Algorithm for making recommendations in electronic educational environments based on stochastic Markov models


graduate student
Russia, Bryansk State Technical University
[email protected]

Associate Professor of the Department of Computer Technologies and Systems
Russia, Bryansk State Technical University
[email protected]


In this artical proposing recommendation algorithm for educational resources in e-learning systems. The new approach uses Markov's model of evaluating the content of the systems by ordinary users to form the parameters of the initial state which characterizes a new user of the system as evaluations of the first resources (system content) to recommend interesting system elements to an active user. Thus the problem of "cold start" for the new users at the first stages of interaction with the system is solved. This problem is inherent in the system under development, because the e-learning system includes a module for making recommendations, which allows it to refer to the class of recommendation-based automated systems. The new approach will combine the use of the Markov process and the time factor to use them as a single source of data for making recommendations. This approach will be based on the principle of access analysis of similar users of the system (the similarity is determined by comparing their profiles) in the same periods of time. An integral part of the created system is also usability. Therefore, at the design stage, it is necessary to think about the ergonomics of the recommendations in the educational system.


mathematical modeling, learning systems, e-learning, remote learning, Markov chains, Markov process, cloud learning.

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The work was carried out as part of the DR-2022 event: III International competition of scientific papers and projects for young researchers “Digital Region 2022” (Science and Education ON-LINE)

Suggested citation

Goncharov Dmitriy Ivanovich , Gerashchenkova Tatyana Mikhaylovna
Algorithm for making recommendations in electronic educational environments based on stochastic Markov models// Modern Management Technology. ISSN 2226-9339. – #1 (97). Art. # 9713. Date issued: . Available at:

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