A Model for Assessing the Rating of Higher Education School Academic Staff Members Based on the Fuzzy Inference System

Abstract

In this paper, we present the model of assessment of higher education school academic staff members rating based on the complex use of Harrington's desirability function and a fuzzy inference system. Four main directions of activity were evaluated within the framework of the proposed model: pedagogical activity; science and research; organization activity and other activity types. Each of the proposed types of activities was assessed separately using the Harrington desirability function based on prior determined scores, where the general activity index of the appropriate score was calculated as the weighted average of the private desirability values. The Mamdani inference algorithm with triangular and trapezoidal membership functions was used within the framework of the fuzzy inference process implementation to evaluate the general rating score of the appropriate academic staff member. The base of rules was proposed to form the output for general rating scores calculated based on the individual rating scores. The output of the system was presented for each of the appropriate activity types separately and based on the assessment of the general rating score that contained the separate rating scores as the components. To our best mind, the proposed model can help us to optimize the functional possibilities of higher school system operation.