Using Learning Analytics to Determine Predictors of Successful Completion for Online Students

By M. C. Clowes, Melanie Shaw and Scott Burrus.

Published by The International Journal of Adult, Community and Professional Learning

Format Price
Article: Print $US10.00
Published online: June 14, 2017 $US5.00

In this study, the researchers sought to determine predictors of student success in learning tasks and efficiency in student task attempts necessary for at-risk students to achieve success in an online learning environment. The study was informed by archival (de-identified) data gathered in the United Kingdom from students in an online education setting, which bears many similarities to American online higher education: namely, non-traditional student demographics, open enrollment, and a completely online education structure. A series of logistic regressions yielded two findings. First, the only statistically significant predictor of student success is the number of course attempts. Therefore, since number of attempts is a more statistically significant predictor of student completion than student demographics, understanding what influences how many course attempts students need in order to be successful can be more useful for targeted educational policy making. Second, for those who only required one attempt to be successful, the number of previously studied credits was the only significant predictor. For those who required more than one course attempt, age was also a significant predictor. Thus, the number of credits students had prior to taking an online course is a useful predictor of whether a student is successful the first time they attempt an online learning module. For those who seem to need numerous attempts before being successful, age seems to be more of a statistically significant predictor than any other demographic factor.

Keywords: Learning Analytics, Student Success, Learning Outcomes, Hierarchical Regression, Online Education, At-risk Students, Andragogy

The International Journal of Adult, Community and Professional Learning, Volume 24, Issue 2, pp.15-21. Article: Print (Spiral Bound). Published online: June 14, 2017 (Article: Electronic (PDF File; 274.375KB)).

Dr. M. C. Clowes

Research Fellow, Center of Learning Analytics Research, The University of Phoenix, Phoenix, Arizona, USA

Dr. Melanie Shaw

Professor, School of Education, Northcentral University, Scottsdale, Arizona, USA

Dr. Scott Burrus

Research Chair, Center for Learning Analytics Research, University of Phoenix, Tempe, Arizona, USA