Identifying and Understanding Student Dropouts Using Metaheuristic Optimized Classifiers and Explainable Artificial Intelligence Techniques

Identifying and Understanding Student Dropouts Using Metaheuristic Optimized Classifiers and Explainable Artificial Intelligence Techniques

This study addresses the pressing issue of student dropout in higher education institutions and explores the potential of artificial intelligence (AI) to mitigate this challenge. Student dropout is a complex phenomenon influenced by diverse factors, including internal and external, student character...

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Bibliographic Details
Main Authors: Radic Goran, Luka Jovanovic, Nebojsa Bacanin, Milos S. Stankovic, Vladimir Simic, Milos Antonijevic, Miodrag Zivkovic
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Higher education dropout
explainable artificial intelligence
metaheuristics
XGBoost
AdaBoost
optimization
Online Access:https://ieeexplore.ieee.org/document/10640075/

Internet

https://ieeexplore.ieee.org/document/10640075/

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