An Ensemble Predictive Model for Learner Attrition in Online Learning

Abstract

To increase student retention and the success of online learning initiatives, it is critical to make very accurate predictions about learner attrition. In order to put early intervention strategies into place, universities must identify students who are likely to withdraw early. A number of variables, such as academic achievement, demographic traits, and engagement metrics, affect how accurately learner attrition is predicted. Effective prediction models will be developed by analysing these characteristics using machine learning techniques.

This study's main goal is to create an ensemble-based machine learning model that predicts early learner attrition in Kenyan online learning environments by combining XGBoost, Neural Networks Decision Trees (DT), and Random Forests (RF). Learning Management Systems (LMS) secondary data collected from Kenya's five universities will be used in the study. In order to provide a strong framework for the early detection of learners who are at risk, this study describes the technique for data preprocessing, feature selection, model training, and integration.

The research's conclusions will help institutions and policymakers enhance online learning platforms, maximise student retention strategies, and tackle e-learning issues. The research intends to aid in the creation of a more effective and inclusive online learning system in Kenya by early detection of students who are at risk.