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Initializing the Tutor Model Using K-Means Algorithm

机译:使用K均值算法初始化导师模型

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This paper proposes an approach for the initialization and the construction of tutor's model in the e-learning systems. This actor has several roles and different tasks from a system to another. His main purpose is tracking and guiding students throughout their learning process. In their first interaction, the system has rather little information about its new tutors. The proposed approach serves to offer much information for each specific tutor based on the models of other similar tutors. The problem of initializing the tutor model can be resolved by assigning the tutor to certain group of tutors. Thus, a data mining algorithm, namely k-means is responsible for creating clusters based on the pre-entered information on tutors. Then, each new tutor is assigned to his closest cluster center. This model facilitates the assignment of tutors to learners for adapting the monitoring process.
机译:本文提出了一种在电子学习系统中初始化和构建教师模型的方法。这个角色有几个角色,从一个系统到另一个角色都有不同的任务。他的主要目的是在整个学习过程中跟踪和指导学生。在他们的第一次互动中,系统几乎没有有关其新导师的信息。所提出的方法用于基于其他类似导师的模型为每个特定导师提供大量信息。可以通过将教师分配给特定的一组教师来解决初始化教师模型的问题。因此,一种数据挖掘算法,即k-means,负责基于预先输入的关于导师的信息来创建集群。然后,将每个新导师分配到他最近的群集中心。该模型有助于将导师分配给学习者,以适应监控过程。

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