Developing decision-making procedures for elective course selection considering the competence of participants in the educational process

• Date: 2026
• Main Authors: Кудін, В. І., Іларіонов, О. Є., Циганок, В. В., Власенко, О. О., Подскребко, О. С., Золотухіна, О. А.
• Format: Article
• Language: Ukrainian
• Published: Інститут проблем реєстрації інформації НАН України 2026
• Subjects: individual educational trajectory; elective disciplines; multi-criteria decision-making; competence assessment; AHP; TOPSIS; decision support; educational analytics
• Online Access: https://drsp.ipri.kiev.ua/article/view/358841
• Journal Title: Data Recording, Storage & Processing

In this study, it is proposed an integrated mathematical model for forming an individual educational trajectory (IET) of a student based on the combination of multi-criteria decision-making (MCDM) methods and formalized competence assessment of participants in the educational process. The relevance of the investigation is determined by the increasing complexity of elective course selection under conditions of multiple criteria and conflicting objectives. First, the research analyzes and compares widely used MCDM methods, including the Analytic Hierarchy Process (AHP), TOPSIS, ELECTRE, and ranking-based approaches. Their advantages, limitations, and applicability to educational decision-making problems are substantiated. Second, the problem of elective discipline selection is formalized as a discrete multi-criteria optimization problem in the criteria space. The structure of alternatives, criteria, and the integral objective function is defined. Third, a key contribution of the study is the integration of a competence coefficient into the decision-making model. The competence level is determined using a normalized student rating and is incorporated into the integral evaluation function together with criteria weights and threshold constraints. Furthermore, the algorithms and procedures for implementing the proposed model, including ranking methods with and without competence consideration, as well as mechanisms for competence adjustment have been developed. The research also presents a conceptual architecture of an information and advisory decision support system, including modules for data collection, processing, visualization, and feedback. The scientific contribution lies in combining MCDM methods with competence-based evaluation into a unified decision-making framework for educational environments. The practical value of the proposed approach consists in improving the objectivity, transparency, and personalization of elective course selection and supporting informed academic decision-making. Tabl.: 4. Refs: 23 titles.