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Research on Multifeature-Based Superposter Identification in Online Learning Forums

机译:在线学习论坛中基于多因偶的超光识别研究

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摘要

With the development of online learning and distance education, online learners’ discussions in forums become increasingly effective to facilitate learning. Superposters, who play a more and more important role in forums, have attracted researchers’ close attention. The key to the research is how to identify superposters among a large number of participants. Some studies focus on the network interaction of superposters and some content-related features but neglect the basic quality like language expression that a superposter should possess and the learning-related features like learning collaboration. Based on the analysis of online learning corpus, through network interaction and combination of the different features of N-gram, the paper proposed the superposter identification method based on the three primary features including language expression (L), content quality (C), and social network interaction (S) and the eight secondary features including learning collaboration. The paper applied the method in the real online learning forum corpus for identifying 28 preset superposters, achieving the results of , , , and . Experiments showed that this was an effective superposter identification method in online learning forums.
机译:随着在线学习和远程教育的发展,在线学习者在论坛中的讨论变得越来越有效,以便于学习。在论坛中发挥越来越重要的角色的超级职位吸引了研究人员的密切关注。研究的关键是如何在大量参与者中识别超级职位。一些研究侧重于超级职位和一些与内容相关的特征的网络互动,而是忽视了超级卫生服务器应该拥有的语言表达等基本质量,以及与学习协作等学习相关的功能。基于在线学习语料库的分析,通过网络交互和组合N-GRAM的不同特征,提出了基于包括语言表达式(L),内容质量(C)的三个主要特征的超级选择识别方法,社交网络互动和八个二级特征,包括学习协作。本文应用了真实在线学习论坛语料库中的方法,用于识别28个预设的超级职位,实现结果,,和。实验表明,这是在线学习论坛中的有效的超级选择识别方法。

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