Toktarova Vera Ivanovna
Доктор педагогических наук, Dr. Sci. (Pedag.), Associate Professor, Vice-Rector for Digital Transformation – Chief Project Officer, Mari State University, toktarova@yandex.ru, Yoshkar-Ola
Pashkova Yulia Alexeyevna
Master’s student of the Faculty of Physics and Mathematics in the field of training “Information Systems and Technologies”, Mari State University, pashkova_97.1998@mail.ru, Yoshkar-Ola

Predictive Analytics in Digital Education: Analysis and Evaluation of Students’ Learning Success

Problem and Aim. The article actualizes the problem of modeling the learning process within electronic educational environment, based on the use of educational analytics data. The purpose of the article is to reveal and substantiate the possibilities of using predictive analytics methods for analyzing and assessing the of students’ learning success based on their current educational results. Methodology. Theoretical, empirical and mathematical methods were used during the research. The construction of the predictive model was carried out on the basis of the technologies of intellectual analysis and processing of big data (Data Mining). Research results. The relevance of the use of methods for analyzing big data in the educational sphere for improving the quality and efficiency of the educational and pedagogical process has been substantiated. The definitions of the technology of data mining are given, its types are considered (descriptive, diagnostic, predictive and prescriptive). The machine learning methods used in predictive analytics algorithms are described. Examples of the using predictive analytics methods in various fields of activity are given. An algorithm for designing and developing a predictive model is described. On its basis, a predictive model is proposed for analyzing and assessing the success of student learning, implemented as part of an experimental study within training module “Optimization Methods”. To assess the quality of the model, the methods of linear and logistic regression, deep neural networks and a decision tree were applied. In conclusion, it is concluded that the development and implementation of a model based on a set of methods gives a synergistic effect – the ability to maximally take into account the peculiarities of a student’s work with educational material and the degree of its assimilation. And as a result, it allows you to increase the level of training of students, improve the quality of the educational material provided in an electronic environment, build an individual trajectory of learning and help eliminate the problem of student expulsion through timely action.
big data analysis, predictive analytics, digital education, learning success, model, student, university

1. Bystrova, T. Yu., Larionova, V. A., Sinitsyn, E. V., Tolmachev, A. V., 2018. Educational analytics of MOOCs as a tool for predicting successful students. Education Issues, no. 4, pp. 139–166. (In Russ., abstract in Eng.)
2. Vilkova, K. A., Zakharova, U. S., 2020. Educational analytics in traditional education: its role and results. University management: practice and analysis, no. 24 (3), pp. 59–76. (In Russ., abstract in Eng.)
3. Gusev, A. V., Novitsky, R. E., 2020. Predictive analytics technologies in the fight against the COVID-19 pandemic. Artificial intelligence in healthcare, no. 4, pp. 25–32. (In Russ., abstract in Eng.)
4. Zheleznov, M. M., 2020. Methods and technologies for processing big data. Moscow: Publishing house MISS – MGSU, 46 p. (In Russ.)
5. Kozlova, I. V., Saidakhmedova, M. B., 2021. Analytics in the theoretical spectrum of digitalization of higher educational institutions based on a review of foreign sources. International Research Journal, no. 4 (106), pp. 123–126. (In Russ., abstract in Eng.)
6. Kotova, E. E., 2019. Forecasting the success of learning in an integrated educational environment using online analytics tools. Computer tools in education, no. 4, pp. 55–80. (In Russ., abstract in Eng.)
7. Limanovskaya, O. V., Alferieva, T. I., 2020. Fundamentals of Machine Learning. Yekaterinburg: Publishing house of the Ural University, 88 p. (In Russ.)
8. National program “Digital Economy of the Russian Federation” [online]. Available at: https://base.garant.ru/72296050/ (accessed 10.08.2021). (In Russ.)
9. Ozerova, G. P., Pavlenko, G. F., 2019. Predicting student success in blended learning using data from educational analytics. Science for Education Today, vol. 9, no. 6, pp. 73–87. (In Russ., abstract in Eng.)
10. Patarakin, E. D., 2015. Joint network activity and educational analytics supporting it. Higher education in Russia, no. 5, pp. 145–154. (In Russ., abstract in Eng.)
11. Patarakin, E. D., 2015. Educational analytics of joint network activity. School technologies, no. 4, pp. 80–86. (In Russ., abstract in Eng.)
12. On National Goals and Strategic Development Objectives of the Russian Federation for the Period up to 2024. Decree of the President of the Russian Federation dated 07 May 2018, No. 204 [online]. Available at: https://base.garant.ru/71937200/ (accessed 10.08.2021). (In Russ.)
13. On the Strategy for the Development of the Information Society in the Russian Federation for 2017–2030. Decree of the President of the Russian Federation dated 05 September 2017, No. 203 [online]. Available at: https://www.garant.ru/products/ipo/prime/doc/71570570/ (accessed 10.08.2021). (In Russ.)
14. Fedin, F. O., Fedin, F. F., 2012. Data analysis. Data Mining Tools. Moscow: Moscow City Pedagogical University, 308 p. (In Russ.)
15. Chubukova, I. A., 2020. Data Mining. Moscow, Saratov: National Open University “INTUIT”, IP Ar Media, 469 p. (In Russ.)
16. Anthony Dalton, Justin Beer, Sriharshasai Kommanapalli, James S. Lanich, 2018. Machine Learning to Predict College Course Success. SMU Data Science Review, vol. 1, no. 2, art. 1. (In Eng.)
17. D. Revina Rebecca, Mahammad Ashraf, Partha Pratim Sarkar, 2019. A Classification based predictive model to predict students aspiring for higher education. Proceedings of the 2nd National Conference on Advanced Computing Technologies and Applications (NCACTA 2019). (In Eng.)
18. Fazal Aman, Azhar Rauf, Rahman Ali, Farkhund Iqbal, Asad Masood Khattak, 2019. A Predictive Model for Predicting Students Academic Performance. Proceedings of the 10th International Conference on Information, Intelligence, Systems and Applications (IISA). DOI: 10.1109/IISA.2019.8900760 (In Eng.)
19. SmartDataCollective. Site about big data, analytics, artificial intelligence and the cloud [online]. Available at: https://www.smartdatacollective.com/amazon-using-big-data-analytics-read... (accessed 10.08.2021). (In Eng.)
20. Systems Engineering Thinking Wiki. Site about systems engineering, project management and related area [online]. Available at: http://sewiki.ru/Data_Mining (accessed 10.08.2021). (In Eng.)
21. Tech Funnel [online]. Available at: https://www.techfunnel.com/hr-tech/top-3-examples-of-predictive-analytic... (accessed 10.08.2021). (In Eng.)
22. Usman, Ashfaq, Booma, P. M., Raheem, Mafas, 2020. Managing Student Performance: A Predictive Analytics using Imbalanced Data. International Journal of Recent Technology and Engineering (IJRTE), pp. 2277–2283. (In Eng.)
23. Vaibhav Kumar, M. L. Garg, 2018. Predictive Analytics: A Review of Trends and Techniques. International Journal of Computer Applications, pp. 31–37. (In Eng.)