Factors associated to student success in online learning: a data mining analysis

Abstract

This research study aims to determine variables associated to student success in online learning (e-learning). The knowledge discovery in databases (KDD) consists on applying algorithms to find hidden data patterns. The method used here is the CRISP-DM (cross industry standard process for data mining) and was applied to examine online degree programs at the Distance Education Center of the Northern Catholic University (DEC-NCU) in Chile. The sample was collected from 19 years of teaching and consists of 18,610 students. The results show that the variables that best explain student success are age, gender, degree study, educational level, and locality. It is concluded that these results contribute to improve the understanding of distance education critical factors.