AI in Preventing Dropout in Distance Higher Education: A Systematic Literature Review

This document provides a systematic review of methodologies offering ways to reduce dropout rates in university virtual learning environments. These environments generate significant amounts of data on courses and students, necessitating the use of computational analysis tools. The high dropout rate in university online courses is considered the main problem by most higher education institutions. Our study aims to identify solutions using machine learning (ML) techniques to reduce these high dropout rates. We conducted a systematic review to identify, filter, and categorize primary studies. The initial search in academic databases resulted in 117 articles, with 23 included in the final analysis. The review reports on the historical evolution of publications, the ML techniques used, the characteristics of the used data, and identifies the proposed solutions for reducing university dropout in distance education. Our study provides an overview of the state of the art in proposed solutions to reduce university dropout rates using ML techniques and can guide future studies and tool development.