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A Framework for the Initialization of Student Models in Web-based Intelligent Tutoring Systems

机译：基于Web的智能辅导系统中的学生模型初始化的框架

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摘要

Initializing a student model for individualized tutoring in educational applications is a difficult task, since very little is known about a new student. On the other hand, fast and efficient initialization of the student model is necessary. Otherwise the tutoring system may lose its credibility in the first interactions with the student. In this paper we describe a framework for the initialization of student models in Web-based educational applications. The framework is called ISM. The basic idea of ISM is to set initial values for all aspects of student models using an innovative combination of stereotypes and the distance weighted k-nearest neighbor algorithm. In particular, a student is first assigned to a stereotype category concerning her/ his knowledge level of the domain being taught. Then, the model of the new student is initialized by applying the distance weighted k-nearest neighbor algorithm among the students that belong to the same stereotype category with the new student. ISM has been applied in a language learning system, which has been used as a test-bed. The quality of the student models created using ISM has been evaluated in an experiment involving classroom students and their teachers. The results from this experiment showed that the initialization of student models was improved using the ISM framework.
机译：初始化学生模型以在教育应用中进行个性化补习是一项艰巨的任务，因为对新学生知之甚少。另一方面，学生模型的快速有效初始化是必要的。否则，辅导系统可能会在与学生的第一次互动中失去其信誉。在本文中，我们描述了一个用于在基于Web的教育应用程序中初始化学生模型的框架。该框架称为ISM。 ISM的基本思想是使用原型和距离加权k最近邻算法的创新组合为学生模型的各个方面设置初始值。特别是，首先将一个学生分配给一个关于他/他对所教授领域的知识水平的刻板印象类别。然后，通过在与新学生属于相同定型类别的学生中应用距离加权k最近邻算法来初始化新学生的模型。 ISM已被应用在语言学习系统中，该系统已被用作测试平台。使用ISM创建的学生模型的质量已经在涉及课堂学生及其老师的实验中进行了评估。该实验的结果表明，使用ISM框架可以改进学生模型的初始化。

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