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Using Data Mining to Predic Success in Studying

Vlado Simeunovic ; Faculty of Education in Bijeljina
Ljubiša Preradović ; Faculty of Architecture and Civil Engineering in Banja Luka

Puni tekst: hrvatski pdf 472 Kb

str. 491-523

preuzimanja: 551


Puni tekst: engleski pdf 472 Kb

str. 491-523

preuzimanja: 433



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.

Ključne riječi

backward stepwise analysis; CART algorithm; decision trees; logistic regression; neural networks

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Podaci na drugim jezicima: hrvatski

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