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Comparative Analysis of Selected Classifiers in Mining Students� Educational Data

by Ayinde A.q, E.o Omidiora, A.b Adetunji
Communications on Applied Electronics
Foundation of Computer Science (FCS), NY, USA
Volume 1 - Number 5
Year of Publication: 2015
Authors: Ayinde A.q, E.o Omidiora, A.b Adetunji
10.5120/cae-1533

Ayinde A.q, E.o Omidiora, A.b Adetunji . Comparative Analysis of Selected Classifiers in Mining Students� Educational Data. Communications on Applied Electronics. 1, 5 ( April 2015), 5-8. DOI=10.5120/cae-1533

@article{ 10.5120/cae-1533,
author = { Ayinde A.q, E.o Omidiora, A.b Adetunji },
title = { Comparative Analysis of Selected Classifiers in Mining Students� Educational Data },
journal = { Communications on Applied Electronics },
issue_date = { April 2015 },
volume = { 1 },
number = { 5 },
month = { April },
year = { 2015 },
issn = { 2394-4714 },
pages = { 5-8 },
numpages = {9},
url = { https://www.caeaccess.org/archives/volume1/number5/327-1533/ },
doi = { 10.5120/cae-1533 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-09-04T18:37:37.071568+05:30
%A Ayinde A.q
%A E.o Omidiora
%A A.b Adetunji
%T Comparative Analysis of Selected Classifiers in Mining Students� Educational Data
%J Communications on Applied Electronics
%@ 2394-4714
%V 1
%N 5
%P 5-8
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Educational data mining is concerned with developing methods that discover knowledge from educational databases. Many predictive classifiers have been applied in mining educational data with less emphasis on their performance evaluation in order to determine the most efficient. In this study, a comparative analysis of three predictive classifiers for mining educational data was conducted.

References
  1. Han and Kamber 2000. Data Mining Concepts and Techniques.
  2. Witten I. H. , Frank E. ,(2005) "Data Mining: Practical Machine Learning Tools and Techniques", 2nd Edition. San Francisco: Morgan Kaufmann, 2005.
  3. Daniel Larose (2005): Knowledge Discovery In Data. An Introduction to Data Mining. First Edition ISBN 0-471-66657-2 pg 5,2005
  4. Michalski. K and Michalski. R (2004). Educational Data Mining and Reporting: Analyzing student Data in Order To Improve Educational Process. In L. Cantoni and C. McLoughlin(Eds). Proceedings of World Conference on Educational Multimedia,Hypermedia and Telecommunications 2004(pp. 1088 1094) Chesapeake,VA: AAC
  5. Han and Kamber (2006):Data Mining Concept and Techniques. 2nd Edition ISBN 1-55860-901-6
  6. Nguyen Thai Nghe, P. Janecek, and P. Haddawy (2007)"A comparative analysis of techniques for predicting academic performance", ASEE/IEEE Frontiers in Education Conference,pp. T2G7-T2G12, 2007.
  7. Kotsiantis . S ,Pierrakeas . C, and Pintelas. P (2004), "Prediction of Student's Performance in Distance Learning Using Machine Learning Techniques", Applied Artificial Intelligence, Vol. 18, No. 5, 2004, pp. 411-426
  8. B. Minaei-Bidgoli, G. Kortemeyer, and W. F. Punch, (2004) "Enhancing Online Learning Performance: An Application of Data Mining Method", In proceedings of The 7th IASTED International Conference on Computers and Advanced Technology in Education (CATE2004), Kauai, Hawaii, USA, pp. 173-8, August 2004.
  9. W. Zang, and F. Lin,(2003) "Investigation of web-based teaching and learning by boostingalgorithms". In Proceedings of IEEE International Conference on Information Technology: Research and Education (ITRE 2003), pp. 445–449, 2003.
Index Terms

Computer Science
Information Sciences

Keywords

Comparative Analysis Selected Classifiers Instance Based Learning Lazy Classifier.