Adaptive prediction of student learning outcomes in online mode

In conditions of a mass character of higher education, the problems of student identification are relevant because of the differences in the contingent of students in terms of level of training, personal and cognitive characteristics. The learning process is characterized by the presence of uncertainty factors, which requires modeling and control of the application process of methods and tools of adaptive prediction of trajectories of students' learning. A method has been developed for the sequential refinement of the prediction of students' learning, taking into account individual cognitive characteristics.