Identifying significant indicators using LMS data to predict course achievement in online learning
Internet and Higher Education Volume 29, Number 1, ISSN 1096-7516 Publisher: Elsevier Ltd
This study sought to identify significant behavioral indicators of learning using learning management system (LMS) data regarding online course achievement. Because self-regulated learning is critical to success in online learning, measures reflecting self-regulated learning were included to examine the relationship between LMS data measures and course achievement. Data were collected from 530 college students who took an online course. The results demonstrated that students' regular study, late submissions of assignments, number of sessions (the frequency of course logins), and proof of reading the course information packets significantly predicted their course achievement. These findings verify the importance of self-regulated learning and reveal the advantages of using measures related to meaningful learning behaviors rather than simple frequency measures. Furthermore, the measures collected in the middle of the course significantly predicted course achievement, and the findings support the potential for early prediction using learning performance data. Several implications of these findings are discussed.
You, J.W. (2016). Identifying significant indicators using LMS data to predict course achievement in online learning. Internet and Higher Education, 29(1), 23-30. Elsevier Ltd.
Cited ByView References & Citations Map
Mike Carbonaro & Amanda Montgomery, University of Alberta, Canada; Amin Mousavi, University of Saskatchewan, Canada; Bill Dunn & Denyse Hayward, University of Alberta, Canada
EdMedia + Innovate Learning 2017 (Jun 20, 2017) pp. 62–66
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