An Artificial Neural Network Approach to Analyze Students' Dropout in Higher Education

This study used Artificial Neural Networks to analyze student drop out in higher education. The data set contained 50,000 records of Mexican students and the investigated predictors were university background, prior academic achievement, and SES indicators. The predictive systems were accurate and allowed the identification of the most important predictors.