Research of the applicability of machine learning methods for assessment of departments’ performance

Abstract

Currently, a number of educational organizations in Russia and abroad, including the National Research University “Moscow Power Engineering Institute” (MPEI), are introducing the European improvement model EFQM, designed to analyze and improve the educational, scientific and other activities of the departments. In accordance with this model, each university department is assigned a score equal to the sum of points for two groups of criteria: criteria from the group of opportunities and criteria from the group of results. To obtain such assessments, a commission consisting of external experts, EFQM assessors and university staff meets with heads of departments. Based on the results of the discussion of the results of the meetings, the commission determines the score and rating of the departments in accordance with the EFQM model.The purpose of the work presented in the article is to study the possibility of using machine learning to simplify the work of experts in terms of obtaining estimates according to criteria from a group of results.The article proposes a system for evaluating the activities of departments according to criteria from a group of results based on machine learning. A program in the Python programming language has been developed, which evaluates the activities of departments according to these criteria for each department of the MPEI. The program receives the initial data for such assessments from the monitoring system of key performance indicators implemented in MPEI.