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Predicting Academic Success from Student Enrolment Data using Decision Tree Technique

M Narayana Swamy, M. Hanumanthappa Published in Artificial Intelligence

International Journal of Applied Information Systems
Year of Publication 2012
© 2010 by IJAIS Journal
10.5120/ijais12-450654
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  1. Narayana M Swamy and M Hanumanthappa. Article: Predicting Academic Success from Student Enrolment Data using Decision Tree Technique. International Journal of Applied Information Systems 4(3):1-6, September 2012. BibTeX

    @article{key:article,
    	author = "M Narayana Swamy and M. Hanumanthappa",
    	title = "Article: Predicting Academic Success from Student Enrolment Data using Decision Tree Technique",
    	journal = "International Journal of Applied Information Systems",
    	year = 2012,
    	volume = 4,
    	number = 3,
    	pages = "1-6",
    	month = "September",
    	note = "Published by Foundation of Computer Science, New York, USA"
    }
    

Abstract

The Indian education system has witnessed significant expansion in recent years, both in terms of the number of institutions as well as the student enrollment. There is a massive growth in self financed higher educational institutions in India in the next two decades. This causes a competition among institutions while attracting the student to get admission in these institutions. Therefore, institutions focused on the strength of students not on the quality of student at the time of enrollment. After the enrollment the institution tries to improve the quality of the student. Like other domain educational domain also produce huge amount of data. To improve the quality of education the data analysis plays an important role for decision support. The data mining is used to extract hidden information from large data set/data warehouse. In this paper we present the data mining technique to predict the performance of the students based on the enrollment data. It helps the teacher to take remedial measure for slow learners to improve the performance in the university examination.

Reference

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Keywords

Educational Data mining, Classification, Decision Tree, Higher Education