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Reseach Article

Predicting Student's Performance in Education using Data Mining Techniques

by Sara Fatima, Salma Mahgoub
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 177 - Number 19
Year of Publication: 2019
Authors: Sara Fatima, Salma Mahgoub
10.5120/ijca2019919607

Sara Fatima, Salma Mahgoub . Predicting Student's Performance in Education using Data Mining Techniques. International Journal of Computer Applications. 177, 19 ( Nov 2019), 14-20. DOI=10.5120/ijca2019919607

@article{ 10.5120/ijca2019919607,
author = { Sara Fatima, Salma Mahgoub },
title = { Predicting Student's Performance in Education using Data Mining Techniques },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2019 },
volume = { 177 },
number = { 19 },
month = { Nov },
year = { 2019 },
issn = { 0975-8887 },
pages = { 14-20 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume177/number19/31007-2019919607/ },
doi = { 10.5120/ijca2019919607 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:46:20.322381+05:30
%A Sara Fatima
%A Salma Mahgoub
%T Predicting Student's Performance in Education using Data Mining Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 177
%N 19
%P 14-20
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this data world, where users spawn their digital footprint and generate a huge amount of unstructured data continuously with each activity, data mining techniques help in discovering interesting patterns, establishing relationships and unravel the problems through analysis, in different aspects of life. Educational data mining is a multidisciplinary research area, in which data from various educational organizations, is explored and made operational, for various facets concerned with the students, like predicting academic performance, analyse the learning pattern, solving e-learning issues, predict employability, visualize the critical courses affecting performance, investigate the reasons for student’s failure or drop out and thus make data-driven decisions to improve the institutions standards. This paper provides a brief overview of Data Mining tools and techniques, and its encroachment in the educational domain. It also proposes a simple framework using different variables which helps in predicting student’s academic success using two different algorithms: Decision Trees and Bayesian Network. Finally, a comparative analysis of accuracy is done. The results show that Bayesian Network outperforms the Decision Tress and gives better accuracy.

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Index Terms

Computer Science
Information Sciences

Keywords

EDM Decision Trees Higher Education (HE)