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首页> 外文会议>International Conference on Computational Science and Its Applications(ICCSA 2006) pt.1; 20060508-11; Glasgow(GB) >A Diagnostic Model Using a Clustering Scheme
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A Diagnostic Model Using a Clustering Scheme

机译：使用聚类方案的诊断模型

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摘要

It has been recognized that it is a challenging problem to deal with the situation where learners have diverse computing backgrounds and the learning content to be covered is also in the broad coverage. In the case, it's required to devise a sophisticated diagnostic model for applying a proper teaching-learning method. We have drawn a scheme which can be applied to that case efficiently by using clustering algorithms based on web technology. In our approach, we focus on finding out methods for classifying both learners and learning content on the web. To make classification and manipulation of learning content ease, we reorganize learning content in order to have discrete form by introducing the concept of the knowledge unit which is extracted from each topic. Also, to make classification and diagnostic ease, we develop questions to measure them and analyze each question using item response theory (IRT) on the web. From the experiment of students sampled using our method, we show that learners with various backgrounds and the learning content with distribution on the broad range can be categorized effectively into the groups with homogeneous property. Also, we describe how to apply our proposed scheme to the introductory courses at postsecondary level.
机译：已经认识到，应对学习者具有不同计算背景并且所要涵盖的学习内容也在广泛范围内的情况是一个具有挑战性的问题。在这种情况下，需要设计一个复杂的诊断模型以应用适当的教学方法。通过使用基于Web技术的聚类算法，我们提出了一种可以有效地应用于该情况的方案。在我们的方法中，我们专注于找出对学习者和网络学习内容进行分类的方法。为了简化学习内容的分类和操作，我们通过引入从每个主题中提取的知识单元的概念来重组学习内容，以使其具有离散形式。另外，为了使分类和诊断变得容易，我们开发了问题以对其进行度量，并使用项目响应理论（IRT）在网络上分析每个问题。通过使用我们的方法对学生进行的实验，我们表明，具有不同背景的学习者以及分布广泛的学习内容可以有效地归类为具有同质性的组。另外，我们描述了如何将我们提出的方案应用于中学后的入门课程。

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