Changes in Social Awareness of Early Childhood Coding Education revealed in Big data

Objectives This study uses semantic network analysis, a big data analysis method, to examine the flow of social awareness and demands for early childhood coding education and identify key keywords to improve understanding of the flow of early childhood coding education and to improve early childhood coding education in the future. The purpose is to find implications for the direction of institutional and policy research. Methods For this purpose, the entire period, 2007-2023, was divided into a total of 4 quarters such as from September 25, 2007 to September 25, 2011, September 26, 2011 to September 25, 2015, September 26, 2015 to September 25, 2019, September 26, 2019 to September 25, 2023. Afterwards, data was collected using ‘Early childhood coding education’ as a search term in TEXTOM. Next, we conducted emotional vocabulary analysis of TEXTOM based on the collected data. In addition, after two purification processes, a total of 200 keywords, 50 for each quarter, were extracted based on TF-IDF. Finally, CONCOR analysis was performed with the final keywords. Results As a result of the study, social interest in early childhood coding education increased over time, content was expanded into various channels, and positive perceptions were consistently higher than negative. In addition, 50 keywords were extracted for each quarter, and as a result of CONCOR analysis, 4 word groups were formed for each quarter. Through 50 keywords and 4 word groups for each quarter, we were able to identify the social awareness and flow of early childhood coding education by quarter. Conclusions This study identified the flow of social awareness and demands for early childhood coding education, and based on this, it can be used as basic data to identify people's major interest in early childhood coding educa-tion and prepare policy measures for the development of early childhood coding education.