Computational intelligence: a logical approach
Computational intelligence: a logical approach
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Designing CIspace: pedagogy and usability in a learning environment for AI
ITiCSE '05 Proceedings of the 10th annual SIGCSE conference on Innovation and technology in computer science education
You can lead a horse to water: how students really use pedagogical software
ITiCSE '05 Proceedings of the 10th annual SIGCSE conference on Innovation and technology in computer science education
Engagement tracing: using response times to model student disengagement
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
Building a stochastic dynamic model of application use
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Enhancing Tutoring Intelligence Using Knowledge Discovery Techniques
WI-IATW '06 Proceedings of the 2006 IEEE/WIC/ACM international conference on Web Intelligence and Intelligent Agent Technology
Unsupervised and supervised machine learning in user modeling for intelligent learning environments
Proceedings of the 12th international conference on Intelligent user interfaces
Modeling and understanding students' off-task behavior in intelligent tutoring systems
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Problem-Solving Knowledge Mining from Users' Actions in an Intelligent Tutoring System
CAI '07 Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Activity sequence modelling and dynamic clustering for personalized e-learning
User Modeling and User-Adapted Interaction
Plan Recognition and Visualization in Exploratory Learning Environments
ACM Transactions on Interactive Intelligent Systems (TiiS)
Visualizing expert solutions in exploratory learning environments using plan recognition
Proceedings of the 19th international conference on Intelligent User Interfaces
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In this paper, we present the application of unsupervised learning techniques to automatically recognize behaviors that may be detrimental to learning during interaction with an Exploratory Learning Environment (ELE). First, we describe how we use the k-means clustering algorithm for off-line identification of learner groups with distinguishing interaction patterns who also show similar learning improvements with an ELE. We then discuss how a k-means on-line classifier, trained with the learner groups detected off-line, can be used for adaptive support in ELEs. We aim to show the value of a data-based approach for recognizing learners as an alternative to knowledge-based approaches that tend to be complex and time-consuming even for domain experts, especially in highly unstructured ELEs.