Designing and Evaluating Emotional Student Models for Game-Based Learning

  • Karla MuñozEmail author
  • Paul Mc Kevitt
  • Tom Lunney
  • Julieta Noguez
  • Luis Neri


Research in game-based learning environments aims to recognise and show emotion. This chapter describes the main approaches and challenges involved in achieving these goals. In addition, we propose an emotional student model that can reason about students’ emotions using observable behaviour and responses to questions. Our model uses Control-Value Theory (Pekrun et al., The control value theory of achievement emotions. An integrative approach to emotions in education. In: Schutz, P.A., Pekrun, R. (eds.) Emotion in Education, pp. 13–36. Elsevier, London, 2007) as a basis for representing behaviour and was designed and evaluated using Probabilistic Relational Models (PRMs), Dynamic Bayesian Networks (DBNs) and Multinomial Logistic Regression. Olympia, a game-based learning architecture, was enhanced to incorporate affect and was used to develop PlayPhysics, an emotional game-based learning environment for teaching Physics. PlayPhysics’ design and emotional student model was evaluated with 79 students of Engineering at Tecnológico de Monterrey, Mexico City campus (ITESM-CCM). Results are presented and discussed. Future work will focus on conducting tests with a larger population of students, implementing additional game challenges and incorporating physiological signals to increase the accuracy of classification.


Learning Environment Observable Behaviour Student Model Educational Game Activity Emotion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We would like to convey our thanks to the anonymous reviewers of this paper and the editors of this book, Dr. Eunice Ma, Dr. Andreas Oikonomou and Prof. Lakhmi Jain. We thank the University of Ulster for its support with a Vice Chancellor’s Research Studentship (VCRS) and acknowledge recommendations from Prof. Mark Shevlin from the Psychology Research Institute and Dr. Girijesh Prasad and Dr. Abdul Satti from the Intelligent Systems Research Centre. Also, we would like to thank Dr. Deaglan Page and Dr. Donncha Hanna from the School of Psychology, Queen’s University Belfast for their advice in statistical methods. We recognise the technical support provided during the design, implementation and deployment of PlayPhysics by Dennis Heaney from Beep Blip Games and Gabriel Deak from the Intelligent Systems Research Centre. Additionally, we would like to express our gratitude to Richard Walsh from ZooCreative for modelling the player characters in PlayPhysics. We want to thank Peter Starostin for creating LowMax, the free rig for 3D Studio Max, which was adapted to be the learning companion M8 in PlayPhysics. Finally, we wish to acknowledge the assistance of the members of the E-learning Research Group at Tecnológico de Monterrey, Mexico City campus (ITESM-CCM), Víctor Robledo, Dr. Moises Alancastre, Dr. Lourdes Muñoz, M.Sc. Gerardo Aguilar, Gilberto Huesca and Benjamín Hernández in the evaluation of PlayPhysics.


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Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  • Karla Muñoz
    • 1
    Email author
  • Paul Mc Kevitt
    • 2
  • Tom Lunney
    • 3
  • Julieta Noguez
    • 4
  • Luis Neri
    • 5
  1. 1.Faculty of Computing and Engineering, School of Computing and Intelligent Systems, Intelligent Systems Research CentreUniversity of UlsterDerry/LondonderryUK
  2. 2.University of UlsterDerry/LondonderryUK
  3. 3.Faculty of Computing and Engineering, School of Computing and Intelligent SystemsUniversity of UlsterDerry/LondonderryUK
  4. 4.Computer Department, Engineering SchoolTecnológico de Monterrey (ITESM)Mexico CityMexico
  5. 5.Engineering SchoolTecnológico de Monterrey (ITESM)Mexico CityMexico

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