An Ensemble Predictive Model for Learner Attrition
in Online Learning

An Ensemble Predictive Model for Learner Attrition
in Online Learning

learner attrition, ensemble learning, gradient boosting,
neural networks, online education, predictive modeling.

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.

Stanley Munga Ngigi

https://ijcit.com/index.php/ijcit/article/view/521

School of Pure and Applied Sciences
Kirinyaga University, Kutus, Kenya

june 2025

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