Exploring self-regulation through learning navigation pathways in online learning during the pandemic

Abstract

Online learning has shown significant growth as a powerful alternative method to deliver learning through the pandemic situation. In the meantime, many studies have been attempting to investigate how to provide education within online platforms effectively; however, a few have examined how students regulate their learning during online courses.

Through the lens of self-regulated learning theory and Zimmerman’s cyclical model (2000), the present study examines how successful students and less successful students regulate their learning in hypermedia contexts. Moreover, the research aims to explore self-regulatory behaviors via the learning pathways between successful students and less successful students in a learning management system.

The process-oriented method was applied to investigate the student’s learning paths from the log data collected. The coding was done based on a new coding scheme created through the lens of self-regulated learning theories, in which half of the events were assigned with self-regulatory activities due to the lack of theoretical explanation. The frequency analysis and process mining analysis of coded learning events were generated to examine the differences in self-regulated learning between successful and less successful students.

The results indicate how successful and less successful students regulate differently in their learning navigation. For educators, the study provides insights to better design online learning courses and suggests self-regulatory strategies to support students in hypermedia contexts.