Application of Random Survival Forest to Educational Longitudinal Data Analysis: Focusing on Predicting the Timing of University Students’ Private Tutoring Participation

This study introduces random survival forest (RSF), a machine learning-based survival analysis technique, and validates its utility for educational longitudinal data. RSF is a non-parametric method that handles non-linear relationships and variable interactions without the proportional hazards assumption. Using data from 3,656 university students in the Korean Education Longitudinal Study 2005, we compared RSF with the Cox model and the Kaplan-Meier method for predicting initial participation in employment-related private tutoring. RSF achieved similar predictive accuracy to the Cox model while operating stably without variable selection procedures, and demonstrated superior performance compared to the Kaplan-Meier method throughout the follow-up period. Variable importance analysis identified key predictive factors, and partial dependence plots visualized each variable’s contribution patterns and cumulative risk over time. This study contributes to expanding methodological diversity in educational longitudinal research by demonstrating RSF’s applicability to educational data analysis.