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.
