Abstract

Student dropout is a major concern in higher education, as it leads to direct economic losses and substantial social costs. Public and private institutions spend considerable resources to prevent student dropout. The efficiency and effectiveness of these investments, however, may be improved by adopting a profit-driven perspective. In this paper, we propose a novel approach for implementing student dropout prediction using data-driven methods. Extending upon profit metrics as used in business analytics, we design a novel performance measure for evaluating predictive models that is tailored to the student dropout problem and that quantifies the net savings of a retention campaign. This metric supports the identification and selection of students to optimally allocate the limited resources for preventing student dropout and to maximize the resulting savings. Experiments were performed using data from three bachelor?s programs of a higher education institution containing information on dropouts and participation in a retention program, i.e., tutorials. The proposed metric allows for a better choice of prediction model and classification threshold than conventional approaches and, as a result, yields tangible savings for the institution. Finally, the presented approach and experimental results highlight pathways to design tailored student retention programs.