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An enhanced personal learning environment using social semantic web technologies

机译:使用社交语义网技术的增强型个人学习环境

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Compared with learning in classrooms, classical e-learning systems are less adaptive and once a system that supports a particular strategy has been designed and implemented, it is less likely to change according to student's interactions and preferences. Remote educational systems should be developed to ensure as much as necessary the personalization of learning tasks. New e-learning environments should appear to act as intelligent systems that better fit the needs of their users and especially students according to their interests, preferences, motivations, objectives and knowledge. In this paper, we present how the personalization of students' learning process leveraging the use of social semantic web, using resource description framework models, ontologies, social networking and collaborative tagging. Our aim is to develop an approach of personalization according to students' preferences, interests and knowledge by defining for them the best learning paths, this means, provide them as recommendations the best collaborators and the relevant resources that better fit their needs. We present a new method of recommendation based on users' similarity calculation. We demonstrate the effectiveness of our approach through the design, implementation, analysis and evaluation of a social learning environment.
机译:与课堂学习相比,经典的在线学习系统适应性较差,并且一旦设计并实施了支持特定策略的系统,就不太可能根据学生的互动和喜好进行更改。应该开发远程教育系统,以确保尽可能多地个性化学习任务。新的电子学习环境应该看起来像智能系统,可以根据用户的兴趣,偏好,动机,目标和知识来更好地满足其用户(尤其是学生)的需求。在本文中,我们介绍了如何利用资源描述框架模型,本体,社交网络和协作标记来利用社交语义网来个性化学生的学习过程。我们的目标是通过为学生定义最佳的学习途径,根据他们的喜好,兴趣和知识来开发个性化方法,这意味着向他们提供最佳的合作者和更适合他们需求的相关资源作为建议。我们提出了一种基于用户相似度计算的推荐新方法。我们通过设计,实施,分析和评估社会学习环境来证明我们的方法的有效性。

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