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IJSTR >> Volume 10 - Issue 11, November 2021 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

ANN-based Model For predicting Academic Success In A Private Educational Institution

[Full Text]



Francis Makombe, Manoj Lall



Accuracy; Academic performance; Artificial Neural Networks; Classification modelling; Data mining; Higher education institutions; Predictive model.



The pressure to improve on success rates is greater on private HEIs than on public funded HEIs, as their main source of funding is from fees collected from students. Poor success rates will undoubtedly affect private HEIs’ reputation and funding. To minimise the impact of a poor success rate amongst students, it is important to be able to identify students at risk of failing at an early stage, so that a more targeted remedial action could be taken. Private institutions apply various strategies such as making provision for extra tuition, extended laboratory access and establishing learning communities. From the discussion presented here, it is apparent that the timely identification of students at risk of failing a particular programme is of significant importance to both the students and the institutions they are registered with. In this article, artificial neural networks, extreme gradient boost, logistic regression, support vector machine, naive Bayes, and random forest are used for the classification of students. A dataset of 3 000 students were collected from a private higher education provider. It was observed that artificial neural networks produced the best performing model with an accuracy of 88.07%.



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