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Erschienen in:
2015 | OriginalPaper | Buchkapitel
Comparing Representations for Learner Models in Interactive Simulations
verfasst von : Cristina Conati, Lauren Fratamico, Samad Kardan, Ido Roll
Erschienen in: Artificial Intelligence in Education
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Providing adaptive support in Exploratory Learning Environments is necessary but challenging due to the unstructured nature of interactions. This is especially the case for complex simulations such as the DC Circuit Construction Kit used in this work. To deal with this complexity, we evaluate alternative representations that capture different levels of detail in student interactions. Our results show that these representations can be effectively used in the user modeling framework proposed in [2], including behavior discovery and user classification, for student assessment and providing real-time support. We discuss trade-offs between high and low levels of detail in the tested interaction representations in terms of their ability to evaluate learning and inform feedback.