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 ) as a basis for representing behaviour and was designed and evaluated using Probabilistic Relational Models (PRMs), Dynamic Bayesian Networks

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... (DBNs) and Multinomial Logistic Regression. Olympia, a game-based learning architecture, was enhanced to incorporate affect and was used to develop PlayPhysics, an emotional gamebased learning environment for teaching Physics. PlayPhysics' design and emotional student model was evaluated with seventy nine 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. or an affective state and determine what emotions are relevant to the learning experience. In addition, it is important to know that personal preferences and differences influence the presence and communication of an emotional state (Conati and Maclaren 2009). Therefore, it is important to deal with the inherent uncertainty of the emotional domain. Also, in education, the effects of positive and negative emotions are not totally understood. It is also not always clear what should be the appropriate response to a student's emotion Lepper et al. 1993; Schutz and Pekrun 2007). Additionally, the creation and acceptance of educational games involves creative design and academic concerns, such as the difference between game content and curriculum content and resis-Clancey GL, Buchanan B (1982) Exploration of Teaching and Problem Solving Strategies '1979 -1982'.