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Prediction of Graduate Students for Master Degree based on Their Past Performance using Decision Tree in Weka Environment
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 74 - Number 11 |
| Year of Publication: 2013 |
| Authors: Jaimin N. Undavia, Prashant M. Dolia, Nikhil P. Shah |
10.5120/12930-9877
|
Jaimin N. Undavia, Prashant M. Dolia, Nikhil P. Shah . Prediction of Graduate Students for Master Degree based on Their Past Performance using Decision Tree in Weka Environment. International Journal of Computer Applications. 74, 11 ( July 2013), 23-29. DOI=10.5120/12930-9877
Abstract
For generating comprehensive and precise analysis, Decision Tree technique is found as most adequate technique. Usually decision trees are used in data mining to study historical data and on the basis of the data analysis and its rules, one can predict the result. Most of the higher education institutions are suffering from low percentage of result, placement and interest of the students. To address this issue, we have suggested one Decision Support System using decision tree which predicts the post graduation stream for the students on the basis of their past academic performance. Prediction of students' performance is a great concern to the higher education institutions. So, this paper covers all the parameters which have some influence in student's performance. In this investigation, a survey cum experimental methodology is adopted to generate the data store. Paper also discusses use of decision tree for the prediction. Decision tree algorithms are applied on Post Graduate students who are either pursuing or have completed. Academic history and social data are collected and used to design the model. This model is used for the prediction of students' performance.
References
- R. R. Kabra, R. S. Cichkar – "Performance Prediction of Engineering Students using Decision Tree", International Journal of Computer Applications(0975-8887), Vol. 36 – No. 11, December 2011.
- V. Ramesh, P. Parkavi, K. Ramar – " Predicting Student Performance: A Statistical and Data Mining Approach", International Journal of Computer Applications (0975-8887), Vol. 63 – No. *, February 2013.
- Vo Thi Ngoc Chau & Nguyen Hua Phung, " A knowledge-Driven Educational Decision Support System" , 978-1-4673-0309-5/12/$31. 00©2012 IEEE.
- Chen Z. , "Computational Intelligence for Decision Support", CRC Press LLC: 2000.
- Vasile Paul Bresfelean – " Analysis and Predictions on Student's Behavior Using Decision Tree in WekaEnvironment", Prodeeding of the ITI 2007 29th International Conference on Information Technology Interfaces, June 25-28, 2001, Cavtat, Croatia.
- 6. Naeimeh Delavari, Dr. Mohammad Reza Beikzadeh & Dr. Somnuk Phon-Amnuaisuk – " Application of Enhanced Analysis Model for Data Mining Process in Higher Education System", ITHET 6th International Conference, 2005.
- M. Ramaswami and R. Bhaskaran, "A CHAID Based Performance Prediction Model in Educational Data Mining", International Journal of Computer Science Issues Vol. 7, Issue 1, No. 1, January 2010.
- Surjeet Kumar Yadav & Saurabh Pal – "Data Mining: A Prediction for Performance Improvements of Engineering Students using Classification", Worlds of Computer Science and Information Technology Journal (2221-0741), Vol. 2, No. 2,51-56, 2012.
- Kobus Ehlers, Malan Joubert, Johann Kinghorn & Arnold van Zyl - " A Decision Support System for Institutional Research Management in Higher Education", 2009 International Conference on Computational Science & Engineering, IEEE Computer Society.
- Behrouz Minaei-Bidgoli, DeborahA. Kashy, Gerd Kortemeyer & William F. Punch – "PREDICTING STUDENT PERFORMANCE: AN APPLICATION OF DATA MINIG METHODS WITH AN EDUCATIONAL WEB-BASED SYSTEM" , 33'd ASEEIIEEE Frontiers in Education Conference T2A-13, November 5 – 8, 2003.
- J. Han and M. Kamber, "Data Mining: Concepts and Techniques," Morgan Kaufmann, 2000.
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