employee
Russian Federation
VAK Russia 1.2.1
UDC 004.9
UDC 378
UDC 656
Purpose: to eliminate the methodological gap between the evolving needs of the labour market particularly in light of the digital transformation in transportation and the slow-moving processes associated with the development of major professional educational programmes (MPEP). Methods: various existing approaches to modelling MPEP have been systematically analyzed and classified. The structure of the programme has been formalized through graph theory, while intelligent data analysis techniques have been applied to assess and process the demands of employers. Results: the study reveals the limitations associated with both expert and ontological approaches when dealing with large arrays of labor market data. A novel mathematical model of the MPEP has been formulated as a weighted directed graph G = (V, E), where the vertices integrate various disciplines, competencies, and employer requirements, including professional standard and job vacancies. Furthermore, an innovative integral indicator for programme quality is introduced, derived from an additive convolution of criteria assessing the completeness of competency coverage and the graph connectivity. Practical significance: the proposed model and algorithms establish a foundation for an automated decision support system. This system aims to minimize the adaptation time of educational programmes to the demands of high-tech sectors, such as unmanned transportation and digital logistics, thereby facilitating a shift towards evidence-based management of education.
mathematical modelling, educational programme, oriented graph, data mining, digital transformation of transport, integral quality indicator, decision support system
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