Artificial Intelligence Models for Predicting School Dropout: A systematic review
DOI:
https://doi.org/10.5753/reic.2025.5523Keywords:
Distance learning, MOOCs, School dropout, Artificial intelligence, Machine learning, Systematic reviewAbstract
Massive Open Online Courses (MOOCs) have emerged as accessible platforms for virtual education. In Brazil, the Ministry of Education (MEC) reported a 474% increase in MOOC enrollments between 2011 and 2021, indicating a growing trend among public and private institutions. However, high dropout rates remain a significant challenge. This study conducts a systematic review of the literature from 2018 to 2023 to investigate the application of artificial intelligence (AI) techniques for predicting student dropout in MOOCs. The results show that Random Forest (RF), Gaussian Naive Bayes (GNB), and Long Short-Term Memory (LSTM) algorithms are the most commonly used for prediction. The KDD CUP 2015 dataset and custom datasets were frequently employed. Precision, Recall, and F1- score were the primary evaluation metrics. The study identified several challenges, including limited and diverse data, the complexity of measuring model effectiveness, and the influence of external factors on student performance. While Random Forest and Naive Bayes algorithms are popular choices, there is a need for more diverse datasets and a deeper understanding of external factors, such as educational policies, to improve prediction accuracy.
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