Neuronske mreže i stabla odlučivanja za predviđanje uspješnosti studiranja
Neuron Networks and Trees of Decision-making for Prediction of Efficiency in Studies

Summary/Abstract: The paper is dealing with models for prediction of students efficiency with the help of neuron networks and decision-making classification trees and then with the analysis of factors that influence the efficiency of students. A created model, based on demographic data of students as well as their behaviour and attitudes toward learning, tries to classify student in one of the two efficiency categories. The efficiency is measured by the average of marks during studies. Various architectures of neuron networks have been trained and tested and the best model is obtained with the help of stratified perceptron network. The trees of decision-making offered a significantly better accuracy than neuron networks and we suggest their using due to their being a more precise method for the set of observed data. A sensitivity analysis of output variables on the input ones carried out with neuron networks refers to the fact that preliminary exams, attendance of exercises, importance of marks to students, and scholarships are among the most significant factors for the efficiency of students. The trees of decision-making separated the most significant variables: the time spent in learning, attendance of exercises and the sorts of materials from which students learn. Future researches, with the increased number of input variables and enlargement of the pattern and methodological expansion of other artificial intelligence techniques and statistical methods, would make possible to create more successful model to be the basis for building the support system of decision-making in university level education.

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