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
  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.

References
[1] J. Ruby & K. David, “A study model on the impact of various indicators in the performance of students in higher education”, International Journal of Research in Engineering and Technology, Vol. 3, Issue 5, pp. 750-755, 2014.
[2] J. Ruby and K. David, “Analysis of Influencing Factors in Predicting Students Performance Using MLP – A Comparative Study “,International Journal of Research in Engineering and Technology, Vol. 3, Issue 2 , pp.1085-1092, 2015.
[3] Kumar S. Anupama and M. N Vijayalakshmi, “Efficiency of Decision Trees in Predicting Students Academic Performance”, Computer Science & Information Technology , Vol. 02, .pp. 335–343, 2011.
[4] M. Ali, “Role of Data Mining in Education Sector”, International Journal of Computer Science and Mobile Computing Vol.2 Issue. 4, pg. 374-383, 2013.
[5] O. F. Noah and B. Barida, “Evaluation of Student Performance Using Data Mining Over a Given Data Space”, International Journal of Recent Technology and Engineering (IJRTE), Volume-2, Issue-4, pp.101-104, 2013.
[6] J. Rowley, “Is higher education ready for knowledge management”, International Journal of Educational Management, vol. 14(7), pp. 325–333, 2000.
[7] E. A. Hanushek and M. E. Raymond,” Does School Accountability Lead to Improved Student Performance”, pp.1-52, 2004.
[8] B.K. Baradwaj and S. Pal , “ Mining Educational Data to Analyze Students Performance” IJACSA, Vol.2, No.6, pp.63-69, 2011.
[9] Z. J. Kovacic , “Early prediction of student success: Mining student enrolment data”, Proceedings of Informing Science & IT Education Conference 2010.
[10] M. Ramaswami, and R. Bhaskaran, “CHAID Based Performance Prediction Model in Educational Data Mining“, International Journal of Computer Science Issues, Vol. 7, Issue 1, No. 1, pp.10-18, 2010.
[11] M. Pandey and V. K. Sharma,”A Decision Tree Algorithm Pertaining to the Student Performance Analysis and Prediction”, International Journal of Computer Applications , Volume 61– No.13, pp.1-5, 2013.
[12] J. Han and M Kamber, “Data mining concepts and techniques”, San Francisco, USA, Morgan Kaufmann, 2001.
[13] M. O. Mansur, M. Sap and M. Noor, ”Outlier Detection Technique in Data Mining: A Research Perspective‟ , In Postgraduate Annual Research Seminar, pp.23-31, 2005.
[14] P. Cortez and A. Silva, “Using Data Mining to Predict Secondary School Student Performance”. In A. Brito and J. Teixeira Eds., Proceedings of 5th FUture BUsiness TEChnology Conference 2008, pp. 5-12, Porto, Portugal, 2008, EUROSIS, ISBN 978-9077381-39-7.
[15] J. Ruby & K. David, “Predicting the Performance of Students in Higher Education Using Data Mining Classification Algorithms - A Case Study “, International Journal for Research in Applied Science & Engineering Technology, Volume 2 Issue XI, November pp.80-84, 2014.
[16] S. K. Gupta, S. Gupta & R. Vijay,” Prediction Of Student Success That Are Going To Enroll In The Higher Technical Education”. International Journal of Computer Science Engineering and Information Technology Research (IJCSEITR), Vol. 3, Issue 1, pp.95-108, 2013
[17] A. TEKIN,” Early Prediction of Students’ Grade Point Averages at Graduation: A Data Mining Approach”. Eurasian Journal of Educational Research, Issue 54, pp.207-226, 2014.
[18] Q. Al.Radaideh, E. Al-Shawakfa and M. Al-Najjar,”Mining Student Data Using Decision Trees‟ , The 2006 International Arab Conference on Information Technology – Conference Proceedings. Pp.1-5, 2006
[19] E. Chandra, and K. Nandhini, “Knowledge Mining from Student Data‟ , European Journal of Scientific Research, vol. 47, no. 1, pp. 156-163, 2010.
[20] V. Kumar, and A. Chadha, “An Empirical Study of the Applications of Data Mining Techniques in Higher Education‟ , International Journal of Advanced Computer Science and Applications, vol. 2, no. 3, pp. 80-84. 2011.
[21] M.S. Mythili, Dr. A.R.Mohamed Shanavas. “An Analysis of students’ performance using classification algorithms” IOSR Journal of Computer Engineering (IOSR-JCE), Issue 1, Ver. III , pp. 63-69,2014
[22] Komal S. Sahedani, B Supriya Reddy ,” A Review: Mining Educational Data to Forecast Failure of Engineering Students “. International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 12, pp.628-635, 2013
[23] M. Bray, The shadow education system: private tutoring and its implications for planners, 2nd ed. UNESCO, PARIS, France, 2007.
[24] Svetlana, S. Aksenova, Machine Learning with WEKA WEKA Explorer Tutorial for WEKA Version 3.4.3, 2004.
[25] http://eric.univ-lyon2.fr/~ricco/tanagra/index.html. (Browing date: dec:2016)

Keywords
Data Mining, Classification, Student Performance.