BUILDING KNOWLEDGE GRAPHS FOR MOOC SELECTION BASED ON THE INTEGRATION OF LABOR MARKET DEMANDS AND EDUCATIONAL PROGRAMS

Abstract

The article presents the development of an integration model based on knowledge graphs and machine learning methods for analyzing relationships between educational programs, labor market requirements, and online courses (MOOCs). The relevance of the study is driven by rapid changes in the information technology sector, which lead to a mismatch between graduate competencies and employer expectations. To reduce this gap, a heterogeneous knowledge graph integrating data from university curricula, job vacancies, and online courses is proposed. The model implements a meta-path connecting skills, job requirements, and learning courses, enabling recommendations for educational and career development for different categories of users. Multilingual transformers were applied to process textual descriptions in Russian and English. The data sources include competency maps of educational programs, job postings from the hh.kz platform, and courses from Coursera, all integrated into the Neo4j graph database. The obtained results demonstrate the capability to identify missing competencies and automatically recommend relevant courses for their acquisition. The proposed model can be applied in curriculum design, development of learning trajectories, and decision-making support within educational environments.