ISSN(Online): 2320-9801 ISSN (Print): 2320-9798 International Journal of Innovative Research in Computer and Communication Engineering (An ISO 3297: 2007 Certified Organization) Vol. 2, Issue 12, December 2014 10.15680/ijircce.2014.0212015 Copyright to IJIRCCE www.ijircce.com 7232 A Decision Support System for Predicting Student Performance Lalit Dole 1 , Jayant Rajurkar 2 Assistant Professor, Dept. of CSE, G.H.Raisoni College of Engineering, Nagpur (M.S), India 1 . Dept. of CSE, G.H.Raisoni College of Engineering, Nagpur (M.S), India 2 . ABSTRACT: In recent years data mining has been successfully implemented in the business world. Evaluating students' academic success is becoming increasingly challenging, its use is intended for identification and extraction of new and potentially valuable knowledge from the data. Predicting educational outcome is a practical alternative heterogeneous environment. Performance prediction models can be built by applying data mining techniques to enrolment data. In this paper we present an Naive Bayes algorithm (NB) approach to predict graduating cumulative Grade Point Average based on applicant data collected from the surveys conducted during the summer semester at the University of Tuzla, the Faculty of Economics, academic year 2010-2011, among first year students and the data taken during the enrolment. The Naive Bayes algorithm is used to discover the most suited way to predict student's success. KEYWORDS: Data Mining, Classification, Prediction, Naive Bayes algorithm (NB), Student Evaluation. I. INTRODUCTION Many leading higher education and Technical Education institutions aim is to contribute to the improvement of quality of higher education, the success of creation of human capital is the subject of a continuous analysis[1]. Therefore, the prediction of students' success is essential for higher education and Technical education institutions, because the quality of teaching process is the ability to meet students' needs. In this sense important data and information are gathered on a regular basis, and they are considered at the appropriate authorities, and standards in order to maintain the quality are set. All participants in the educational process could benefit by applying data mining on the data from the higher education system decibel in figure1. Computational data process from different Perspectives represents from data mining with the goal of extracting implicit and interesting samples , trends and information from the data, it can greatly help every participant in the educational process in order to improve the understanding of the teaching process, and it centres on discovering, detecting and explaining educational phenomenon’s [1]. . Most Researchers suggests academic performance [3, 4] using student outcome as a good basis to assess applicants’ qualifications. A performance prediction model can be built by applying data mining to available admission and graduation grade point average data. Fortunately, AIT has a large database of information on past and current applicants. [2]. Decision support systems have been built to help advisors instruct students in choosing suitable courses and appropriate study plans [5, 6]. Previous work on student performance prediction used logistic regression to examine the impact of various factors on student performance [5]. Bekele and Menzel [7] used Bayesian networks to predict mathematics performance of high school students. Their model categorized students into three categories: below satisfactory, satisfactory, and above satisfactory. The work reported in the present paper differs from theirs in the highly international nature of the applicant pool and the more fine grained prediction [2].