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Data Mining on Student Database to Improve Future Performance

by Kashish Kohli, Shiivong Birla
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 146 - Number 15
Year of Publication: 2016
Authors: Kashish Kohli, Shiivong Birla
10.5120/ijca2016910717

Kashish Kohli, Shiivong Birla . Data Mining on Student Database to Improve Future Performance. International Journal of Computer Applications. 146, 15 ( Jul 2016), 42-46. DOI=10.5120/ijca2016910717

@article{ 10.5120/ijca2016910717,
author = { Kashish Kohli, Shiivong Birla },
title = { Data Mining on Student Database to Improve Future Performance },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2016 },
volume = { 146 },
number = { 15 },
month = { Jul },
year = { 2016 },
issn = { 0975-8887 },
pages = { 42-46 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume146/number15/25495-2016910717/ },
doi = { 10.5120/ijca2016910717 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:50:36.565228+05:30
%A Kashish Kohli
%A Shiivong Birla
%T Data Mining on Student Database to Improve Future Performance
%J International Journal of Computer Applications
%@ 0975-8887
%V 146
%N 15
%P 42-46
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Data Mining refers to the process of extracting information from large sets of data. Its primary implication is finding relationships between different variables to extract meaningful information. In this paper, we apply Data Mining techniques to find and evaluate future results and factors which affect them. Following preprocessing of data, several data mining techniques have been applied namely association, classification and clustering. We present the result and analysis after each process.

References
  1. Minaei-Bidgoli B.; Kashy D.; Kortemeyer G.; and Punch W., 2003. Predicting student performance: an application of data mining methods with an educational web-based system. In Proc. of IEEE Frontiers in Education. Colorado, USA, 13–18.
  2. Turban E.; Sharda R.; Aronson J.; and King D.,2007. Business Intelligence, A Managerial Approach. Prentice-Hall.
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  8. Mining students data to analyze Learning Behacior: A Case Study ALAA EL-HALEES; ResearchatGate
  9. The State of Educational Data Mining in 2009: A Review and Future Visions; Ryan S.J.d. Baker, Kalina Yacef.
Index Terms

Computer Science
Information Sciences

Keywords

Data Mining Association Classification Clustering Education

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