Minjeong Rho

The purpose of this study was to explore variables related to school belonging from a holistic perspective, including a large number of variables in one model, different to the traditional analytical method. Using 2015 data from the Program for International Student Assessment (PISA), we sought to identify variables related to school belonging by searching for hundreds of predictors in one model using the group Mnet machine learning technique. The study repeated 100 rounds of model building after random data splitting. After exploring 504 variables (384 student and 99 parent), 32 variables were finally selected after selection counts. Variables predicting a sense of school belonging were categorized as individual/parent variables (e.g. motivation to achieve, tendency to cooperative learning, parental support) and school-related variables (e.g. school satisfaction, peer/teacher relationship, learning/physical activities). The significance and implications of the study as well as future research topics were discussed.

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.

With the advent of the big data era, machine learning methods have evolved and proliferated. This study focused on penalized regression, a procedure that builds interpretive prediction models among machine learning methods. In particular, penalized regression coupled with large-scale data can explore hundreds or thousands of variables in one statistical model without convergence problems and identify yet uninvestigated important predictors. As one of the first Monte Carlo simulation studies to investigate predictive modeling with missing categorical predictors in the context of social science research, this study endeavored to emulate real social science large-scale data. Likert-scaled variables were simulated as well as multiple-category and count variables. Due to the inclusion of the categorical predictors in modeling, penalized regression methods that consider the grouping effect were employed such as group Mnet. We also examined the applicability of the simulation conditions with a real large-scale dataset that the simulation study referenced. Particularly, the study presented selection counts of variables after multiple iterations of modeling in order to consider the bias resulting from data-splitting in model validation. Selection counts turned out to be a necessary tool when variable selection is of research interest. Efforts to utilize large-scale data to the fullest appear to offer a valid approach to mitigate the effect of nonignorable missingness. Overall, penalized regression which assumes linearity is a viable method to analyze social science large-scale survey data.