Statistical analysis of academic performance (by an example from the faculty of economics of Voronezh State University)

Keywords: higher education, economic analysis, academic performance

Abstract

Importance. We live in a world in which educational processes are one of the constituent components of the modern society life. It is a well-known fact that higher education processes play an important role in achieving both individual and collective effectiveness. Therefore, they are subject to careful state and public monitoring.
Objectives. Identification of statistically significant determinants of students' academic performance and in order to improve the quality of the educational process.
Methods.
During the research, we used parametric and nonparametric methods of data analysis and machine learning. We formed the research information base using data on educational activities, academic performance, points on the Unified State Exam, as well as generalized address data of students in the bachelor's degree in the areas of "Economics", "Management" 2013-2015 recruitment.
Results. In the empirical part of the study, we tested several working hypotheses about the relationship of student academic performance with home region, gender, source of funding for educational activities, and the results of entrance tests. We also tested the hypothesis about the relationship between the completion of the educational activities of students due to academic failure and their low entrance score. We found that school-related stress has little to no impact on academic performance. Female students show better results in both areas of training. Girls who study at the expense of the federal budget demonstrate greater motivation for high academic performance compared to students at their own expense. There were no statistically significant linear relationships between the entrance scores and student performance. It would be unfair not to mention that fact that students completing their educational activities due to academic failure entered mainly with low total USE scores. Therefore it makes sense to significantly strengthen fundamental training, namely the classroom load in the disciplines of the basic part of the curriculum, develop convergent educational areas, and also strengthen interdisciplinary ties and the practice of studying the logic of cognition in order to smooth out the heterogeneity in the formation of competencies among students.

Metrics

Metrics Loading ...

Author Biographies

Pavel A. Kanapukhin, Voronezh State University

Dr. Sci. (Econ.), Assoc. Prof., Head of Economics, Marketing and Commerce Department

Viacheslav V. Korotkikh, Voronezh State University

Cand. Sci. (Econ.), Assoc. Prof., IT and Mathematical Methods in Economics Department

Svetlana S. Shchekunskikh, Voronezh State University

Cand. Sci. (Phys.-Math.), Assoc. Prof., IT and Mathematical Methods in Economics Department

References

Levene, H. (1960) Robust tests for equality of variances. In Olkin, I. (ed.) Contributions to Probability and Statistics. Palo Alto, California, Stanford University Press, pp. 278-292.

Bartlett, M. S. (1937) Properties of Sufficiency of Statistical Tests. Proc. Roy. Soc. A 160, 268-287.

Shapiro, S. S. & Wilk, M. B. (1965) An Analysis of Variance Test for Normality. Bio-metrika. 52(3), 591-611.

Filliben, J. J. (1975) The Probability Plot Correlation Coefficient Test for Normality. Technometrics. 17(1), 111-117.

Kruskal, W. H. & Wallis, W. A. (1952) Use of Ranks in One-criterion Variance Analysis. Journal of the American Statistical Association. 47(260), 583-621.

Antonenkov, E. & Kryukov, Y. (2019) Intellectual analysis of personal preferences of students in the course of training in higher education. Modern Science: Actual problems of theory and practice. Series: Natural and Technical Sciences. 11(2), 30-34. (In Russ.)

Bystrova, T., Larionova, V., Sinitsyn, Е. & Tolmachev, А. (2018) Learning Analytics in Massive Open Online Courses as a Tool for Predicting Learner Performance. Educational Studies Moscow. 4, 139-166. (In Russ.)

Grankov, M. & Al-Gabri, W. (2017) Regression model of the performance of students groups in higher education institution. Engineering journal of Don. 1(44), 46. (In Russ.)

Endovitsky, D. (2019). [The Moscow International Ranking "Three University Missions” as an Item for the Analysis of the Current State and Development Prospects of Universi-ties]. Proceedings of Voronezh State University. Series: Problems of Higher Education. 1, 5-11. (In Russ.).

Endovitsky, D., Korotkikh, V., & Voronova, M. (2020). Competitiveness of Russian Universities in the Global System of Higher Education: Quantitative Analysis. Vysshee obrazovanie v Rossii [Higher Education in Russia]. 29(2), 9-26. (In Russ., abstract in Eng.)

Kondrateva, E. & Kondrateva O. (2018) Analysis of efficiency of forecasting prosperity on the basis of fuzzy logic. In: Dudov, S. (ed.) Mathematical and computer modeling in economics, insurance and risk management : Proceedings of the International youth scien-tific and practical conference, 14-17 November 2018, Saratov. pp. 79-82. (In Russ.)

Kotova, Е. & Pisarev, A. (2019) Automated prediction of student learning outcomes. Proceedings of Saint Petersburg Electrotechnical University. 5, 31-39. (In Russ.)

Kuznetsov, V., Bayramov, R., Smirnov, E., Kosilova, E. & Kosilov K. (2019) The rela-tionship of self-assessment of health status and morbidity with academic performance in senior students of medical specialties, taking into account the impact of socio-economic and demographic characteristics. Medical Almanac. 5-6 (61), 10-15. (In Russ.)

Martynov G. (2019) Statistical dependency analysis performance students from related factors. Aktualnye voprosy obrazovaniya. 3. 62-66. (In Russ.)

Nikulina, I. & Snezhkova, A. (2019) Study of the level of formation of motivation for learning activities of students. Vestnik of Samara University. History, pedagogics, philolo-gy. 25(3), 89-94. (In Russ.)

Sadovnichy, V. (2019). [Eurasian University Mission]. V. А. Sadovnichy et al. (Eds.). Three University Missions : Education, Science, Society. Moscow : Maks Press Publ., pp. 7-19. (In Russ.).

Stepanova, I., Ganzina, I., Atavina, O., Postnova, T. & Mugak V. (2018) Prognosis of training success in chemical disciplines according to results of the Unified state exam (USE) in medical university. International journal of experimental education. 11, 17-22. (In Russ.).

Tarasova, E. (2018) [Analysis of the influence of environmental factors on the annual dy-namics of student performance at a northern university]. Bulletin of the Northern State Medical University. 1(40), 13-14. (In Russ.)

Temnyatkina, O. & Tokmeninova, D. (2019) Models of teacher performance evaluation applied in various countries. Perspectives of science and education. 3(39), 489-499. (In. Russ.)

Shmarikhina, E. (2018) Investigation the factors of students performance. Vestnik NSUEM. 3, 130-143. (In. Russ.)

Published
2020-06-30
How to Cite
Kanapukhin, P. A., Korotkikh, V. V., & Shchekunskikh, S. S. (2020). Statistical analysis of academic performance (by an example from the faculty of economics of Voronezh State University). Proceedings of Voronezh State University. Series: Economics and Management, (2), 27-44. https://doi.org/10.17308/econ.2020.2/2899
Section
Accounting, statistics