Preview

Informatics and education

Advanced search

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

https://doi.org/10.32517/0234-0453-2021-36-8-41-51

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.

About the Authors

A. R. Mukanova
National Research University "Moscow Power Engineering Institute"
Russian Federation

Alexandra R. Mukanova, a 4th year undergraduate student of the Department of Computing Machines, Systems and Networks, Institute of Information Technologies and Computer Science

Moscow



Sh. A. Otsokov
National Research University "Moscow Power Engineering Institute"
Russian Federation

Shamil A. Otsokov, Doctor of Sciences (Engineering), Associate Professor at the Department of Computing Machines, Systems and Networks, Institute of Information Technologies and Computer Science

Moscow



References

1. Maslov D. V., Mazaleczkaya A., Stid K. Model of EFQM improvement. Vysshee Obrazovanie v Rossii = Higher Education in Russia. 2005;(9):48–55.

2. Ovsyanitskiy A. D. On the use of the terms “artificial intelligence”, “machine learning” and “deep learning”. The digital economy in the socio-economic development of Russia: the view of the young. Proc. 15th Int. Scientific and Practical Conf. Chelyabinsk; 2019. P. 549–551.

3. Professional information and analytical resource dedicated to machine learning, pattern recognition and data mining. Available at: http://www.machinelearning.ru

4. Galimov R. G. Fundamentals of machine learning algorithms — supervised learning. Alley of Science. 2017;1(14):810–817.

5. Stanula P., Ziegenbein A., Metternich J. Machine learning algorithms in production: A guideline for efficient data source selection. Procedia CIRP. 2018;78:261–266. DOI: 10.1016/j.procir.2018.08.177

6. Portugal I., Alencar P., Cowan D. The use of machine learning algorithms in recommender systems: A systematic review. Expert Systems with Applications. 2018;97:205–227. DOI: 10.1016/j.eswa.2017.12.020

7. Müller A., Guido S. An introduction to machine learning with Python. Moscow, Al`fa-kniga; 2018. 480 p.

8. Shalev-Shwartz S., Ben-David S. Understanding machine learning: From theory to algorithms. New York, Cambridge University Press; 2014. 449 p. DOI: 10.1017/CBO9781107298019

9. Klyachkin V. N. A method for estimating the parameter of ridge regression. Bulletin of the Ulyanovsk State Technical University. 1998;(1):38–40.

10. Borodin I. D. Reflection in machine learning on the example of learning decision trees. Alley of Science. 2017;4(9):857–865.

11. Leusenko V. P. Decision making with consideration of utility. Decision trees. Mathematical methods, models and information technologies. Proc. 18th Scientific and Practical Conf.. Taganrog; 2017. P. 184–187.

12. Azad М., Moshkov М. Multi-stage optimization of decision and inhibitory trees for decision tables with manyvalued decisions. European Journal of Operational Research. 2017;263(3):910–921. DOI: 10.1016/j.ejor.2017.06.026

13. Kaftannikov I. V., Parasich A. V. Methods for reducing the interdependence of decision tree errors in training decision forests. Scientific search. Proc. 7th Scientific Conf. Chelyabinsk; 2015. P. 315–320.

14. Dyakonov I. D., Novikova S. V. Solution of the forecasting problem using gradient boosting over decision trees. Scientific forum: technical and physical and mathematical sciences. Proc. 15th Int. Scientific and Practical Conf. Moscow; 2018. P. 9–12.

15. Chatkin V. V. Differences between artificial intelligence, machine learning and deep learning. Territories competitiveness. Proc. 22nd All-Russian Economic Forum of Young Scientists and Students. Ekaterinburg; 2019. P. 185–187.

16. Mingaliev Z. Z., Kychkin I. M. Solution of multidimensional inverse problems based on multilayer perceptron. Bulletin of Modern Research. 2019;(3.3):30–34.

17. Arefiev A. V., Shumaev V. V. Estimation of the significance of the regression equation, its coefficients, the coefficient of determination. Innovative ideas of young researchers for the agro-industrial complex of Russia. Proc. All-Russian Scientific and Practical Conf. Penza; 2019. P. 103–104.

18. Kinyakin V. N., Milevskaya Yu. S. Some caveats for checking the quality of a regression model using the coefficient of determination. Bulletin of the Moscow University of the Ministry of Internal Affairs of Russia. 2014;(8):200–204.

19. Bazilevskii M. P. Selection an optimal number of variables in regression models using adjusted coefficient of determination as a mixed integer linear programming problem. Applied Mathematics and Control Sciences. 2020;(2):41–54. DOI: 10.15593/2499-9873/2020.2.03

20. Brink X., Richards D., Feverolf M. Machine learning. Saint Petersburg, Piter; 2017. 336 p.


Review

For citations:


Mukanova A.R., Otsokov Sh.A. Research of the applicability of machine learning methods for assessment of departments’ performance. Informatics and education. 2021;(8):41-51. (In Russ.) https://doi.org/10.32517/0234-0453-2021-36-8-41-51

Views: 420


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 0234-0453 (Print)
ISSN 2658-7769 (Online)