TY - JOUR
T1 - Predictive modeling of student dropout indicators in educational data mining using improved decision tree
AU - Sivakumar, Subitha
AU - Venkataraman, Sivakumar
AU - Selvaraj, Rajalakshmi
PY - 2016
Y1 - 2016
N2 - Background/Objectives: Educational Data mining is an emerging interdisciplinary research area that deals with the development of methods to explore data originating in an educational context. The objective of this work is to identify relevant attributes from socio-demographic, academic and institutional data from undergraduate students at the university located in India and develop an improved decision tree algorithm based on ID3 which can able to predict whether the students continue or drop their studies. Methods/Statistical Analysis: The traditional ID3 algorithm is improved by using Renyi entropy, Information gain and Association Function and the model generated by improved decision tree algorithm may be beneficial for university administrators to create guidelines and policies related to raise the enrollment rate in university and to take precautionary and advisory measures and thereby reduce student dropout. It can also used to find the reasons and relevant factors that affect the dropout students. Findings: Experimental results proved that improved decision tree algorithm provides better prediction accuracy in educational data than that of traditional classification algorithms in the literature. Improvements/Applications: Improved decision algorithm was proposed that enhances the ability to form decision trees and thereby to prove that the classification accuracy of improved decision algorithm on educational dataset is greater.
AB - Background/Objectives: Educational Data mining is an emerging interdisciplinary research area that deals with the development of methods to explore data originating in an educational context. The objective of this work is to identify relevant attributes from socio-demographic, academic and institutional data from undergraduate students at the university located in India and develop an improved decision tree algorithm based on ID3 which can able to predict whether the students continue or drop their studies. Methods/Statistical Analysis: The traditional ID3 algorithm is improved by using Renyi entropy, Information gain and Association Function and the model generated by improved decision tree algorithm may be beneficial for university administrators to create guidelines and policies related to raise the enrollment rate in university and to take precautionary and advisory measures and thereby reduce student dropout. It can also used to find the reasons and relevant factors that affect the dropout students. Findings: Experimental results proved that improved decision tree algorithm provides better prediction accuracy in educational data than that of traditional classification algorithms in the literature. Improvements/Applications: Improved decision algorithm was proposed that enhances the ability to form decision trees and thereby to prove that the classification accuracy of improved decision algorithm on educational dataset is greater.
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U2 - 10.17485/ijst/2016/v9i4/87032
DO - 10.17485/ijst/2016/v9i4/87032
M3 - Article
AN - SCOPUS:84958182144
SN - 0974-6846
VL - 9
SP - 1
EP - 5
JO - Indian Journal of Science and Technology
JF - Indian Journal of Science and Technology
IS - 4
M1 - 87032
ER -