A Conceptual Model for Measuring and Supporting Self-Regulated Learning using Educational Data Mining on Learning Management Systems

Abstract

Self-Regulated Learning (SRL) is a field in educational psychology that as attracted tremendous research studies from various communities including ICTs. Recent studies and meta-analysis have greatly contributed to the domain knowledge that the use SRL strategies boost academic performance for learners. Despite these developments in SRL, our understanding on the tools and instruments to measure SRL in online learning environments is limited as the use of traditional tools developed for face-to-face classroom settings are still used to measure SRL on e-learning systems. Additionally, most of these studies focus on SRL on Massive Open Online Course (MOOC) environments and only analyse SRL strategies with limited attention to the scaffolds or interventions employed to stimulate growth of SRL skills in learners. The modern Learning Management Systems (LMS) such as Moodle allow storage of datasets on student activities. Consequently, it is possible to use Educational Data Mining (EDM) to extract learner patterns which can be used to support SRL and explore how SRL strategies change over time and inform the instructor. In view of this, the research examines the current SRL measurement and promotion tools for e-learning environments and proposes a conceptual model grounded on EDM for implementation as a solution to measuring and promoting SRL strategies for learners within LMS.