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DOI: 10.14569/IJACSA.2020.0110949
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A Predictive Model for the Determination of Academic Performance in Private Higher Education Institutions

Author 1: Francis Makombe
Author 2: Manoj Lall

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 9, 2020.

  • Abstract and Keywords
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Abstract: The growth and development of predictive models in the current world has influenced considerable changes. Today, predictive modelling of academic performance has transformed more than a few institutions by improving their students' academic performance. This paper presents a computational predictive model using artificial neural networks to predict whether a student will pass or fail. The model is unique in the current literature as it is specifically designed to evaluate the effectiveness of the predictive strategies on neural networks as well as on five additional algorithms. The analysis of the experimental results shows that Artificial Neural Networks outperformed the eXtremeGBoost, Linear Regression, Support Vector Machine, Naive Bayes, and Random Forest algorithms for academic performance prediction.

Keywords: Classification modelling; data mining; higher education institutions; accuracy; academic performance

Francis Makombe and Manoj Lall, “A Predictive Model for the Determination of Academic Performance in Private Higher Education Institutions” International Journal of Advanced Computer Science and Applications(IJACSA), 11(9), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110949

@article{Makombe2020,
title = {A Predictive Model for the Determination of Academic Performance in Private Higher Education Institutions},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110949},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110949},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {9},
author = {Francis Makombe and Manoj Lall}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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