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.

学生辍学是高等教育的主要问题，因为这会导致直接的经济损失和巨大的社会成本。公共和私人机构花费大量资源来防止学生辍学。但是，通过采用利润驱动的观点，可以提高这些投资的效率和有效性。在本文中，我们提出了一种使用数据驱动方法来实现学生辍学预测的新颖方法。扩展了业务分析中使用的利润指标后，我们设计了一种新颖的绩效评估指标，用于评估预测模型，该模型针对学生辍学问题量身定制，并量化了留任活动的净节省额。该指标支持对学生的识别和选择，以最佳地分配有限的资源，以防止学生辍学并最大程度地节省成本。使用来自高等教育机构的三个学士学位课程的数据进行了实验，这些数据包含有关辍学和参加保留计划（即教程）的信息。与常规方法相比，提出的度量标准可以更好地选择预测模型和分类阈值，从而为该机构带来切实的节省。最后，提出的方法和实验结果突出了设计量身定制的学生保留计划的途径。高等教育机构的课程，其中包含有关辍学和参加保留计划的信息，即教程。与常规方法相比，提出的度量标准可以更好地选择预测模型和分类阈值，从而为该机构带来切实的节省。最后，提出的方法和实验结果突出了设计量身定制的学生保留计划的途径。高等教育机构的课程，其中包含有关辍学和参加保留计划的信息，即教程。与常规方法相比，提出的度量标准可以更好地选择预测模型和分类阈值，从而为该机构带来切实的节省。最后，提出的方法和实验结果突出了设计量身定制的学生保留计划的途径。