A Systematic Review of Predictive Factors for Learner Attrition in Online Learning: Insights for Machine Learning Models
DOI:
https://doi.org/10.24203/s2anxm35Keywords:
Learner Attrition, online learning, dropout, e-learning retentionAbstract
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.
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Copyright (c) 2025 Stanley Munga Ngigi, James Mwikya, Victor Mageto
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The articles published in International Journal of Computer and Information Technology (IJCIT) is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
