Comparative Analysis of Models for Student Performance with Data Mining Tools

Comparative Analysis of Models for Student Performance with Data Mining Tools

  IJCTT-book-cover
 		 International Journal of Computer Trends and Technology (IJCTT)          
 
© 2017 by IJCTT Journal
Volume-46 Number-1
Year of Publication : 2017
Authors : A. K. Shrivas, Pragya Tiwari
DOI :  10.14445/22312803/IJCTT-V46P109

MLA

A. K. Shrivas, Pragya Tiwari "Comparative Analysis of Models for Student Performance with Data Mining Tools". International Journal of Computer Trends and Technology (IJCTT) V46(1):42-46, April 2017. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
In modern time the analysis of student performance is very challenging task for every educational institutions. The main reason behind that rapid growth of population and increasing number of schools and colleges claiming that they can give their students quality education and provide the best environment for quality learning and many other aspects through which they can increase the performance capabilities in each and every student. There are different researcher have worked in the field of analysis of student performance, but they have not achieved satisfactory result. In this research work, we have used various data mining techniques for analyzing of student performance using WEKA , Rapid Miner, Tanagra and Orange data mining tools in case of both Portuguese and Mathematics Dataset . Random forest gives best accuracy as 93.52% in Weka data mining tool while 73.65% of accuracy in Tanagra data mining tool in binary and multiclass problem respectively with Portuguese data set. Similarly, in case of Mathematics dataset, Radom forest achieved 92.40% of accuracy in Weka data mining tool while 74.43% of accuracy in Orange data mining tool with binary and multiclass problem respectively. Finally, Random forest is robust model for classification of student performance.

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Keywords
Data Mining, Classification, Student Performance.