The Phenomenon of Stochastic Metamodeling of Neuro-Digital Scientific and Educational Ecosystems
Abstract
One of the key trends in recent years is the possibility of creating and transforming knowledge based on the digital educational ecosystem and artificial intelligence. The concept of stochastic metamodeling of neuro-digital scientific and educational ecosystems (NDSEE) is proposed – the phenomenon of digital twins simulating organizational systems with a pronounced character of knowledge transfer. The implemented methodology combines network analysis models (Мn approach) and probabilistic models of distributive semantics (Мs approach). A neural network approach to clustering information search results posted in scientific bibliographic, academic, educational databases and external network resources is substantiated. The general results of the approbation of the proposed approach based on data processing of citation networks of scientific publications and networks of scientific terms compiled from samples of bibliographic databases arXiv, Scopus and Google Scholar are considered. Graphical results of modeling the probability of reaching the percolation threshold of networks of quotations from the arXiv section on theoretical high-energy physics hep-th are presented. Verification of the existence of mechanisms of interaction between the M and We models confirms that the method we present is working. The theoretical significance of the research lies in the development of new approaches to the complex stochastic modeling of the effectiveness of dynamic NDSEE using original computational methods and algorithms..
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