Abstract:
The article presents a method and a model developed for predicting students’ educational success based on educational data mining. The application of flexible learning tracks for students requires higher level of responsibility and monitoring by an educational institution. The innovative technical solutions are aimed at supporting decision-making processes, as well as timely monitoring and reporting regarding students with the risk of dropping out due to low academic performance. The introduction of the developed models to the university information management system enabled to monitor the learning tracks of students and predict deviations from the accepted values using the reports generated from the university's account.
Keywords:
digital transformation of the educational process, prediction, data mining, educational data mining.
Citation:
R. B. Kupriyanov, D. Yu. Zvonarev, “Development of the students’ educational success prediction model for universities”, Artificial Intelligence and Decision Making, 2021, no. 2, 11–20
\Bibitem{KupZvo21}
\by R.~B.~Kupriyanov, D.~Yu.~Zvonarev
\paper Development of the students’ educational success prediction model for universities
\jour Artificial Intelligence and Decision Making
\yr 2021
\issue 2
\pages 11--20
\mathnet{http://mi.mathnet.ru/iipr97}
\crossref{https://doi.org/10.14357/20718594210202}
\elib{https://elibrary.ru/item.asp?id=46326254}
Linking options:
https://www.mathnet.ru/eng/iipr97
https://www.mathnet.ru/eng/iipr/y2021/i2/p11
This publication is cited in the following 1 articles:
Yu. Yu. Yakunin, V. N. Shestakov, D. I. Liksonova, A. A. Danichev, “Predicting student performance using machine learning tools”, Informatika i obrazovanie, 38:4 (2023), 28
Citation in format AMSBIB
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