Predictive Model for Early Detection of Students with Difficulties in Online Learning

Abstract

Online learning has become increasingly prevalent in all education levels during recent years. While in highly developed regions transition from traditional to online learning happens without significant difficulties, in underdeveloped and developing countries introducing students to online learning is typically followed by complications and frustration. Many researchers conducted studies to solve the issue of conforming to online learning and provide equal opportunities to all students regardless of their demographical characteristics and environmental factors. Introducing artificial intelligence tools to this problem can provide valuable insight into patterns and predictors in online education. This study proposes a machine learning model for predicting the low-level student adaptability to online learning. This model can indicate students who might have difficulties adapting to online learning with 94% accuracy based on their demographical and environmental characteristics. The model is developed using locally weighted learning with a C4.5 decision tree classifier. This paper contributes to understanding the problems underlying online learning adaptability and offers an accurate tool for detecting students prone to online learning issues, which can help persons of authority provide dependable and rapid aid.