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 characte...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Plekhanov Russian University of Economics
2021-07-01
|
| Series: | Открытое образование (Москва) |
| Subjects: | |
| Online Access: | https://openedu.rea.ru/jour/article/view/791 |
| Summary: | 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 state and corporate information resources, robotics, access control systems, information retrieval, control systems, etc., and is already beginning to find application in the field of distance educational technologies. In March 2021, the Government of the Russian Federation developed a decree on the basis of which higher educational institutions are allowed to use distance learning technologies. Conducting remotely activities of intermediate and final certification, as well as monitoring the current progress of both distance learning students and full-time and part-time students with a temporary transition to distance learning in a pandemic, the problem of identifying the student’s personality arises 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 on the 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 students in distance education systems it is first necessary to form the actual base of biometric characteristics of the authorized users, which will be 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 of learning 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 authorized users in the field of distance learning technologies, and relying on the results obtained in the course of generalization and analysis of existing experience and our own studies, the authors 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 of authorized users and user authentication according to the previously formed reference base, and also revealed that when training a neural network, it is necessary to take into account a sufficiently large number of different attributes affecting it. With an insufficient number of training sets (datasets), neural networks begin to perceive errors as reliable information, which, as a result, will lead to the need to retrain neural networks. With a sufficiently large number of training sets (dataset), more versions of dependencies and variability appear, which makes it possible to create rather complex machine learning models of neural networks, in which retraining takes the main place. |
|---|---|
| ISSN: | 1818-4243 2079-5939 |