Learner Modeling in Academic Networks

机译：学术网络中的学习者建模

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摘要

Learning analytics (LA) deals with the development of methods that harness educational data sets to support the learning process. To achieve particular learner entered LA objectives such as intelligent feedback, adaptation, personalization, or recommendation, learner modeling is a crucial task. Learner modeling enables to achieve adaptive and personalized learning environments, which are able to take into account the heterogeneous needs of learners and provide them with tailored learning experience suited for their unique needs. In this paper, we focus on learner modeling in academic networks. We present theoretical, design, implementation, and evaluation details of PALM, a service for personal academic learner modeling. The primary aim of PALM is to harness the distributed publication information to build an academic learner model.

机译：学习分析（LA）处理利用教育数据集支持学习过程的方法的开发。为了实现特定的学习者进入LA目标，例如智能反馈，适应，个性化或推荐，学习者建模是一个至关重要的任务。学习者建模可以实现自适应和个性化的学习环境，能够考虑学习者的异构需求，并为他们提供适合其独特需求的量身定制的学习体验。在本文中，我们专注于学习网络中的学习者建模。我们展示了Palm的理论，设计，实施和评估细节，为个人学习者建模提供服务。 Palm的主要目标是利用分布式出版信息来构建学习学习者模型。

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来源
《IEEE International Conference on Advanced Learning Technologies》|2014年||共5页
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Chatti Mohamed Amine; Dugoija Darko; Thus Hendrik; Schroeder Ulrik;
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中图分类计算技术、计算机技术;
关键词
Abstracts; Adaptation models; Analytical models; Arrays; Data mining; Data models; Social network services; adaptation; interest mining; learner modeling; learning analytics; personalization;

机译：摘要;自适应模型;分析模型;阵列;数据挖掘;数据模型;社会网络服务;适应;兴趣挖掘;学习型;个性化;

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Learner Modeling in Academic Networks

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