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Comparing Representations for Learner Models in Interactive Simulations

机译:在交互式仿真中比较学习者模型的表示形式

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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, 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.
机译:在探索性学习环境中提供适应性支持是必要的,但由于交互的非结构化性质而具有挑战性。对于复杂的仿真,例如在本文中使用的DC电路构造套件,尤其如此。为了处理这种复杂性,我们评估了替代表示形式,这些表示形式可以捕获学生互动中不同级别的细节。我们的结果表明,这些表示可以有效地用于提出的用户建模框架中,包括行为发现和用户分类,以用于学生评估和提供实时支持。我们讨论了在测试的交互表示形式的高细节级别和低细节级别之间的权衡取舍,即它们评估学习和提供反馈的能力。

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