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Affective educational games and the evolving teaching experience

Munoz, K, McKevitt, P, Lunney, T, Noguez, J and Neri, L (2011) Affective educational games and the evolving teaching experience. In: Computer games as educational and management tools: uses and approaches. (Eds: Cruz-Cunha, MM, Costa Carvalho, VH and Almeida Tavares, PC), IGI Global, Hershey, PA, USA, pp. 206-228. ISBN 978-1-60960-569-8 [Book section]

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URL: http://www.igi-global.com/book/computer-games-educational-management-tools/46178

DOI: 10.4018/978-1-60960-569-8.ch013


Teaching methods must adapt to learners’ expectations. Computer game-based learning environments enable learning through experimentation and are inherently motivational. However, for identifying when learners achieve learning goals and providing suitable feedback, Intelligent Tutoring Systems must be used. Recognizing the learner’s affective state enables educational games to improve the learner’s experience or to distinguish relevant emotions. This chapter discusses the creation of an affective student model that infers the learner’s emotions from cognitive and motivational variables through observable behavior. The control-value theory of ‘achievement emotions’ provides a basis for this work. A Probabilistic Relational Models (PRMs) approach for affective student modeling, which is based on Dynamic Bayesian Networks, is discussed. The approach is tested through a prototyping study based on Wizard-of-Oz experiments and preliminary results are presented. The affective student model will be incorporated into PlayPhysics, an emotional game-based learning environment for teaching Physics.PRMs facilitate the design of student models with Bayesian Networks. The effectiveness of PlayPhysics will be evaluated by comparing the students’ learning gains and learning efficiencies.

Item Type:Book section
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Faculty of Arts
Faculty of Arts > School of Creative Arts and Technologies
Research Institutes and Groups:Computer Science Research Institute > Intelligent Systems Research Centre
Computer Science Research Institute
ID Code:21113
Deposited By: Professor Paul McKevitt
Deposited On:05 Mar 2012 16:17
Last Modified:17 Oct 2017 16:02

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