| Summary: | Electronic educational games integrate the target domain knowledge in a game-like
environment in order to help students learn. While, in general, these educational games
are more engaging than the traditional computer-based educational software, they often
do not necessarily trigger learning. One explanation is that many students play the
games without actively reasoning about the underlying domain knowledge. To make
learning more effective in educational games, we are designing intelligent pedagogical
agents that can provide tailored interventions to students. However, in order not to
compromise the high level of engagement that is the main advantage of educational
games, it is important for these agents to consider students' emotional states in addition
to their cognitive states (such as learning) to decide when and how to provide
interventions.
This thesis focuses on the creation of an affective student model that assesses the
students' emotional states while they are playing an educational game Prime Climb. The
affective student model explicitly represents the cognitive appraisal process of emotions
by implementing the cognitive theory of emotions (OCC Model). It relies on Dynamic
Decision Networks (DDNs) to deal with the high level of uncertainty involved in
affective user modeling.
The initial version of the model was built based on the observation of two
preliminary Prime Climb user studies, our intuitions, and psychological findings. This
model was then revised based on the results of a third Prime Climb study.
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