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

Dublin Core

Title

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

Subject

Learner Attrition, online learning, dropout, e-learning
retention

Description

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

Creator

Stanley Munga Ngigi

Publisher

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

Date

june 2025

Contributor

Fajar bagus W

Format

PDF

Language

English

Type

Text

Files

Citation

Stanley Munga Ngigi, “A Systematic Review of Predictive Factors for
Learner Attrition in Online Learning: Insights for
Machine Learning Models,” Repository Horizon University Indonesia, accessed January 1, 2026, https://repository.horizon.ac.id/items/show/9746.