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Reseach Article

A Decision Tree Algorithm Pertaining to the Student Performance Analysis and Prediction

by Mrinal Pandey, Vivek Kumar Sharma
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 61 - Number 13
Year of Publication: 2013
Authors: Mrinal Pandey, Vivek Kumar Sharma
10.5120/9985-4822

Mrinal Pandey, Vivek Kumar Sharma . A Decision Tree Algorithm Pertaining to the Student Performance Analysis and Prediction. International Journal of Computer Applications. 61, 13 ( January 2013), 1-5. DOI=10.5120/9985-4822

@article{ 10.5120/9985-4822,
author = { Mrinal Pandey, Vivek Kumar Sharma },
title = { A Decision Tree Algorithm Pertaining to the Student Performance Analysis and Prediction },
journal = { International Journal of Computer Applications },
issue_date = { January 2013 },
volume = { 61 },
number = { 13 },
month = { January },
year = { 2013 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume61/number13/9985-4822/ },
doi = { 10.5120/9985-4822 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:08:59.055490+05:30
%A Mrinal Pandey
%A Vivek Kumar Sharma
%T A Decision Tree Algorithm Pertaining to the Student Performance Analysis and Prediction
%J International Journal of Computer Applications
%@ 0975-8887
%V 61
%N 13
%P 1-5
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Growth of an educational institute can be measured in terms of successful students of the institute. The analysis related to the prediction of students academic performance in higher education seems an essential requirement for the improvement in quality education. Data mining techniques play an important role in data analysis. For the construction of a classification model which could predict performance of students, particularly for engineering branches, a decision tree algorithm associated with the data mining techniques have been used in the research. A number of factors may affect the performance of students. Here some significant factors have been considered while constructing the decision tree for classifying students according to their attributes (grades). In this paper four different decision tree algorithms J48, NBtree, Reptree and Simple cart were compared and J48 decision tree algorithm is found to be the best suitable algorithm for model construction. Cross validation method and percentage split method were used to evaluate the efficiency of the different algorithms. The traditional KDD process has been used as a methodology. The WEKA (Waikato Environment for Knowledge Analysis) tool was used for analysis and prediction. . Results obtained in the present study may be helpful for identifying the weak students so that management could take appropriate actions, and success rate of students could be increased sufficiently.

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Index Terms

Computer Science
Information Sciences

Keywords

Data mining Decision tree Classification Prediction Classifiers Cross validation