Understanding Online Self-Regulation: A Data-Driven Approach to LMS Interaction Indicators

Abstract

Self-regulation skills are becoming increasingly important in the continuously growing field of online learning. The study aims to suggest a way to provide information about students' self-regulated learning skills using students-specific online interaction data on LMSs. The online self-regulation scale was applied to identify indicators of LMS events. Interaction data on self-regulated learning were derived from theoretical relationships between online interaction types. The interaction data gathered from Moodle LMS (forum, chat, assignment, etc.) and the perception data from the self-regulation scale were used in correspondence analysis and inferences were made as online SRL indicators. Senior students from the instructional technologies department participated in the study. The results indicated that while the consistency level of goal setting, environment structuring and self-evaluation are high, the other self-regulation skills (task strategies, time management, help seeking) were relatively moderate and low. Along with the consistencies, the results indicate that the interaction data from Moodle LMS can be used to provide information about students' online self-regulation skills. This study by matching LMS-events and self-regulation skills is hoped to shed light on the measurement, prediction, or intervention for self-regulation skills in online learning.