Analysis of students performance in relation to the results of state unified exam: the case of Russian university
Abstract
Purpose – Considering the limited number of studies covering the topic, the goal is to check the existence of the correlation between the results of Russia’s Unified State Exam and performance at the university.
Research methodology – the article uses quantitate analysis (regression) of the student performance on a sample of 4664 students. To provide statistical evaluation, the authors use SPSS Statistics software.
Findings – the research suggests, that results of unified state exam and individual students scores, awarded by the university under restrictions, are non-efficient in terms of predicting student performance. On the opposite, students’ performance during their first semester is a good predictor for the whole period of academic studies. As existing results of testing such hypotheses are inconsistent, the research provides value to the field of educational research.
Research limitations – data for research refer to only Kazan National Research Technical University named after A. N. Tupolev (KNRTU-KAI).
Practical implications – the research clearly indicate, that the universities cannot rely solely on the unified state exam during admission; they are to use different assessment tools to ensure future academic performance and lower dropouts rate.
Originality/Value – There is a gap in the investigation the link between secondary education and higher education performance.
Keyword : students’ performance, admission criteria, Russia Unified State Exam
How to Cite
Svirina, A., Lopatin, A., & Titko, J. (2021). Analysis of students performance in relation to the results of state unified exam: the case of Russian university. Business, Management and Economics Engineering, 19(1), 170-179. https://doi.org/10.3846/bmee.2021.14201
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Zamkov, O., & Peresetsky, A. (2013). Russian Unified National Exams (UNE) and academic performance of ICEF HSE students. Applied Econometrics, 30(2), 93–114.
Zivcic-Becirevic, I., Smojver-Azic, S., & Martinac Dorcic, T. (2017). Predictors of University students’ academic achievement: A prospective study. Društvena istraživanja: časopis za opća društvena pitanja, 26(4), 457–476. https://doi.org/10.5559/di.26.4.01
Alyahyan, E., & Düştegör, D. (2020). Predicting academic success in higher education: literature review and best practices. International Journal of Educational Technology in Higher Education, 17(1), 3. https://doi.org/10.1186/s41239-020-0177-7
Aziz, S. M., & Awlla, A. H. (2019). Performance analysis and prediction student performance to build effective student using data mining techniques. UHD Journal of Science and Technology, 3(2), 10–15. https://doi.org/10.21928/uhdjst.v3n2y2019.pp10-15
Beharu, W. T. (2018). Psyhological factors affecting students academic performance among freshman psychology students in Dire Dawa University. Journal of Education and Practice, 9(4), 59–65.
Chatterjee, S., Hadi, A. S., & Price, B. (2000). Regression analysis by example (3rd ed.). John Wiley & Sons, New York.
Curtis, D. A., Lind, S. L., Plesh, O., & Finzen, F. C. (2007). Correlation of admissions criteria with academic performance in dental students. Journal of Dental Education, 71(10), 1314–1321. https://doi.org/10.1002/j.0022-0337.2007.71.10.tb04395.x
European Commission. (2020). Education and training. https://ec.europa.eu/education/policies/european-policy-cooperation/et2020-framework_en
Eurostat. (2019). Early leavers from education and training. https://ec.europa.eu/eurostat/statistics-explained/index.php/Early_leavers_from_education_and_training
Gruzdev, I., Gorbunova, E., & Frumin, I. (2010) Students need help, not exclusion. https://iq.hse.ru/news/177668602.html
Goldstein, H., & Thomas, S. (1996). Using examination results as indicators of school and college performance. Journal of the Royal Statistical Society: Series A (Statistics in Society), 159(1), 149–163. https://doi.org/10.2307/2983475
Hellas, A., Ihantola, P., Petersen, A., Ajanovski, V. V., Gutica, M., Hynninen, T., Knutas, A., Leinonen, J., Messom, Ch., & Liao, S. N. (2018, July). Predicting academic performance: A systematic literature review. In Proceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education (pp. 175–199). https://doi.org/10.1145/3293881.3295783
Hughes, C., Gremillion, H., Bridgman, G., Ashley, P., & McNabb, D. (2017). Student selection process effectiveness: Correlations between task performance and undergraduate success. Aotearoa New Zealand Social Work, 29(4), 32–48. https://doi.org/10.11157/anzswj-vol29iss4id385
Kent, J. D., & McCarthy, M. T. (2016). Holistic review in graduate admissions: A Report from the Council of Graduate Schools. Washington, DC: Council of Graduate Schools. https://cgsnet.org/ckfinder/userfiles/files/CGS_HolisticReview_final_web.pdf
Khan, B., Khiyal, M. S. H., & Khattak, M. D. (2015). Final grade prediction of secondary school student using decision tree. International Journal of Computer Applications, 115(21), 32–36. https://doi.org/10.5120/20278-2712
Khavenson, T., & Solovyova, A. (2014). Studying the relation between the Unified State Exam points and higher education performance. Educational Studies, 1, 176–199. https://ideas.repec.org/a/nos/voprob/2014i1p176-199.html
OECD. (2017). Education at a Glance 2017. https://www.oecd-ilibrary.org/docserver/eag-2017-34-en.pdf?expires=1604676581&id=id&accname=guest&checksum=91EE7E047E0C52CFCFDCAA26A50201E2
Olufemi, O. T., Adediran, A. A., & Oyediran, W. O. (2018). Factors affecting students’ academic performance in colleges of education in Southwest, Nigeria. British Journal of Education, 6(10), 43–56.
Ramesh, V. A. M. A. N. A. N., Parkavi, P., & Ramar, K. (2013). Predicting student performance: A statistical and data mining approach. International Journal of Computer Applications, 63(8), 35–39. https://doi.org/10.5120/10489-5242
Rastrollo-Guerrero, J. L., Gómez-Pulido, J. A., & Durán-Domínguez, A. (2020). Analysing and predicting students’ performance by means of machine learning: A review. Applied Sciences, 10(3), 1042. https://doi.org/10.3390/app10031042
Sembiring, S., Zarlis, M., Hartama, D., Ramliana, S., & Wani, E. (2011, April). Prediction of student academic performance by an application of data mining techniques. International Conference on Management and Artificial Intelligence IPEDR, 6(1), 110–114.
Semyonov, D., Isaeva, N., Platonova, D., & Kobtseva, A. (2015) Student accountability in Post-Soviet countries. https://unesdoc.unesco.org/in/documentViewer.xhtml?v=2.1.196&id=p::usmarcdef_0000259570&file=/in/rest/annotationSVC/DownloadWatermarkedAttachment/attach_import_8a4052fd-018a-4379-9401-601e1b771531%3F_%3D259570eng.pdf&locale=en&multi=true&ark=/ark:/48223/pf0000259570/PDF/259570eng.pdf#%5B%7B%22num%22%3A189%2C%22gen%22%3A0%7D%2C%7B%22name%22%3A%22XYZ%22%7D%2C0%2C792%2Cnull%5D
Shahiri, A. M., Husain, W., & Rashid, N. A. (2015). A review on predicting student’s performance using data mining techniques. Procedia Computer Science, 72, 414–422. https://doi.org/10.1016/j.procs.2015.12.157
Silva, M. C., Camanho, A. S., & Barbosa, F. (2020). Benchmarking of secondary schools based on Students’ results in higher education. Omega, 95, 102119. https://doi.org/10.1016/j.omega.2019.102119
Stanko, T., Valiev, M., & Johnston, D. M. (2016). Case study: Using Russia’s Unified State Exam and other admission metrics as a predictor of academic performance at an IT University. In ASEE’s 123rd Annual Conference. https://www.researchgate.net/publication/307168473_Case_Study_Using_Russia’s_Unified_State_Exam_and_other_admission_metrics_as_a_predictor_of_academic_performance_at_an_IT_University
Sulphey, M. M., Al-Kahtani, N. S., & Syed, A. M. (2018). Relationship between admission grades and academic achievement. Journal of Entrepreneurship and Sustainability Issues, 5(3), 648–658. https://doi.org/10.9770/jesi.2018.5.3(17)
Tatar, A. E., & Düştegör, D. (2020). Prediction of Academic performance at undergraduate graduation: Course grades or grade point average? Applied Sciences, 10(14), 4967. https://doi.org/10.3390/app10144967
University of Cambridge. (2020). Undergraduate study. https://www.undergraduate.study.cam.ac.uk/applying/what-are-we-looking-for
van der Vinne, V., Zerbini, G., Siersema, A., Pieper, A., Merrow, M., Hut, R. A., Roenneberg, T., & Kantermann, T. (2015). Timing of examinations affects school performance differently in early and late chronotypes. Journal of Biological Rhythms, 30(1), 53–60. https://doi.org/10.1177/0748730414564786
Yousafzai, I. I., & Jamil, B. (2019). Relationship between admission criteria and academic performance: A correlational study in nursing students. Pakistan Journal of Medical Sciences, 35(3), 858–861. https://doi.org/10.12669/pjms.35.3.217
Zamkov, O., & Peresetsky, A. (2013). Russian Unified National Exams (UNE) and academic performance of ICEF HSE students. Applied Econometrics, 30(2), 93–114.
Zivcic-Becirevic, I., Smojver-Azic, S., & Martinac Dorcic, T. (2017). Predictors of University students’ academic achievement: A prospective study. Društvena istraživanja: časopis za opća društvena pitanja, 26(4), 457–476. https://doi.org/10.5559/di.26.4.01