Automation of Student Knowledge Assessment on the Basis of Neural Network Technology (on the Example of Programming Subject)

In this article, in order to apply neural networks to the educational process, the part of algorithms and programming of programming subject structured the content of teaching materials on the basis of logical principles and formalized the materials of the topic in the form of semantic networks. In the practical application of artificial neural networks, attention is paid to the logical similarity of the organic connections between the implementation of this neural network training procedure and the effective methods of human pedagogy and the teaching procedure. In order to further improve, modernize the training of engineers and programmers in higher education, to bring it up to modern standards, the introduction of new organizational and pedagogical tools, new innovative technologies in education, methods of effective use of information and communication technologies, including neurotechnology. A predicate system for assessing student knowledge on the topic of algorithms and programming, a model for assessing knowledge. An algorithm based on Hopfield neural networks was developed in accordance with the predicate system of knowledge assessment. The methodology of registration of predicate systems of knowledge assessment on this topic and the method of building a neural network can be applied to other disciplines, with changes in the number of predicates, its essence. In pedagogical science, based on the generalization of the accumulated experience in the implementation of the procedure of teaching artificial neural networks, the systematization of psychological and pedagogical developments on the basis of certain criteria, structuring, detailing, effectively describing the components of science in accordance with the principles of logic.