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Predicting and Analysis of Students’ Academic Performance using Data Mining Techniques

by Reda M. Ahmed, Nahla F. Omran, Abdelmgeid A. Ali
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 182 - Number 32
Year of Publication: 2018
Authors: Reda M. Ahmed, Nahla F. Omran, Abdelmgeid A. Ali
10.5120/ijca2018918250

Reda M. Ahmed, Nahla F. Omran, Abdelmgeid A. Ali . Predicting and Analysis of Students’ Academic Performance using Data Mining Techniques. International Journal of Computer Applications. 182, 32 ( Dec 2018), 1-6. DOI=10.5120/ijca2018918250

@article{ 10.5120/ijca2018918250,
author = { Reda M. Ahmed, Nahla F. Omran, Abdelmgeid A. Ali },
title = { Predicting and Analysis of Students’ Academic Performance using Data Mining Techniques },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2018 },
volume = { 182 },
number = { 32 },
month = { Dec },
year = { 2018 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume182/number32/30231-2018918250/ },
doi = { 10.5120/ijca2018918250 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:13:03.367101+05:30
%A Reda M. Ahmed
%A Nahla F. Omran
%A Abdelmgeid A. Ali
%T Predicting and Analysis of Students’ Academic Performance using Data Mining Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 182
%N 32
%P 1-6
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The educational database holds on the massive amount of data and it is increasing rapidly. Data mining provides effective techniques for discovering useful knowledge and pattern from students’ data. The discovered patterns can be used to understand many problems in the educational field. This paper proposes a framework to predict the achievement of first-year bachelor’s students in computer science course. Decision Tree, Na¨ive Bayes, and Multi-Layer Perceptron classification methods are applied to the students’ data using the WEKA Data Mining tool to produce the best prediction model of the students’ academic performance. Experiments conducted to detect the best model among the used techniques then the models’ accuracy is computed. The extracted knowledge from the prediction model will be utilized to recognize and profile the student to decide the students’ level of success in the first semester.

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Index Terms

Computer Science
Information Sciences

Keywords

Educational Data Mining Decision Tree Na¨ive Bayes Multi-Layer Perceptron Prediction students’ academic performance

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