Enhancing Students' Self-regulated Learning in Online Learning Environments Using Learning Analytics

Online education has become popular in the last two decades and has played an important role in efforts to continue offering education to all levels of students during the coronavirus pandemic. Recent studies indicate that online learners must have the capacity to regulate their own learning in order to succeed in online courses. Measuring a learner’s ability to regulate their own learning is difficult. Traditional self-reported self-regulated learning questionnaires have many limitations when used alone. The research study reported in this dissertation collected online learners’ digital trace data recorded by a learning management system and compared that data with the results of self-reported data to evaluate which one accurately measures students’ self-regulatory ability. In-depth interviews were conducted to better understand the causes accounting for the differences. A manuscript format consisting of three manuscripts has been chosen for the dissertation. The first manuscript (Chapter 2) is a historical review of learning analytics in the learning, design, & technology field, which provides a methodology foundation for the study. The second manuscript (Chapter 3) is a literature review of self-regulated learning theory and measurements, which provides a theoretical foundation for the design of the major study. The third manuscript (Chapter 4) is a mixed-methods study to investigate the differences and similarities between the digital trace data and self-reported SRL data as well as identify key learning behaviors that related to students’ self-regulatory ability.