The Effect of Self-Regulated Learning in Online Professional Training

Abstract

With the rapid expansion of mobile, blended, and seamless learning, researchers claim two factors, lack of self-discipline and poor time management, adversely impact learning performance. In online educational environments, reduced social interactions and low engagement levels generate high dropout rates. Self-regulated learning (SRL), the individual ability to check progress toward a goal and manage learning behavior, appears critical to adult online learning success. Clickstream data can observe, record, and evaluate patterns of users' real-time learning behavior in an online learning environment. Linking clickstream data with performance outcomes allows researchers to assess online learning behaviors and academic performance. The guiding research question was: Are students who apply SLR strategies more likely to demonstrate mastery of knowledge and skills in a self-directed e-learning context? Clickstream data and performance measures were analyzed to explore whether task and cognitive conditions influence how SLR strategies are applied in online training.