首页> 外文期刊>International Journal of Artificial Intelligence Tools: Architectures, Languages, Algorithms >AN ENSEMBLE OF CLASSIFIERS APPROACH TO USER MODELING ON ADAPTIVE LEARNING COMMUNITIES
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AN ENSEMBLE OF CLASSIFIERS APPROACH TO USER MODELING ON ADAPTIVE LEARNING COMMUNITIES

机译:适应性学习社区中用户建模的分类方法

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摘要

Nowadays, web-based distance learning is benefiting from improved communication services. However, the mere fact of setting up an environment for students and lecturers does not guarantee mutual collaboration or successful student learning. This is partly due to the fact that an unique response is given to every user with different background knowledge, different interests, or different skill levels in the use of the services provided. To resolve this situation, adaptive systems provide an adapted response to each user's needs based on a constructed user model containing his/her characteristics. In this paper we will see that the construction of this user model is not trivial. In a user model in a web-based collaborative environment, there are very diverse attributes related with the interaction of the user with the services and the value of these attributes are obtained in very different ways. In addition, as any adaptive system the user model constructed should be explicit and accesible by the user and tutor. We will also see how we dynamically manage the user model in an adaptive learning environment by means of a ensemble of classifiers.
机译:如今,基于Web的远程学习正受益于改进的通信服务。但是,仅为学生和讲师建立环境的事实并不能保证相互合作或成功的学生学习。这部分是由于以下事实:在使用所提供的服务时,会向具有不同背景知识,不同兴趣或不同技能水平的每个用户提供唯一的响应。为了解决这种情况,自适应系统基于包含他/她的特征的构建的用户模型来提供对每个用户需求的适应性响应。在本文中,我们将看到此用户模型的构建并非无关紧要。在基于Web的协作环境中的用户模型中,存在与用户与服务的交互相关的非常多种属性,并且这些属性的值以非常不同的方式获得。另外,作为任何自适应系统,所构建的用户模型应该是明确的,并且可由用户和辅导员访问。我们还将看到我们如何通过一组分类器在自适应学习环境中动态管理用户模型。

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