文章基本信息

标题：MINIMIZING STUDENT ATTRITION IN HIGHER LEARNING INSTITUTIONS IN MALAYSIA USING SUPPORT VECTOR MACHINE
本地全文：下载
作者：ANBUSELVAN SANGODIAH ; PRASHANTH BELEYA ; MANORANJITHAM MUNIANDY 等
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2015
卷号：71
期号：3
出版社：Journal of Theoretical and Applied
摘要：Attrition or better known as student dismissal or drop out from completing courses in higher learning institutions is prevalent in higher learning institutions in Malaysia and abroad. There are several reasons attributed to the attrition in the context of student in higher learning institutions. The degree of attrition varies from one institution to another and it is cause for concern as there will be a lot of wastage of resources of academic and administrative besides the adverse effect on the social aspect. In view of this, minimizing the attrition rate is of paramount importance in institutions. There have been numerous non-technical approaches to address the issue, but they have not been effective to predict at early stage the likelihood of students dropping out from higher learning institutions. Technical approach such as data mining has been used in predicting student attrition by some researchers in their past research work. However, not all prediction data mining techniques and other relevant and significant factors attributed to student attrition have been fully explored to address the issue. As of result this, this study will focus on using support vector machine model to predict probation status of student in which in most cases will lead to student�s dismissal. It will also examine relevant and other factors that contribute to the attrition among students in Malaysia. The result of the study is appealing as the support vector machine model achieves a decent accuracy in prediction despite working on small size of data set. With all this in place, higher learning institutions in Malaysia can deploy the model in predicting probation status of student to minimize student attrition.
关键词：Attrition; Data Mining; Support Vector Machine; Classification Model; Educational Data Mining