Transitioning self-regulated learning profiles in hypermedia-learning environments

Abstract

Self-regulated learning (SRL) is a process that highly fluctuates as students actively deploy their metacognitive and cognitive processes during learning. In this paper, we apply an extension of latent profiling, latent transition analysis (LTA), which investigates the longitudinal development of students' SRL latent class memberships over time. We will briefly review the theoretical foundations of SRL and discuss the value of using LTA to investigate this multidimensional concept. This study is based on college students (n = 75) learning about the human circulatory system while using MetaTutor, an intelligent tutoring system that adaptively supports SRL and targets specific metacognitive SRL processes including judgment of learning (JOL) and content evaluation (CE). Preliminary results identify transitional probabilities of SRL profiles from four distinct events associated with the use of SRL.