To successfully reduce student attrition, it is imperative to understand which students are at risk of dropping out. We develop an early detection system (EDS) to predict student success in tertiary education as a basis for a targeted intervention. The EDS uses regression analysis, neural networks, decision trees and the AdaBoost algorithm to identify student characteristics which distinguish potential dropouts from graduates. The developed method can be implemented in every German university, as it uses student performance and demographic data collected and maintained by legal mandate. Therefore the EDS self-adjusts to the university where it is employed. The EDS is tested and applied on a state university and a private university of applied sciences. Both institutes of higher education differ considerably in their organization, tuition fees and studentteacher ratios. Our results indicate a prediction accuracy at the end of the first semester of 79% for the state university and 85% for the private university of applied sciences. After the fourth semester, the accuracy improves to 90% for the state university and 95% for the private university of applied sciences.


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