Identifying university students' online self-regulated learning profiles: predictors, outcomes, and differentiated instructional strategies

Self-regulated learning (SRL) is critical in online learning, and profiling learners' SRL patterns is needed to provide personalized support. However, little research has examined how each learner performs the cyclical phases of SRL based on trace data. To fill the gap, this study attempts to derive SRL profiles encompassing all cyclical phases of forethought, performance, and self-reflection based on learning analytics and establish specific SRL support by exploring profile membership predictors and distal outcomes. Through profiling 106 students in a university online course using Latent profile analysis (LPA), four distinctive SRL profile types emerged: Super Self-Regulated Learners, All-around Self-Regulated Learners, Unbalanced Self-Regulated Learners, and Minimally Self-Regulated Learners. Multinomial logistic regression analysis revealed that task value and teaching presence significantly predicted profile membership. Additionally, multivariate analysis of variance (MANOVA) showed that cognitive, affective, behavioral, and agentic engagement and learning achievement differed significantly among the four profiles. More instructional strategies for supporting SRL are described in the paper.