DOI: 
http://dx.doi.org/10.15293/1813-4718.2301.06
UDC: 
378.1+004.8
Toktarova Vera I.
Доктор педагогических наук, Professor of the Department of Applied Mathematics and Computer Science, Mari State University, toktarova@yandex.ru, Yoshkar-Ola, Russia
Popova Olesya G.
artificial intelligence center employee, Mari State University, olesya_popova10@mail.ru, Yoshkar-Ola, Russia

Visual Analytics of Students’ Educational Data within the e-Learning System

Abstract: 
Today, the analysis of educational data is a rapidly developing area that contributes to improving the quality and efficiency of student learning in e-learning systems and environments. Visual analytics methods are the best means for reviewing and presenting educational data in a convenient and informative form for perception. The purpose of the article is the analysis of educational data using visualization methods to identify patterns in the educational activities of students. Methodology. The methodological basis of the study is a complex of theoretical, empirical and mathematical methods. The paper provides a visualization of educational data based on the electronic course “Fundamentals of Programming”, hosted in the electronic educational environment of the university (based on LMS Moodle). The data of 118 students who completed the course with different academic performance were considered. Research results. The paper substantiates the relevance of using visualization for the analysis of educational data. Methods of analysis are considered, areas of their application are given. An analysis of the works of domestic researchers is given. The educational data describing the study of theoretical material by students, the performance of practical and test tasks, the time spent in the e-learning system are analyzed. Based on the method of identifying relationships, the dependences and patterns of students’ activities in the study of the course are visually displayed. In conclusion, it is concluded that the behavior of students in the electronic course and their marks for practical and test tasks are interrelated. Regularities in the distribution of estimates are revealed. Visualization made it possible to present data in a visual and informative form for perception. The proposed approach can be useful in the analysis of a student’s digital footprint and building his digital profile.
Keywords: 
data visualization; analytics; educational data; pattern detection; the e-Learning system; e-course; student
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