Learning Analytics and Student Success in Accelerated Graduate Online Courses: A Mixed-Methods Approach

• In Event: Fostering Persistence and Self-Regulatory Processes in Online Courses

Abstract

As more accelerated online courses are offered, where students complete a full semester’s work in much less time, it is essential to support students’ academic success. Learning analytics data about online learning behaviors may be used to support academic success. This mixed methods study explores the relationship between LMS data and final grade and learning experiences of the students in accelerated distance graduate courses.
Ninety-one students in seven online, seven-week graduate courses participated. The multiple regression model accounted for 54.7% of the variance of the final grades. On-time submission and regularity of log-in behaviors significantly predicted final grades. In interviews, students expressed the lack of reflection time in the accelerated schedule despite efforts at self-regulation and time management.

Authors

• Joshua Key, Renewable Energy Group

• Dabae Lee, Kennesaw State University