Keywords: productivity of work activities, expert assessment method, machine learning, innovation, artificial intelligence, data modeling, researchers
An intelligent system for evaluating the performance of researchers in research organizations
UDC 681.518
DOI: 10.26102/2310-6018/2025.48.1.039
The relevance of the study is due to the fact that in the conditions of high competition for qualified personnel, research organizations seek to attract and retain talented employees. Effective motivation systems based on objective performance assessment are becoming an important tool for achieving this goal. Intelligent systems can provide management with analytical reports and recommendations based on data, which contributes to more informed decision-making in the field of motivation and management of employees. In this regard, this article is aimed at developing an intelligent system for assessing the performance of employees in research organizations, which is a powerful tool for analyzing and managing human capital in organizations. The expert method is based on the involvement of qualified specialists with deep knowledge and experience in the relevant field, which allows to increase the objectivity and reliability of the assessment results. The article describes the advantages and disadvantages of this approach. The work also proposes the use of a machine learning method to assess the performance of researchers based on key performance indicators. The main performance indicators selected for the assessment of labor activity are: scientific and educational activity, scientific work, presentation of results, scientific and educational activity. The materials presented in the article will be relevant and useful for the heads of scientific and research organizations.
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Keywords: productivity of work activities, expert assessment method, machine learning, innovation, artificial intelligence, data modeling, researchers
For citation: Sakharov Y.S., Kovaleva A.V. An intelligent system for evaluating the performance of researchers in research organizations. Modeling, Optimization and Information Technology. 2025;13(1). URL: https://moitvivt.ru/ru/journal/pdf?id=1837 DOI: 10.26102/2310-6018/2025.48.1.039 (In Russ).
Received 04.03.2025
Revised 20.03.2025
Accepted 24.03.2025
Published 31.03.2025