Eye-tracking for user modeling in exploratory learning environments: An empirical evaluation
Knowledge-Based Systems
Proceedings of the 13th international conference on Intelligent user interfaces
Developing a generalizable detector of when students game the system
User Modeling and User-Adapted Interaction
Adaptive Educational Games: Providing Non-invasive Personalised Learning Experiences
DIGITEL '08 Proceedings of the 2008 Second IEEE International Conference on Digital Game and Intelligent Toy Enhanced Learning
Serious Use of a Serious Game for Language Learning
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Investigating the Utility of Eye-Tracking Information on Affect and Reasoning for User Modeling
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
Evaluating Adaptive Feedback in an Educational Computer Game
IVA '09 Proceedings of the 9th International Conference on Intelligent Virtual Agents
Interacting with a gaze-aware virtual character
Proceedings of the 2010 workshop on Eye gaze in intelligent human machine interaction
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Personalization approaches in learning environments
UMAP'11 Proceedings of the 19th international conference on Advances in User Modeling
An analysis of attention to student --- adaptive hints in an educational game
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
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Prime Climb is an educational game that provides individualized support for learning number factorization skills. This support is delivered by a pedagogical agent in the form of hints based on a model of student learning. Previous studies with Prime Climb indicated that students may not always be paying attentions to the hints, even when they are justified. In this paper we discuss preliminary work on using eye tracking data on user attention patterns to better understand if and how students process the agent's personalized hints, with the long term goal of making hint delivery more effective.