Abstract

To ensure learning, game-based learning environments must incorporate assessment mechanisms, e.g. Intelligent Tutoring Systems (ITSs). ITSs are focused on recognising and influencing the learner’s emotional or motivational states. This research focuses on designing and implementing an affective student model for intelligent gaming, which reasons about the learner’s emotional state from cognitive and motivational variables using observable behaviour. A Probabilistic Relational Models (PRMs) approach is employed to derive Dynamic Bayesian Networks (DBNs). The model uses the Control-Value theory of ‘achievement emotions’ as a basis. A preliminary test was conducted to recognise the students’ prospective-outcome emotions with results presented and discussed. PlayPhysics is an emotional games learning environment for teaching Physics. Once the affective student model proves effective it will be incorporated into PlayPhysics’ architecture. The design, evaluation and post evaluation of PlayPhysics are also discussed. Future work will focus on evaluating the affective student model with a larger population of students, and on providing affective feedback.