Predicting student performance using machine learning tools

Abstract

Digital assistants are increasingly penetrating various areas of human activity, including education. Today, they are no longer just automated systems or web applications that support and automate certain processes, including educational processes. Now they are more intelligent and more autonomous systems. Digital assistants play a special role in a student’s life, in a sense replacing the dean’s office, mentor, tutor, representatives of other university services and other elements of educational infrastructure. The digital support for the student is important and useful, especially in the first year during his adaptation to the environment of higher education, which is significantly different from the school one. It is at this point that the largest amount of students dropouts occurs due to academic failure. According to the authors, a digital assistant in the form of a mobile application that can predict learning outcomes and inform about it in time, can provide important support for the student and help him/her orient and adjust his/her behavior in case of a threat of a negative result. To solve the problems of creating a predictive model of student learning outcomes and a mobile application that implements it, as well as to conduct a pre-project study, the following methods and tools of mathematical statistics were used: k-means method, Kendall correlation method, Friedman’ test with Durbin—Conover posterior test, linear regression, logistic regression, categorical Bayesian classifier, random forest method, neural network (multilayer perceptron), non-parametric estimation of the Nadaraya—Watson regression function, STATISTICA 10.0 and Jamovi 2.2.5, Python libraries. As a result of the study, a mathematical model for predicting learning outcomes in disciplines based on current performance in e-learning courses was created. The accuracy of the model depends on the week of training in which it is applied and reaches 92,6 %. In the early stages (e. g., for week 7), the accuracy is at least 85 % and varies depending on the contingent of the student population and disciplines. As a result of the study, a mobile application was developed that implements a predictive model and other related functions to inform the student about his/her estimated educational success. The created predictive model is based on current performance data obtained from electronic courses and is capable of making accurate predictions, which allows it to be applied in practice online and through the mobile application to inform students.