Data Mining в образовании: прогнозирование успеваемости учащихся
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Научный журнал Моделирование, оптимизация и информационные технологииThe scientific journal Modeling, Optimization and Information Technology
Online media
issn 2310-6018

Data Mining in education: predicting student performance

idPopova N.A. idEgorova E.S.

UDC 004.89+004.65
DOI: 10.26102/2310-6018/2023.41.2.003

  • Abstract
  • List of references
  • About authors

The ability to predict student academic performance is valuable to any institution seeking to improve student achievement and motivation. Based on the predictions generated, students identified as being at risk for expulsion or failure can be supported in a more timely manner. This article discusses various classification models for predicting student performance using data collected from universities in Penza. The data include student enrollment data as well as activity data from the university electronic information and education environment (EIE). An important contribution of this study is the consideration for student heterogeneity in the construction of predictive models. This is based on the observation that students with different socio-demographic characteristics or modes of learning may exhibit different motivation to learn. Experiments confirmed the hypothesis that models trained using instances in student subgroups outperform models built using all data instances. In addition, the experiments showed that accounting for both enrollment and learning activity patterns helped to identify vulnerable students more accurately. Experimental results have demonstrated that no single method has superior performance in all aspects. The homegrown analytics platform Loginom was employed as a tool to create a predictive model.

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Popova Nataliya Aleksandrovna
Candidate of Technical Sciences


Penza State University

Penza, The Russian Federation

Egorova Ekaterina Sergeevna
Candidate of Economic Sciences


Penza State Technological University

Penza, The Russian Federation

Keywords: data Mining, intellectual analysis of educational data, forecasting of student progress, heterogeneity of students, electronic information and educational environment

For citation: Popova N.A. Egorova E.S. Data Mining in education: predicting student performance. Modeling, Optimization and Information Technology. 2023;11(2). Available from: DOI: 10.26102/2310-6018/2023.41.2.003 (In Russ).


Full text in PDF

Received 02.03.2023

Revised 03.04.2023

Accepted 18.04.2023

Published 18.04.2023