Towards an index of opportunity: understanding changes in mental workload during task execution
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Eye-tracking for user modeling in exploratory learning environments: An empirical evaluation
Knowledge-Based Systems
Pedagogy and usability in interactive algorithm visualizations: Designing and evaluating CIspace
Interacting with Computers
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
User-adaptive explanatory program visualization: evaluation and insights from eye movements
User Modeling and User-Adapted Interaction
Inferring word relevance from eye-movements of readers
Proceedings of the 16th international conference on Intelligent user interfaces
Activity recognition using eye-gaze movements and traditional interactions
Interacting with Computers
Visual attention for solving multiple-choice science problem: An eye-tracking analysis
Computers & Education
Comparing information graphics: a critical look at eye tracking
Proceedings of the 3rd BELIV'10 Workshop: BEyond time and errors: novel evaLuation methods for Information Visualization
Cognitive Systems Research
Data mining for adding adaptive interventions to exploratory and open-ended environments
UMAP'12 Proceedings of the 20th international conference on User Modeling, Adaptation, and Personalization
Proceedings of the 2013 international conference on Intelligent user interfaces
Proceedings of the 19th international conference on Intelligent User Interfaces
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This paper explores the value of eye-tracking data to assess user learning with interactive simulations (IS). Our long-term goal is to use this data in user models that can generate adaptive support for students who do not learn well with these types of unstructured learning environments. We collected gaze data from users interacting with the CSP applet, an IS for constraint satisfaction problems. Two classifiers built upon this data achieved good accuracy in discriminating between students who learn well from the CSP applet and students who do not, providing evidence that gaze data can be a valuable source of information for building user modes for IS.