Exploration of Variables Predicting Sense of School Belonging Using the Machine Learning Method-Group Mnet.

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.

Large-Scale Survey Data Analysis with Penalized Regression: A Monte Carlo Simulation on Missing Categorical Predictors.

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.

Exploration of Predictors for Korean Teacher Job Satisfaction via a Machine Learning Technique, Group Mnet.

Despite the high academic achievements of Korean students in international comparison studies, their teachers' job satisfaction remains below the Organization for Economic Co-operation and Development (OECD) average. As job satisfaction is one of the major factors affecting student achievement as well as student and teacher retention, the identification of the most important satisfaction predictors is crucial. The current study analyzed data from the OECD 2013 Teaching and Learning International Survey (TALIS) via machine learning.

Microbial risk assessment of staphylococcal food poisoning in Korean kimbab.

The objective of this study was to conduct a preliminary microbial risk assessment for Staphylococcus aureus in kimbab, and then propose appropriate guidelines for its preparation and storage. Kimbab is a ready-to-eat product, popular in Korea, made of various foods surrounded by rice and seaweed. Because it is usually prepared by hand and stored at room temperature, it can be contaminated with S.