Predictive modeling of student dropout indicators in educational data mining using improved decision tree

Subitha Sivakumar, Sivakumar Venkataraman, Rajalakshmi Selvaraj

    Research output: Contribution to journalArticle

    10 Citations (Scopus)

    Abstract

    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.

    Original languageEnglish
    Article number87032
    Pages (from-to)1-5
    Number of pages5
    JournalIndian Journal of Science and Technology
    Volume9
    Issue number4
    DOIs
    Publication statusPublished - 2016

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    Decision trees
    Data mining
    Students
    Statistical methods
    Entropy

    All Science Journal Classification (ASJC) codes

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    Cite this

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    title = "Predictive modeling of student dropout indicators in educational data mining using improved decision tree",
    abstract = "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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    Predictive modeling of student dropout indicators in educational data mining using improved decision tree. / Sivakumar, Subitha; Venkataraman, Sivakumar; Selvaraj, Rajalakshmi.

    In: Indian Journal of Science and Technology, Vol. 9, No. 4, 87032, 2016, p. 1-5.

    Research output: Contribution to journalArticle

    TY - JOUR

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    AU - Venkataraman, Sivakumar

    AU - Selvaraj, Rajalakshmi

    PY - 2016

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