Predictive Modeling in Higher Education: Determining Factors of Academic Performance
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...
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| Format: | Article |
| Language: | English |
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Moscow Polytechnic University
2023-01-01
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| Series: | Высшее образование в России |
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| Online Access: | https://vovr.elpub.ru/jour/article/view/4166 |
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| author | F. M. Gafarov Ya. B. Rudneva U. Yu. Sharifov |
| author_facet | F. M. Gafarov Ya. B. Rudneva U. Yu. Sharifov |
| author_sort | F. M. Gafarov |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-73c822d73ee84537870bdb9acdd1ad14 |
| institution | Kabale University |
| issn | 0869-3617 2072-0459 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Moscow Polytechnic University |
| record_format | Article |
| series | Высшее образование в России |
| spelling | doaj-art-73c822d73ee84537870bdb9acdd1ad142025-02-01T13:14:31ZengMoscow Polytechnic UniversityВысшее образование в России0869-36172072-04592023-01-01321517010.31992/0869-3617-2023-32-1-51-702345Predictive Modeling in Higher Education: Determining Factors of Academic PerformanceF. M. Gafarov0Ya. B. Rudneva1U. Yu. Sharifov2Kazan (Volga region) Federal UniversityKazan (Volga region) Federal UniversityKazan (Volga region) Federal UniversityFor 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.https://vovr.elpub.ru/jour/article/view/4166educational analyticsstudent dropout factorsdata miningartificial neural networksforecasting |
| spellingShingle | F. M. Gafarov Ya. B. Rudneva U. Yu. Sharifov Predictive Modeling in Higher Education: Determining Factors of Academic Performance Высшее образование в России educational analytics student dropout factors data mining artificial neural networks forecasting |
| title | Predictive Modeling in Higher Education: Determining Factors of Academic Performance |
| title_full | Predictive Modeling in Higher Education: Determining Factors of Academic Performance |
| title_fullStr | Predictive Modeling in Higher Education: Determining Factors of Academic Performance |
| title_full_unstemmed | Predictive Modeling in Higher Education: Determining Factors of Academic Performance |
| title_short | Predictive Modeling in Higher Education: Determining Factors of Academic Performance |
| title_sort | predictive modeling in higher education determining factors of academic performance |
| topic | educational analytics student dropout factors data mining artificial neural networks forecasting |
| url | https://vovr.elpub.ru/jour/article/view/4166 |
| work_keys_str_mv | AT fmgafarov predictivemodelinginhighereducationdeterminingfactorsofacademicperformance AT yabrudneva predictivemodelinginhighereducationdeterminingfactorsofacademicperformance AT uyusharifov predictivemodelinginhighereducationdeterminingfactorsofacademicperformance |