Trend Analysis of Individualized Learning -Using the Keyword Network-

Abstract

Individualized learning is the education that is provided at a speed and method appropriate to each learner based on the learner's knowledge, interests, and so on. This study aimed to analyze the trends in individualized learning for general education in the past ten years by dividing them into those from 2012-2016 (Period 1) and 2017-2021 (Period 2), and to obtain implications necessary to practice individualized learning in school education. After screening, a total of 277 studies were analyzed by conducting text mining and keyword network analysis, using Textom, Ucinet, and NetDraw. By conducting the process, term frequency, TF-IDF, the keyword network, degree centrality, closeness centrality, and betweenness centrality, which are the analysis indices, came up. Results of the study revealed that studies about individualized learning have been continuously conducted since 2012, and their number increased over time. Most keywords from both periods were the same based on the termfrequency and TF-IDF, but "SMART-Education" and "Under-achievement" appeared frequently only during Period 1, whereas "Artificial intelligence," "COVID-19," "Future," and "Future education" appeared only during Period 2. Through keyword network analysis, it was found that the density of the network is higher during the second period, but the group degree centralization appeared to be higher during the first period. Furthermore, the degree centrality of "e-learning" during Period 1 and "Online," "Artificial intelligence" appeared to be high. Based on the research results, there are implications for analyzing trends in individualized learning and practicing individualized learning in schools' education in the future. Therefore, it is necessary to create an environment for individualized learning so that schooling does not become overly dependent on developing technology and neglects the curriculum and other elements of education like grades.