Factors of students’ dropout can be studied either by surveys among students or by analyzing data the university collects. In the work reported in this paper, we analyzed data known about students at the time of admission as well as data about the students' study achievements collected on a semester basis. Using data about students who enrolled in the academic year 2013/14, we created several data mining models to predict who will finish their studies successfully and who will not. Our results show that the key factor is the percentage of lost credit vouchers in the most recent semester. The pre-entry attributes have only a very small impact. We also created association rules of different types to find characteristics of students who did not successfully complete the first semester of study. Here, the factor that mainly increases the probability of a failure is the time gap between secondary and tertiary education.

可以通过对学生进行调查或分析大学收集的数据来研究学生辍学的因素。在本文报告的工作中，我们分析了入学时有关学生的已知数据以及关于每学期收集的学生学习成绩的数据。利用有关2013/14学年入学学生的数据，我们创建了多个数据挖掘模型，以预测谁将成功完成学业，而哪些人不会成功完成学业。我们的结果表明，关键因素是最近一个学期丢失的信用凭证的百分比。进入前的属性只产生很小的影响。我们还创建了不同类型的关联规则，以发现未成功完成第一学期学习的学生的特征。这里，