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The relevance of integrating digital tools into university courses on Russian language teaching methodology is driven by the transformation of the professional training system for linguists, the need to explore the application of pervasive technologies as teaching aids and subjects of study, and the necessity of creating a digital educational environment for implementing higher education programs in the program “44.03.05 Pedagogical Education.” The research aims to describe the components of an expert system based on individualization and feedback that accompanies the professional training of future Russian language teachers. Indicators of the development of a student’s linguistic and methodological competence include the ability to (1) select linguistic and didactic materials according to the method of developed communicative activity; (2) choose methods and techniques for studying lexical and grammatical topics; and (3) develop the student’s educational and language abilities and manage their learning. To achieve the research goal, the authors applied parametric and robust statistics, principles of mathematical analysis, and linear algebra. The authors established that the robust method of finding cluster centers serves as a research tool for correcting and evaluating the student’s linguistic and methodological competence through their interaction with a potential student possessing predefined learning characteristics. The practical outcome is increased accuracy in assessing students’ linguistic and methodological competence. The developed software complex is used for statistical modeling of educational situations specific to Russian language lessons and supporting philology students in choosing specific methods of action. The novelty of this research lies in the proposal to use robust methods for finding cluster centers in an expert system for assessing professional competence. The research materials provide an applied solution for managing the digital transformation of higher education—specifically, the practical training of future Russian language teachers using artificial intelligence.

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Expert System with Robust Clustering Methods for Assessing Linguistic and Methodological Competence of Philology Students

  • 章节

  • 发表日期：

    2025年

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  • Iren Yu. Gats,
  • Irina N. Dobrotina,
  • Olga M. Aleksandrova,
  • Egor D. Vuychik

摘要

The relevance of integrating digital tools into university courses on Russian language teaching methodology is driven by the transformation of the professional training system for linguists, the need to explore the application of pervasive technologies as teaching aids and subjects of study, and the necessity of creating a digital educational environment for implementing higher education programs in the program “44.03.05 Pedagogical Education.” The research aims to describe the components of an expert system based on individualization and feedback that accompanies the professional training of future Russian language teachers. Indicators of the development of a student’s linguistic and methodological competence include the ability to (1) select linguistic and didactic materials according to the method of developed communicative activity; (2) choose methods and techniques for studying lexical and grammatical topics; and (3) develop the student’s educational and language abilities and manage their learning. To achieve the research goal, the authors applied parametric and robust statistics, principles of mathematical analysis, and linear algebra. The authors established that the robust method of finding cluster centers serves as a research tool for correcting and evaluating the student’s linguistic and methodological competence through their interaction with a potential student possessing predefined learning characteristics. The practical outcome is increased accuracy in assessing students’ linguistic and methodological competence. The developed software complex is used for statistical modeling of educational situations specific to Russian language lessons and supporting philology students in choosing specific methods of action. The novelty of this research lies in the proposal to use robust methods for finding cluster centers in an expert system for assessing professional competence. The research materials provide an applied solution for managing the digital transformation of higher education—specifically, the practical training of future Russian language teachers using artificial intelligence.