Increasing the Adaptivity of an Intelligent Tutoring System with Educational Data Mining: A System Overview

Authors

  • Igor Jugo Department of Informatics, University of Rijeka
  • Božidar Kovačić Department of Informatics, University of Rijeka
  • Vanja Slavuj Department of Informatics, University of Rijeka

DOI:

https://doi.org/10.3991/ijet.v11i03.5103

Keywords:

e-learning, intelligent tutoring systems, educational data mining, adaptive e-learning

Abstract


Intelligent Tutoring Systems (ITSs) are inherently adaptive e-learning systems usually created for teaching well-defined domains (e.g., mathematics). Their objective is to guide the student towards a predefined goal such as completing a lesson, task, or mastering a skill. Defining goals and guiding students is more complex in ill-defined domains where the expert defines the model of the knowledge domain or the students have freedom to follow their own path through it. In this paper we present an overview of our systems architecture that integrates the ITS with data mining tools and performs a number of educational data mining processes to increase the adaptivity and, consequently, the efficiency of the ITS.

Author Biographies

Igor Jugo, Department of Informatics, University of Rijeka

I. Jugo is a PhD student and a teaching and research assistant at Department of Informatics, University of Rijeka, Croatia (e-mail: ijugo@inf.uniri.hr).

Božidar Kovačić, Department of Informatics, University of Rijeka

B. Kovačić is an assistant professor at Department of Informatics, University of Rijeka, Croatia (e-mail: bkovacic@inf.uniri.hr).

Vanja Slavuj, Department of Informatics, University of Rijeka

V. Slavuj is a PhD student and a research assistant at Department of Informatics, University of Rijeka, Croatia (e-mail: vslavuj@inf.uniri.hr).

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Published

2016-03-30

How to Cite

Jugo, I., Kovačić, B., & Slavuj, V. (2016). Increasing the Adaptivity of an Intelligent Tutoring System with Educational Data Mining: A System Overview. International Journal of Emerging Technologies in Learning (iJET), 11(03), pp. 67–70. https://doi.org/10.3991/ijet.v11i03.5103

Issue

Section

Short Papers