Implementation of motivational tactics in tutoring systems
Journal of Artificial Intelligence in Education
BEAT: the Behavior Expression Animation Toolkit
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Probabilistic Student Modelling to Improve Exploratory Behaviour
User Modeling and User-Adapted Interaction
Affective Learning — A Manifesto
BT Technology Journal
User-centred design and evaluation of affective interfaces
From brows to trust
A BDI approach to infer student's emotions in an intelligent learning environment
Computers & Education
Automatic detection of learner's affect from conversational cues
User Modeling and User-Adapted Interaction
Modeling self-efficacy in intelligent tutoring systems: An inductive approach
User Modeling and User-Adapted Interaction
Developing a novel interface for capturing self reports of affect
CHI '08 Extended Abstracts on Human Factors in Computing Systems
Game Sound: An Introduction to the History, Theory, and Practice of Video Game Music and Sound Design
Empirically building and evaluating a probabilistic model of user affect
User Modeling and User-Adapted Interaction
Adding features of educational games for teaching physics
FIE'09 Proceedings of the 39th IEEE international conference on Frontiers in education conference
Dynamically sequencing an animated pedagogical agent
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Motivating the learner: an empirical evaluation
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Review: Student modeling approaches: A literature review for the last decade
Expert Systems with Applications: An International Journal
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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.