Research Trends of Self-Regulating Learning in Online Learning

Abstract

This study attempted to present a method to utilize self-regulated learning ability in an online class situation. Since online learning is expected to continuously increase in the future, this study is intended to provide a way to increase online learning ability. To this end, this study examined research trends related to self-regulated learning within online learning. Specifically, articles published using two keywords 'online learning' and 'self-regulated learning' were collected and analyzed for the final 96 papers. Self-regulated learning was classified into cognition, motivation, and behavior, focusing on online learning, and semantic network analysis was conducted in each. As a result, in self-regulated learning in online learning, 'learning' was in the most important position in the entire semantic network. Also, it was high in the order of 'strategy', 'online', 'class', 'application', 'academic achievement', 'satisfaction', 'student', 'participation', and 'group'. In addition, when analyzing the semantic network for each area of cognition, motivation, and behavior, there was a difference in major keywords for each area. Based on these results, discussions and implications were presented.