A Systematic Review of Predictive Factors for Learner Attrition in Online Learning: Insights for Machine Learning Models

dc.contributor.author	Ngigi, S. M.,
dc.contributor.author	Mwikya, J.,
dc.contributor.author	Mageto, V.
dc.date.accessioned	2025-10-31T09:58:43Z
dc.date.available	2025-10-31T09:58:43Z
dc.date.issued	2025
dc.identifier.uri	http://repository.kyu.ac.ke/123456789/1198
dc.description.abstract	- Over the past ten years, online education has expanded rapidly due to its accessibility, scalability, and flexibility. Despite its potential, high attrition rates in online education threaten both student progress and the legitimacy of the institution. A comprehensive analysis of empirical research on the factors influencing learner attrition in online learning settings is presented in this study. To identify the individual, course-level, institutional, and technical causes of attrition, it incorporates and categories the body of existing work. The results point to the complex aetiology of attrition and identify important domains for focused intervention and predictive modelling.	en_US
dc.publisher	International Journal of Computer and Information Technology	en_US
dc.subject	Learner Attrition, online learning, dropout, e-learning retention	en_US
dc.title	A Systematic Review of Predictive Factors for Learner Attrition in Online Learning: Insights for Machine Learning Models	en_US
dc.type	Article	en_US