An Innovative Model for Adaptive Learning Utilizing Biofeedback and Item Response Theory

  • László Gazdi Department of Automation and Applied Informatics, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, H-1117 Budapest, Magyar Tudósok krt. 2., Hungary
  • Krisztián Pomázi Department of Automation and Applied Informatics, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, H-1117 Budapest, Magyar Tudósok krt. 2., Hungary
  • Máté Szabó Department of Automation and Applied Informatics, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, H-1117 Budapest, Magyar Tudósok krt. 2., Hungary
  • Bertalan Forstner Department of Automation and Applied Informatics, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, H-1117 Budapest, Magyar Tudósok krt. 2., Hungary

Abstract

Measuring and providing efficiency of educational applications is a serious, open problem, which impacts the future of this expanding industry greatly. Reaching player engagement is a complex challenge, as it also depends on the given task and the mental state of the player. Researches answer this by using adaptive educational games. To reach the goal, however, knowledge about more parameters is required about the game tasks, the abilities of the player, his actual physiological state and performance as well. In this paper we present our results, which use a biofeedback based adaptive algorithm, and based on this, an innovative psychometric model to take a step towards maximizing user engagement.

Keywords: biofeedback, cognitive abilities, item response theory, multiple intelligence, classification
Published online
2018-06-08
How to Cite
Gazdi, L., Pomázi, K., Szabó, M. and Forstner, B. (2018) “An Innovative Model for Adaptive Learning Utilizing Biofeedback and Item Response Theory”, Periodica Polytechnica Electrical Engineering and Computer Science, 62(3), pp. 90-105. doi: https://doi.org/10.3311/PPee.12213.
Section
Articles