A Systematic Review of Random Forest-Based Decision-Support Systems in Educational Management
DOI:
https://doi.org/10.56177/12icmie2025.148Abstract
An increasing number of educational institutions are using predictive analytics to help with strategic decision-making and programs that help students succeed. This paper looks into the most recent improvements in using supervised learning models, specifically Random Forest classifiers, to keep an eye on behavioral trends in digital learning settings. We focus on studies that use structured data exported from Learning Management Systems (LMS), with a concentration on CSV-based processing procedures because they are easy to use and understand. The research brings together several uses of predictive logic in schools, looking at how well the algorithms work, how easy they are to use, and how they were designed. This review provides a theoretical basis for building smart, user-friendly educational tools that can adapt to students' activity profiles by establishing consistent modeling patterns and ways to make them understandable.
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Copyright (c) 2025 Teodora – Mihaela SPIRIDON – MOCIOACĂ, Roxana PUIU, Romina-Cătălina PASCARU, Laura-Florentina BOANȚĂ
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