  In this article, we present our findings from ten years of research on
intelligent educational games. We discuss the architecture of our training
environments for learning spelling and mathematics, and specifically focus on
the representation of the content and the controller that enables personalized
trainings. We first show the multi-modal representation that reroutes
information through multiple perceptual cues and discuss the game structure. We
then present the data-driven student model that is used for a personalized,
adaptive presentation of the content. We further leverage machine learning for
analytics and visualization tools targeted at teachers and experts. A large
data set consisting of training sessions of more than 20,000 children allows
statistical interpretations and insights into the nature of learning.
