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• W2555133631 abstract "In recent years, open-ended interactive educational tools such as games have been gained popularity due to their ability to make learning more enjoyable and engaging. Modeling and predicting individual behavior in such interactive environments is crucial to better understand the learning process and improve the tools in the future. A model-based approach is a standard way to learn student behavior in highly-structured systems such as intelligent tutors. However, dening such a model relies on expert domain knowledge. The same approach is often extremely dicult in educational games because open-ended nature of these systems creates an enormous space of actions. To ease this burden, we propose a data-driven approach to learn individual behavior given a user’s interaction history. This model does not heavily rely on expert domain knowledge. We use our framework to predict player movements in two educational puzzle games, demonstrating that our behavior model performs signicantly better than a baseline on both games. This indicates that our framework can generalize without requiring extensive expert knowledge specic to each domain. Finally, we show that the learned model can give new insights into understanding player behavior." @default.
• W2555133631 created "2016-11-30" @default.
• W2555133631 creator A5002408938 @default.
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• W2555133631 date "2014-07-04" @default.
• W2555133631 modified "2023-09-24" @default.
• W2555133631 title "Learning Individual Behavior in an Educational Game: A Data-Driven Approach." @default.
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