ON THE CLASSIFICATION OF STUDENTS' CONFUSION DURING THE ONLINE EDUCATION PROCESS USING MACHINE LEARNING METHODS

UDC 519.6
DOI: 10.36871/2618-9976.2022.11-2.006

Authors

Eugeny Yu. Shchetinin
Doctor of Phys.math. Sciences, Professor, Department of Mathematics, Financial University under the Government of the Russian Federation, Moscow, Russia

Abstract

In this paper, the problems of improving the quality of the online education are investigated based on the analysis of the process of students viewing course materials on one of the open online platforms (MOOC). In the analysis of the quality of the training conducted, the metric of the student's confusion in the learning process was used. For this, students' electroencephalograms and machine learning methods were used to analyze them to recognize confusion. A hybrid deep learning model 1D_CNN+LSTM is proposed, which has achieved classification accuracy of 96,22%, that exceeds the performance of the other classifiers considered in the article.

Keywords

Distance learning, Confusion, Electroencephalogram, Machine learning, Deep neural networks