Personal Learning Environments: Modeling Students’ Self-Regulation Enhancement Through a Learning Management System Platform

Abstract

A personal learning environment (PLE) is known as a crucial support for educators who lead learners through the process of collection, creation, and organization of personalized learning tools. In this manner, the learner can interpret a variety of new tools in their own interest, which makes the learning process easier. The PLE approach represents a considerable movement away from traditional learning, where learners are considered consumers of information through isolated channels, particularly learning management systems (LMSs), to a model where learners draw significant connections from numerous resources that they choose. Thus, educational settings have implemented LMSs fully into their respective learning contexts. In this sense, LMS is identified as a learning platform that helps learners and educators submit assignments, share ideas, and communicate through web-based systems with numerous benefits. Under these circumstances, self-regulation is addressed as a significant component that explains how learners build and manage PLEs and come up with more choices; they take ownership of their own learning and enhance self-regulated learning (SRL) practices. On this occasion, there is a belief that teachers can utilize LMSs to shift from passive to active learning and to improve self-reflection (SR). Therefore, considering all the above issues, the current study examines integrating a third-generation LMS to enhance learners’ SR. This study considered PLEs by utilizing Zimmerman’s SRL model to investigate the integration of the third-generation LMS. SR is applied in this study in the form of a pretest and posttest following the involvement of the PLE course, which was designed and applied during the COVID-19 pandemic. Finally, the experimental findings of the current study formulated a model of SR factors in PLEs through the LMS platform with partial least squares structural equation modeling (SEM) before and after the intervention.