491 Using Data Mining to Predict Success in Studying Vlado Simeunović 1 and Ljubiša Preradović 2 1 Faculty of Education in Bijeljina 2 Faculty of Architecture and Civil Engineering in Banja Luka Abstract This paper deals with the creation of a model for predicting the performance of students during their studies using data mining, as well as with the analysis of factors which affect the achieved level of success. The model that is created on the basis of students’ socio-demographic data, data on their behaviour, personality characteristics, attitudes towards learning and the entire teaching process organization tends to classify students into one of two categories of success. Performance is measured by students’ grade point average achieved over the period of studies. We tested three methods of data mining: logistic regression, decision trees and neural networks. We believe that the presented model would serve as a test for the creation of a broader base of updated data by using some of the information tools and that, based on this model, a number of attributes that would relatively reliably predict the performance in studying will be defined. Key words: backward stepwise analysis; CART algorithm; decision trees; logistic regression; neural networks. Introduction Future prediction (forecast) is the crown of every science. Education is of strategic importance for economic and social development, i.e. for the development of society founded on knowledge. In the process of European integration it is necessary to harmonize the education system with the criteria and recommendations of the European Union or other European organisations and processes, with particular attention being paid to the indicators of success of the education system, defined by Croatian Journal of Education Vol.16; 2/2014 pages: 491-523 Original research paper Paper submitted: 7 th December 2012 Paper accepted: 3 rd January 2013 1 More details at: http://europa.eu/legislation_summaries/education_training_youth/general_framework/index_en.htm