Mikhail A. Stepanov, Olga M. Gerget

Tomsk polytechnic university “ISHITR”, V.A. Trapeznikov institute of control sciences,

The article proposes a comprehensive model for assessing the success of learning based on the methods of recognition theory and processing of multidimensional data within the activity-based direction, which includes a wide range of modern teaching methods based on intelligent analysis of student data, his digital footprint, planning elements in educational and methodological complexes and predicting learning outcomes. The learning trajectory is formed as an element-by-element clustering of interconnected sequences of labor intensity, intermediate results of mastering work programs for a large sample of students. A neural network model for predictive extrapolation of learning outcomes has been developed. The results of experimental studies of the model on data samples accumulated in digital learning management systems are presented.

individual educational trajectory, spatial extrapolation, parametric identification, learning success prediction, multilayer neural network, metric methods, Kullback-Leibler method