Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Eye-tracking to model and adapt to user meta-cognition in intelligent learning environments
Proceedings of the 11th international conference on Intelligent user interfaces
A review of associative classification mining
The Knowledge Engineering Review
Pedagogy and usability in interactive algorithm visualizations: Designing and evaluating CIspace
Interacting with Computers
User Modeling and User-Adapted Interaction
Adapting to when students game an intelligent tutoring system
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Using learning analytics to assess students' behavior in open-ended programming tasks
Proceedings of the 1st International Conference on Learning Analytics and Knowledge
Improving construct validity yields better models of systematic inquiry, even with less information
UMAP'12 Proceedings of the 20th international conference on User Modeling, Adaptation, and Personalization
User Modeling and User-Adapted Interaction
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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.