Implementing a Machine Learning Approach to Predicting Students' Academic Outcomes.

This research is dedicated to the problem of transforming ”linear” educational systems of higher education institutions into a new paradigm of person-centered, blended and individual education. This paper investigates role, application, and challenges of applying AI to predict the academic performance traditional of students: dropouts, GPA, publication activity and other indicators to decrease dropouts and make the learning process more personalized and adaptive. In the first part, we overview the process of data mining using internal university’s resources (LMS and other systems) and open source data from students’ social networks. Such an aggregation allows describing each student by socio-demographic and psychometric features. Further, we demonstrate how we can dynamically monitor students’ activities during the learning process to supplement the resulting features. In the second part of our research, we propose various static and dynamic targets for predictive models and demonstrate the results of predictions and comparisons of several predictive models. The research is based on the information on data processing of more than 20000 students in 2013-2019.