Discovering and recognizing student interaction patterns in exploratory learning environments

In a Exploratory Learning Environment users acquire knowledge while freely experiencing the environment. In this setting, it is often hard to identify actions or behaviors as correct or faulty, making it hard to provide adaptive support to students who do not learn well with these environments. In this paper we discuss an approach that uses Class Association Rule mining and a Class Association Rule Classifier to identify relevant interaction patterns and build student models for online classification. We apply the approach to generate a student model for an ELE for AI algorithms and present preliminary results on its effectiveness.