Examining Learners' Self-Regulation Patterns Within A Learning Management System
Date
2021
Authors
Hess, Richard Michael
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Abstract
The purpose of this study was to explore and analyze the utilization of learning analytics data produced by a learning management system as an indicator of learners’ self-regulation. In the Spring of 2021, 258 learners at a four-year, mid-Atlantic university provided access to their learning management system data. Of those 258 learners, 86 completed the Motivated Strategies for Learners Questionnaire. Correlational analyses were utilized to examine learners’ self-report self-regulation and their self-regulating behaviors within the learning management system. Relationships between learners’ self-report self-regulation and learner’s planning and regulating behaviors with the learning management system are consistent with Self-Regulation Theory. Additionally, a growth mixture modeling analysis was conducted to examine learners’ self-regulated trajectories over the semester. Five trajectories were identified for the planning activities, four trajectories were identified for monitoring activities, and three trajectories were identified for regulating activities. Lastly, multiple ANOVAs were conducted to compare academic achievement between the trajectories for planning, monitoring, and regulating behaviors. Learners who had higher levels of planning and monitoring activity also had higher levels of academic achievement.
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Keywords
Educational psychology, Growth Mixture Model Analysis, Higher education, Learning Analytics, Learning Management System, Self-Regulation, Student Development