  Having observed low success rates among first-year university students in
both Belgium and France, we develop prediction models in this paper in order to
identify, at the earliest possible stage, those students who are at risk of
failing at the end of the academic year. We applied different data mining
techniques to predict the students' academic success. We find that it is very
difficult to predict success by only considering the variables related to
behavior during classes, and that it is necessary to add variables related to
personal history, involvement in and behavior during their studies, and
perceptions of academic life, to obtain good-quality results.
