Predicting adult students’ online learning persistence: a case study in South Korea using random forest analysis

Abstract

ABSTRACT This empirical study uses a random forest algorithm to examine the factors that influence learners’ persistence in online learning at a prominent Korean institution. The data were collected from students who began their studies in Spring 2021, and encompassed a range of variables including individual attributes, academic engagement, academic achievement, course status, and satisfaction with the institution. The study identified several key predictors of student retention, including academic achievement and variables related to academic engagement, such as students’ learning time, course completion rate, and number of logins to the online learning system. Students’ number of submitted mid-term assignments and attendance at face-to-face classes also emerged as significant factors related to persistence. The predictive model utilised in this study can provide valuable insight, indicating when a learner is at risk of dropping out and thus enabling timely interventions that promote academic persistence and student success.