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The Analysis of Student Performance using Data Mining

Santoso, Leo Willyanto and Yulia, (2018) The Analysis of Student Performance using Data Mining. In: International Conference on Computer, Communication and Computational Sciences, 21-10-2018 - 21-10-2018, Bangkok - Thailand.

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    Abstract

    This paper presents the study of data mining in the education industry to model the performance for students enrolled in university. Two algorithms of data mining were used. Firstly, a descriptive task based on the K-means algorithm was utilized to select several student clusters. Secondly, a classification task supported two classification techniques, known as Decision Tree and Naïve Bayes, to predict the dropout because of poor performance in a students first four semesters. The student academic data collected during the admission process of those students were used to train and test the models, which were assessed using a cross-validation technique. Experimental results show that the prediction of drop out student is improved, student performance is monitored when the data from the previous academic enrollment are added.

    Item Type: Conference or Workshop Item (Paper)
    Uncontrolled Keywords: Data mining, education, drop out, student performance
    Subjects: Q Science > QA Mathematics > QA76 Computer software
    Divisions: Faculty of Industrial Technology > Informatics Engineering Department
    Depositing User: Admin
    Date Deposited: 19 Jan 2019 01:06
    Last Modified: 26 Jan 2019 19:10
    URI: http://repository.petra.ac.id/id/eprint/18101

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