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Neural network technology for developing students’inventive competencies: Application of digital twins and machine learning algorithms

https://doi.org/10.32517/0234-0453-2025-40-4-76-86

Abstract

This article presents a statistical analysis of data obtained during a study of the effectiveness of a technology for developing children’s inventiveness, integrated with digital tools, as a method for fostering creativity and moral qualities in students. The data was collected during an experiment involving 480 schoolchildren and 270 engineering students. The methodology was based on the use of training neural networks and digital twins to simulate inventive activity. The analysis revealed a significant increase in the study indicators in the experimental group: creativity by 28%, the share of high levels of inventiveness from 12 %to 38 %, and the priority of moral motives from 28 %to 56 %. The study constructed mathematical models of the dynamics of creativity (logistic) and inventiveness (exponential). Key predictors of the technology’s effectiveness were identified: the intensity of neural network use, involvement invirtual modeling, and the volume of digital artifacts,whichtogether explainup to 61 %of the variance inthe development of abilities. A gender-specific pattern was identified: boys showed greater improvement in spatial and technical thinking, while girls showed greater improvement in social orientation and communication skills. Cluster analysis revealed three typological groups of students: “Innovators” who demonstrated the highest values for the parameters studied; “Practitioners” who demonstrated moderate levels of creativity and inventiveness; and “Conservatives” who demonstrated relatively low levels of creativity and inventiveness. The study’s results will be useful for developing a ability theory and can serve as a basis for developing personalized programs to support scientific and technical creativity. 

About the Authors

S. A. Novoselov
Ural State Pedagogical University
Russian Federation

Yekaterinburg



L. S. Popova
Ural State Pedagogical University
Russian Federation

Yekaterinburg



P. A. Ivanov
Ural State Pedagogical University
Russian Federation

Yekaterinburg



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For citations:


Novoselov S.A., Popova L.S., Ivanov P.A. Neural network technology for developing students’inventive competencies: Application of digital twins and machine learning algorithms. Informatics and education. 2025;40(4):76-86. (In Russ.) https://doi.org/10.32517/0234-0453-2025-40-4-76-86

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ISSN 0234-0453 (Print)
ISSN 2658-7769 (Online)