DDL: Prediction of Dropout Behaviors in Online Learning using a Cross-classified Random Effect Model (CCREM)

• In Event: Posters Thur2
  In Poster Session: DDL - Posters Thur2

Short Description

The focus of this research is dropout students in online learning. To investigate the learning management system (LMS) usage patterns of dropout students, we examined when dropping out happened and compared the log data between dropouts and completers. In addition, to explore predictive factors as many as possible from a multi-level data structure, we employed a cross-classified random effect model. This approach make possible it to analyze the complicated data previous studies have ignored.

Authors

• Wonjoon Hong, University of Nevada, Las Vegas

• Mimi Recker, Utah State University