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 the context of online educational environments, two specific effects, (1) reduced social interactions and, (2) low engagement levels generate high dropout rates. Self-regulated learning is the ability of an individual to check progress toward a goal and manage learning behavior and appears critical to successful online learning for adults. Clickstream data are information collected about a user that can be applied to observe, measure, and analyze recorded and real-time learning behaviors in a learning management system. Linking click-stream data with performance outcomes connects researchers to a comprehensive assessment of learning behaviors and s academic performance in an online learning experience. The guiding research question was: Are students who apply self-regulated learning strategies more likely to demonstrate mastery of knowledge and skills in a self-directed e-learning context? Clickstream data and individual performance outcomes were analyzed to explore whether task and cognitive conditions influence how self-regulated learning strategies are applied in the context of an online training. Bounded by the Winne and Hadwin model of self-regulated learning, common patterns of learning behaviors emerged across the four recursive phases of understanding and controlling an e-learning environment, including task analysis, goal setting, strategic action, and self-adaptation, and associated facets that influence task performance: conditions, operation, product, evaluation, and standards. We also found a positive relationship between self-regulated learning and academic performance outcomes.

Keywords:

Self-Regulated Learning (SRL), Adult Learning, Asynchronous Online Learning.