Adaptive system of recommendations of elective educational components for students

- Федонюк, Анатолій (orcid.org/0000-0003-0942-227X), Палій, Андрій (orcid.org/0009-0003-3638-0025), Михальчук, Ярослав (orcid.org/0009-0006-2484-0727) and Юнчик, Валентина (orcid.org/0000-0003-3500-1508) (2025) Adaptive system of recommendations of elective educational components for students Information Technologies and Learning Tools, 6 (110). pp. 135-150. ISSN 2076-8184

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Abstract

The article presents the development of an innovative system for automated selection of selective educational components (VOC) for university students. The system analyzes the learning outcomes and personal preferences of the user to dynamically form a list of VOCs, which is updated when interests change or additional courses are taken. The goal is to optimize an individual learning path, increase motivation and learning efficiency. The algorithm consists of three key stages: 1. Content-Based Filtering – analysis of thematic similarity of courses based on their descriptions and modules; 2. Collaborative Filtering – comparison of student profiles with similar outcomes to identify common elective course selections; 3. User Preferences – consideration of individual interests and goals of the student. The system continuously updates data on performance and changing preferences to ensure the relevance of recommendations. To verify the effectiveness of the developed system, an experimental study was conducted on a sample of students of various specialties. The results showed an increase in the average level of success and increased satisfaction with the educational process due to personalized recommendations. The integrated application of three methods provided an accuracy of over 85% in predicting the most relevant EQFs. This system implements a hybrid approach to personalized selection of elective educational components (EECs) based on student preferences. The combination of three methods enables the generation of recommendations tailored to the individual experience of the student and aligned with the choices of other users. Unlike traditional approaches that rely on manual processes or static templates, the system provides dynamic ranking of EECs based on selected categories of interest. Its implementation as an information technology platform built on a modern stack (NestJS + PostgreSQL) ensures scalability and integration into real educational environments.

Item Type:	Article
Keywords:	adaptive information technology platforms; recommender systems; selective educational components; personalization of learning; educational process.
Subjects:	Science and knowledge. Organization. Computer science. Information. Documentation. Librarianship. Institutions. Publications > 00 Prolegomena. Fundamentals of knowledge and culture. Propaedeutics > 004 Computer science and technology. Computing. Data processing > 004.7 Computer communication. Computer networks Science and knowledge. Organization. Computer science. Information. Documentation. Librarianship. Institutions. Publications > 00 Prolegomena. Fundamentals of knowledge and culture. Propaedeutics > 004 Computer science and technology. Computing. Data processing > 004.9 Application-oriented computer-based techniques Science and knowledge. Organization. Computer science. Information. Documentation. Librarianship. Institutions. Publications > 3 Social Sciences > 37 Education > 37.01/.09 Special auxiliary table for theory, principles, methods and organization of education > 37.02 General questions of didactics and method Science and knowledge. Organization. Computer science. Information. Documentation. Librarianship. Institutions. Publications > 3 Social Sciences > 37 Education > 378 Higher education. Universities. Academic study
Divisions:	Institute for Digitalisation of Education > Generic resouse
Depositing User:	Алла 1 Алла Почтарьова
Date Deposited:	27 Feb 2026 08:57
Last Modified:	27 Feb 2026 08:57
URI:	https://lib.iitta.gov.ua/id/eprint/748478

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