-face-to-face distance education, and experienced confusion, tension, and uncertainty about the future society. Due to the spread of COVID-19, the online school curriculum has exposed the limitations and problems of the existing face-to-face school education system. In particular, coding education aimed at understanding and using software can interest students through face-to-face computer practice education. In addition, face-to-face education is important for non-IT majors to realize the importance of SW education and improve their convergence thinking skills. In this study, big data (SNS, Naver blog, youtube, google search keyword frequency, etc.) collected online to analyze the recognition and evaluation of untact (real-time online or pre-recorded video lecture) coding education was used as a text mining technique. Analysis was performed. ‘Coding education’, the keyword of this study, was selected as an analysis keyword, and data was collected through portals/SNSs of Google, Naver, Daum, and YouTube. In addition, after refining/morpheme analysis based on the collected list, word frequency, TF-IDF, N-gram, and topic modeling were analyzed through text mining, and matrix analysis, matrix chart, and sentiment analysis were performed. The results of this study will be used as basic data for college students’ coding education. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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