에듀테크 기반 미용교육의 필요성 모색 2011-2022년 미용연구를 중심으로

Abstract

This study attempted to investigate research trends in edu-tech and high-tech information communication technology-related words and derive implications on the necessity of beauty education studies. This study collected literature data published from 2011 to 2022. Among them, a total of 31 papers were chosen for final analysis to set the direction of education for those in need of digital education in a changing era according to the Fourth Industrial Revolution. Starting with two theses first issued in 2011, high-tech information & communication technology-related beauty papers have been examined so far. In terms of a research topic, the beauty industry was the highest with 13 articles (41.94%), followed by makeup/hairstyling/beauty education (5 papers, 16.13%), aesthetics (2 papers, 6.45%) and nail care (1 paper, 3.22%). When compared to the beauty industry, there needed to be more studies on beauty education. In addition, there has been a demand for changes in educational environments for talent development. In terms of keyword trends, ‘augmented reality (AR)’ was the highest with 51.61%, followed by ‘virtual reality (29.03%)’, ‘3D and machine learning (6.45%)’ and ‘smart learning & metaverse (3.23%)’. In terms of a research method, ‘quantitative research’ was the highest with 13 papers (41.94%), followed by ‘literature review (8 papers, 25.8%)’, ‘other studies (5 papers, 16.13%)’, ‘mixed research (4 papers, 12.9%)’ and ‘qualitative research (1 paper, 3.23%)’. In other words, there is a necessity to perform research with multiple views. The Fourth Industrial Revolution has requested creative convergence talent across the whole process from manufacture to distribution and customer services. Under these circumstances, developing talent that meets such needs in education is also required. This study has suggested the necessity of follow-up studies in domestic beauty education with diverse research topics and through diversification of study subjects and expansion of data collection methods for in-depth approach.