Track: Modeling and Simulation

Abstract

Machine learning provides various algorithms for application in different domains. In the educational field, huge volume of students’ data is generated and machine learning algorithms serve as valuable tool for pattern identification in students’ behavior. In this paper, CRISP DM standard for data mining is applied in the research with decision tree algorithm used for modelling on Croatian dataset to develop predictive models for students’ outcomes prediction. Data set consisted of 264 students of largest Croatian university collected by online survey. The results prove that decision tree modelling achieves superior results in terms of high accuracy and reliability together with interpretability of tree structure and obtained rules in prediction of students’ academic performance. This approach shows promise to be used in student success prediction in the universities in an automatic manner.  Such model can be used to: (i) improve students' learning and develop personalized recommender systems for optimal learning paths, (ii) emphasize to professors most important determinants of students’ academic success (iii) help management of higher education institutions to facilitate the provision of detailed student learning and adjust institutions strategies, (iv) automate adaptation of the course modules and faculty programs.