Analyzing Machine Learning Models Based on Explainable Artificial Intelligence Methods in Educational Analytics

Abstract

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