LAUSR

The main purpose of this study was to demonstrate the uses of regularization, a machine learning technique, in exploring important predictors for online student success. We analyzed student and learning behavioral variables from undergraduate fully-online flipped classrooms. In particular, students’ instructional video watching behaviors at an instructional unit level were extracted from LMS (learning management system) log data, and Enet (elastic net) and Mnet were employed among regularization. As results, regularization not only showed comparable prediction performance to random forest, a nonlinear method well-known for its prediction capabilities, but also produced interpretable prediction models as a linear method. Enet and Mnet selected 17 and 19 important predictors out of 159, respectively, and could identify potential low-performers as early as the first instructional week of the course. Important variables rarely recognized in previous studies included complete viewings of the first video before class and repeated complete viewings of challenging contents after in-class meetings. Unlike previous studies, aggregate measures of video lecture views were not important predictors. Variables in line with previous research were student attitudes towards the course, gender, grade level, clicks on learning materials postings, number of quiz-taking, and mobile lecture watching frequencies. Many students turned out not to complete watching lecture videos before class. Further research on regularization and exploration of these variables with other potentially important predictors can provide more insight into students’ online learning from a comprehensive perspective.