The Use of Neural Networks in Distance Education Technologies for the Identification of Students

Purpose of the research. The purpose of this research is to study the problems of the features of teaching technologies of modern artificial neural networks for carrying out the procedure of unambiguous authentication of students according to a pre-formed reference base of digital biometric characteristics of the authorized users in the field of distance educational technologies. In the modern world, artificial neural networks are successfully used in both applied and scientific fields. The problem of authenticating a human personality, implemented using artificial neural networks, finds practical application in solving problems such as the protection of stateandcorporateinformationresources,robotics,accesscontrolsystems,information retrieval, control systems, etc., and is already beginningto find application in the field of distance educational technologies.InM arc h2021,theGovernmentoftheRu ssia nFedera tio ndev elo peda decree on the basis of which higher educational institutions areallowed to use distance learning technologies. Conducting remotely activitiesofintermediateandfinalcertification,aswellasmonitoringthe current progress of both distance learning students and full-timeandpart-timestudentswitha temporary transition to distance learningin a pandemic, the problem of identifying the student’s personalityarises in order to achieve unambiguous recognition of the authorized users for the purpose of reliable assessment of learning outcomes, which can be solved using modern technologies of artificial neural networks.Materials and methods. Methods of reviewing scientific literature onthe research topic, methods of collecting, structuring and analyzing the information obtained were used as materials and methods.Research results. The results of the study allow us to draw the following conclusions: to solve the problem of authenticating studentsin distance education systems it is first necessary to form the actualbase of biometric characteristics of the authorized users, which willbe compared with the biometric data of the identified users, and for the recognition procedure, the neural network must be trained in advance on special trainers datasets. The identification procedure must be repeated several times during a session to ensure that the identity of the authorized user is verified.Conclusion. Realizing the set goal to study the problematics oflearningtechnologiesofmodernartificialneuralnetworksforcarrying outtheprocedureofunambiguousauthenticationofstudentsaccordingto a pre-formed reference base of digital biometric characteristicsof authorized users in the field of distance learning technologies,and relying on the results obtained in the course of generalizationandanalysisofexistingexperienceandourownstudies,theauthors identified two independent stages in the algorithm for the implementation of the task of identifying the student’s personality: the formation of a reference base of digital biometric characteristics ofauthorized users and user authentication according to the previouslyformed reference base, and also revealed that when training a neural network, it is necessary to take into account a sufficiently largenumber of different attributes affecting it. Withaninsufficient numberof training sets (datasets), neural networks begin to perceive errorsas reliable information, which, as a result, will lead to the need toretrain neural networks. With a sufficiently large number of trainingsets (dataset), more versions of dependencies and variability appear, which makes it possible to create rather complex machine learningmodels of neural networks, in which retraining takes the main place.