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DESIGNING ADAPTIVE LEARNING SYSTEMS BASED ON INTELLIGENT ALGORITHMS

Authors

Name Affiliation
Oryngul Sadykanova D. Serikbayev East Kazakhstan Technical University, Ust-Kamenogorsk, Kazakhstan
Saule Smailova D. Serikbayev East Kazakhstan Technical University, Ust-Kamenogorsk, Kazakhstan
Saule Kumargazhanova D. Serikbayev East Kazakhstan Technical University, Ust-Kamenogorsk, Kazakhstan
Konrad Gromaszek Lublin University of Technology, Lublin, Poland

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Published:

2026-09-30

Article language:

Kazakh

Views:

55

Downloads:

22

Keywords:

интеллектуалды алгоритмдер; оқу стилі; құмырсқалар колониясын оңтайландыру (ACO); Байес білімді қадағалау (BKT); оқу траекториясы; бейімді жүйе, жекешелендіру

Abstract

In the era of technological progress, online learning is one of the priority directions in education. The use of intelligent algorithms in online education allows students not only to receive distance learning, but also to adapt it to their individual characteristics, that is, to create a unique learning trajectory for each student. In this article, intelligent algorithms of adaptive learning are considered, which guide/adapt the learning trajectory taking into account the individual characteristics of students.

In the work, the author presents an original methodology for designing the architecture of an adaptive learning system as a result of the integration of intellectual algorithms ACO and BKT. The proposed architecture allows you to create a personalized learning trajectory that adapts learning depending on the individual characteristics of each student. The architecture describes the main components of adaptive learning: the student model, the domain model and the adaptation model. As a result of the study, a comparison and evaluation of the effectiveness of existing and proposed intelligent algorithms is carried out.

Sadykanova, O., Smailova , S., Kumargazhanova , S., & Gromaszek , K. (2026). DESIGNING ADAPTIVE LEARNING SYSTEMS BASED ON INTELLIGENT ALGORITHMS. EKTU Journal of Information and Communication Sciences, 1(3). https://doi.org/10.51885/3134-8025_JICS_2026_3_9

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