The prediction of academic dropout, with the aim of preventing it, is one of the current challenges of higher education institutions. Machine learning techniques are a great ally in this task. However, attention is needed in the way that academic data are used by such methods, so that it reflects the reality of the prediction problem under study and allows achieving good results. In this paper, we study strategies for splitting and using academic data in order to create training and testing sets. Through a conceptual analysis and experiments with data from a public higher education institution, we show that a random proportional data splitting, and even a simple temporal splitting are not suitable for dropout prediction. The study indicates that a temporal splitting combined with a time-based selection of the students' incremental academic histories leads to the best strategy for the problem in question.

翻译：学术辍学预测及其预防是当前高等教育机构面临的主要挑战之一。机器学习技术在此任务中发挥着重要的辅助作用。然而，需要关注的是此类方法使用学术数据的方式，必须确保其能真实反映所研究预测问题的实际情况，并实现良好效果。本文研究了用于创建训练集和测试集的学术数据划分与使用策略。通过概念分析及某公立高等教育机构的实验数据验证，我们证明了随机比例数据划分，甚至简单的时间序列划分均不适用于辍学预测。研究表明，结合基于时间的学生增量学业历史记录选择的时间序列划分，是解决该问题的最佳策略。