The problem of predicting early dropout of students of Russian universities is urgent and therefore requires the development of new innovative approaches to solve it. To solve this problem, it is possible to develop predictive systems based on the use of student data, available in the information systems of universities. This paper investigates machine learning models for predicting early student dropout trained on the basis of student characteristics and performance data. The main scientific novelty of the work lies in the use of explainable AI methods to interpret and explain the performance of the trained machine learning models. The Explainable AI methods allow us to understand which of the input features (student characteristics) have the greatest influence on the results of the machine learning models. (student characteristics) have the greatest influence on the prediction results of trained models, and can also help to understand why the models make certain decisions. The findings expand the understanding of the influence of various factors on early dropout of students.