Application of Machine Learning Technologies for Personalising the Digital Educational Process in Higher Education Institutions / Primjena tehnologija strojnoga učenja za personalizaciju digitalnoga obrazovnog procesa u visokim obrazovnim ustanovama
DOI:
https://doi.org/10.15516/cje.v28i3.42892Keywords:
analytics; artificial intelligence; educational behaviour analysis; platforms; motivation; social interactionAbstract
Abstract
The aim of the study was to identify the main types of machine learning (ML) models that help adapt the educational environment to the individual needs of students in universities. The methodology combined a systematic analysis of approaches to teaching using ML algorithms with a comparative analysis of the digital transformation of higher education in Kyrgyzstan, Azerbaijan, Poland, and Ukraine. The analysis of ML approaches and the comparative analysis of the digitalisation of the examined educational institutions revealed key mechanisms for personalising the learning process. The comparison of practices showed that Poland and Ukraine prefer reinforcement learning models and academic performance prediction algorithms; Azerbaijan used clustering methods to group students by learning styles; and Kyrgyzstan implemented recommendation systems to improve motivation and feedback. The analysis suggests that the implementation of these technologies depends on four key factors: the level of digital competence of teachers, the availability and quality of educational data, the integration of adaptive modules into learning platforms, and the presence of state policies regulating the use of artificial intelligence (AI) in education. Additionally, it was found that personalised ML modules contribute to increasing students' intrinsic motivation, the development of self-learning skills, and active interaction within learning groups. The practical value of the work lies in the recommendations for deploying adaptive ML modules in higher education institutions in the countries under study, which will help improve the quality of education, student motivation, and reduce the gap in academic achievements.
Keywords: analytics; artificial intelligence; educational behaviour analysis; platforms; motivation; social interaction
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Sažetak
Cilj studije bio je identificirati glavne vrste modela strojnoga učenja (SU) koji pomažu prilagoditi obrazovno okružje individualnim potrebama studenata na sveučilištima. Metodologija je kombinirala sustavnu analizu pristupa poučavanju pomoću SU algoritama s komparativnom analizom digitalne transformacije visokoga obrazovanja u Kirgistanu, Azerbajdžanu, Poljskoj i Ukrajini. Analiza pristupa strojnoga učenja i rezultati komparativne analize digitalizacije ispitivanih obrazovnih ustanova otkrili su ključne mehanizme za personalizaciju procesa učenja. Usporedba praksi pokazala je da Poljska i Ukrajina preferiraju modele učenja pojačavanja i algoritme za predviđanje akademskoga uspjeha, Azerbajdžan je koristio metode klasteriranja za grupiranje studenata prema stilovima učenja, a Kirgistan je implementirao sustave preporuka za poboljšanje motivacije i povratnih informacija. Analiza ukazuje na to da implementacija ovih tehnologija ovisi o četirima ključnim čimbenicima: razini digitalne kompetencije nastavnika, dostupnosti i kvaliteti obrazovnih podataka, integraciji adaptivnih modula u platforme za učenje i postojanju državnih politika koje reguliraju upotrebu umjetne inteligencije (UI) u obrazovanju. Dodatno je utvrđeno da personalizirani SU moduli doprinose povećanju intrinzične motivacije studenata, razvoju vještina samostalnoga učenja i aktivnoj interakciji unutar grupa za učenje. Praktična vrijednost rada jest u preporukama za primjenu adaptivnih SU modula u visokim obrazovnim ustanovama u zemljama koje su predmet istraživanja, što će pomoći u poboljšanju kvalitete obrazovanja, motivacije studenata i smanjenju jaza u akademskim postignućima.
Ključne riječi: analitika; analiza obrazovnoga ponašanja; motivacija; platforme; socijalna interakcija; umjetna inteligencija
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Copyright (c) 2026 Nurkylych Nuraliev, Piotr Kusznieruk, Aizada Azhibekova, Andrii Burachynskyi, Hristo Kamenov (Author)
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