Effectiveness of Artificial Intelligence Models for Predicting School Dropout: A Meta-Analysis

Abstract

School dropout is a major concern in the educational systems of all countries. In recent years, artificial intelligence is playing an important role in predicting school dropout in the different educational stages of formal education. In this context, it is crucial to know that these predictions are accurate and understandable. This meta-analytic study aims to investigate the effectiveness of dropout prediction models conducted until May 2022. The databases used are Web of Science, Scopus, PubMed, ERIC, PsyInfo, Dialnet and Scielo. 15 studies with a sample size of 199,015 participants are analyzed. The meta-analysis uses a random-effects proportions model with 95% confidence interval. Statistical evidence indicates that artificial intelligence models performed well (91%; 95% CI = 89-93%) in predicting dropout; specifically, the Decision Tree model significantly (95.3%; 95% CI = 93-98%) predicts dropout better than other models such as Random Forest, Artificial Neural Network, Support Vector Machines, Logistic Regression and Stacking Ensemble. Consequently, more models should be applied in the dropout field with larger numbers of participants to confirm these findings and improve the quality of education.