Predictive Modeling in Higher Education: Determining Factors of Academic Performance
https://doi.org/10.31992/0869-3617-2023-32-1-51-70
Abstract
For several decades in the field of data mining in education (EDM), predictive learning has remained one of the most popular and internationally discussed research topics. Specifically, data mining is used to predict educational outcomes such as academic performance, retention, success, satisfaction, achievement and dropout rates. In the management practice of higher education institutions, on the basis of an operational forecast, measures are developed and implemented to support those students who fall into the risk group.
Our study is aimed at substantiating a model for predicting the early departure of students using an artificial neural network and analyzing predictors that increase the accuracy of predicting successful graduation from a Russian university. This work will expand the international practice of comparative research in higher education.
The paper confirms the already existing hypotheses about the influence of a number of factors on the prediction of academic performance and suggests the need to test their universality or specificity in a particular institution of higher education. We also proved that an artificial neural network model with a certain set of attributes can be applied in the context of a single higher education institution, regardless of specialization. To determine the potential risk group of students, a binary classification prediction model is used. The overall prediction accuracy of a neural network with combined data reaches 88%. For this neural network model, the basic predictors that affect the accuracy of the forecast are the cumulative average level of achievement (CGPA) and the year of admission to the university.
Keywords
educational analytics,
student dropout factors,
data mining,
artificial neural networks,
forecasting
Supporting agencies: The reported study was funded by RFBR, project number 19-29-14082.
About the Authors
F. M. Gafarov
Kazan (Volga region) Federal University
Russian Federation
Russian Federation
Fail M. Gafarov – Cand. Sci. (Physics and Mathematics), Assoc. Prof., Head of Department
35, Kremlyovskaya str., Kazan, 420000
Ya. B. Rudneva
Kazan (Volga region) Federal University
Russian Federation
Russian Federation
Yana B. Rudneva – Cand. Sci. (History), Senior Specialist of the Centre for Coordination of Educational Project
35, Kremlyovskaya str., Kazan, 420000
U. Yu. Sharifov
Kazan (Volga region) Federal University
Russian Federation
Russian Federation
Umar Yu. Sharifov – Graduate Student
35, Kremlyovskaya str., Kazan
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