Early Detection of University Students in Potential Difficulty

Using data mining methods, this paper presents a new means of identifying freshmen's profiles likely to face major difficulties to complete their first academic year. Academic failure is a relevant issue at a time when post-secondary education is ever more critical to economic success. We aim at early detection of potential failure using student data available at registration, i.e. school records and environmental factors, with a view to timely and efficient remediation and/or study reorientation. For the sake of accuracy, we adapt three data mining methods, namely random forest, logistic regression and artificial neural network algorithms. Real data pertaining to undergraduates at the University of Liège (Belgium), illustrates our methodology.