The paper draws attention to the comparative dynamics of university ranking factors for the last five years (2013-2018). The period under review is characterized by an active transition to the information society. The article deals with a relevant problem of identifying factors in the educational organization ranking formation. These factors are significant in the period of transition to an information society. The aim of the research is development and pilot approbation of the author’s algorithm of dynamic subject-oriented analysis on factors forming a ranking of an educational institution. The basic methods of this research include: factor analysis for the university ranking formation, theoretical and experimental substantiation of the author’s algorithm of dynamic subject-oriented analysis, statistical methods of comparison of teachers and heads of educational organizations, a uniform experimental basis of interpretation and prediction of dynamics of the ranking reflection in the organization’s information environment. The author’s algorithm is based on the mechanisms of factor analysis of subjective representations. The dynamic basis of the author’s algorithm includes the analysis of stability and sustainability of the considered factors observed in the questionnaire surveys conducted in the period from 2013 to 2018. The poll respondents were 150 leaders and pedagogical staff of technical schools and colleges of the Russian Federation. The subjective focus of the author’s algorithm consists of separate opinions of teachers and managers on the educational organization ranking. The significance of a number of factors in the ranking structure, which remain relevant for five years, is confirmed. Among such factors are: the developed information and educational environment, the faculty capacity, the number of accredited specialties and the issuance of degree certificates. The ranking structure revealed the most dynamic positions with a high significance and output beyond the limits of the previously established statistical deviation. In addition, there are factors, the priority of which has been identified recently, for example, the factor of demand by entrants, recommended by the management, and the factor of research activity that was recommended by teachers. The predicted changes for the near and distant future were also revealed. We draw a conclusion about the applicability of the proposed algorithm to identify common factors. The role of education system actors in the formation of university rankings is determined. In the open information environment the algorithm contributes to dynamic forecasting for the near and distant future.

educational organization ranking; significant factors; analysis algorithm; education system actors