Design and Conceptual Modeling of a Synergetic Web Platform for Integrative Learning of the Kazakh Language and Expressive Stylistics
Sara Koyanbekova
Department of Kazakh and Russian Languages, Almaty Technological University; Almaty, Kazakhstan
https://orcid.org/0000-0002-1131-8503
Abylaikhan Bari
Institute of Automation and Information Technologies, Satbayev University, Almaty,Kazakhstan
https://orcid.org/0009-0002-0049-9123
Turgan Junussov
Turkebaev Project Management Institute, Department of Kazakh and Russian Languages, Satbayev University; Almaty,Kazakhstan
https://orcid.org/0000-0002-9650-0792
Saltanat Tashimbay
Department of Social Sciences and Humanities, Kazakh Leading Academy of Architecture and Civil Engineering, Almaty,Kazakhstan
https://orcid.org/0000-0002-8000-8330
Zhanat Ibragimova
Department Head of Social Work, Satbayev University Almaty,Kazakhstan
https://orcid.org/0009-0007-5924-7189
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Keywords

Expressive stylistics
digital learning platform
linguistic analysis
web-based education
Kazakh

How to Cite

Koyanbekova, S., Bari, A., Junussov, T., Tashimbay, S., & Ibragimova, Z. (2026). Design and Conceptual Modeling of a Synergetic Web Platform for Integrative Learning of the Kazakh Language and Expressive Stylistics. Journal of Culture and Values in Education, 9(2), 162-184. https://doi.org/10.46303/jcve.2026.24

Abstract

The objective of this research is to examine the process of teaching and analyzing the expressive stylistics of the Kazakh language through a digital educational environment. The study addresses the problem of insufficient methodological and technological tools for the systematic teaching of complex expressive linguistic constructions. To address this, the study proposes a conceptual model for a synergetic web-based platform that integrates linguistic theory with digital technologies. Data collection was conducted over a six-month period at major Kazakhstani universities using a mixed-methods design. The instruments included a specialized research corpus comprising 32,000-word tokens for system training, a validated multi-section digital questionnaire assessing linguistic knowledge and user attitudes, and a standardized 10-point expert evaluation form. Empirical evaluation involved 218 undergraduate and graduate students and an expert panel. Pre–post testing demonstrated a significant improvement in the identification of expressive stylistic units, with student accuracy increasing from 46.8% to 71.3% (p < 0.001). Expert assessments (mean score of 4.7/5.0) confirmed the accuracy of the implemented classifications, including evaluative suffixation and syntactic emphasis patterns. The platform proves effective for university courses and professional development requiring scalable, consistent instruction in Kazakh stylistic analysis.

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