MODERN METHODS OF COLLECTING AND ANALYZING EMPLOYERS REQUIREMENTS FOR TRAINING IT WORKERS

Authors

  • Andrey V. Zatonskiy Author
  • Polina A. Shakleina Author
  • Roman V. Krasovskiy Author

Abstract

The development of the digital economy requires highly qualified IT specialists capable of meeting the growing demands of the market. Despite the significant increase in demand for IT professionals, the education system often fails to meet employers' expectations. The shortage of skilled personnel and the mismatch between graduates' competencies and market needs are key problems, which necessitate the development of new approaches to educational programs. Aim. The main objective of the research is to develop a concept for managing IT specialist training that takes into account modern employer requirements. This will optimize educational programs and improve the alignment of graduates' skills with current labor market demands. Materials and methods. To achieve the research objectives, machine learning methods and big data analysis were employed, along with vacancy monitoring from major online job platforms. Data on employer requirements were collected and systematized, and then compared with professional and educational standards. Natural Language Processing (NLP) technologies played a key role in the automated analysis of job vacancy texts. Results. The study resulted in the development of a concept for gathering and processing employer requirements to optimize IT specialist educational programs. A monitoring and analysis system for open data was created, allowing for regular updates to educational programs in line with labor market changes. The implementation of this system helps reduce the gap between graduates' professional competencies and employers' needs. Conclusion. The research results confirm that a systematic approach to managing IT specialist training, using employer requirements data, improves the quality of educational programs and increases graduates' competitiveness. This approach will help the education system more effectively adapt to the challenges of the digital economy, providing the labor market with in-demand specialists.

Author Biographies

  • Andrey V. Zatonskiy
    Dr. Sci. (Eng.), Prof., Head of the Department of Automation of Technological Processes, Perm National Research Polytechnic University, Berezniki Branch, Berezniki, Russia
  • Polina A. Shakleina
    Technical Support Engineer, LLC “QUASAR”, Berezniki, Russia
  • Roman V. Krasovskiy
    Postgraduate Student of the Department of Information Technologies and Automated Systems, Perm National Research Polytechnic University, Perm, Russia

Published

2025-02-15

Issue

Section

Control in Social and Economic Systems