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Microblogging in a Classroom: Classifying Students' Relevant and Irrelevant Questions in a Microblogging-Supported Classroom

机译:课堂中的微博:在微博支持的教室中对学生的相关问题和无关问题进行分类

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摘要

Microblogging is a popular technology in social networking applications that lets users publish online short text messages (e.g., less than 200 characters) in real time via the web, SMS, instant messaging clients, etc. Microblogging can be an effective tool in the classroom and has lately gained notable interest from the education community. This paper proposes a novel application of text categorization for two types of microblogging questions asked in a classroom, namely relevant (i.e., questions that the teacher wants to address in the class) and irrelevant questions. Empirical results and analysis show that using personalization together with question text leads to better categorization accuracy than using question text alone. It is also beneficial to utilize the correlation between questions and available lecture materials as well as the correlation between questions asked in a lecture. Furthermore, empirical results also show that the elimination of stopwords leads to better correlation estimation between questions and leads to better categorization accuracy. On the other hand, incorporating students' votes on the questions does not improve categorization accuracy, although a similar feature has been shown to be effective in community question answering environments for assessing question quality.
机译:微博是社交网络应用程序中的一种流行技术,它使用户可以通过Web,SMS,即时消息客户端等实时发布在线短消息(例如,少于200个字符)。微博可以成为课堂和课堂上的有效工具。最近已经引起了教育界的极大关注。本文提出了一种文本分类的新颖应用,可用于在课堂上问两种类型的微博问题,即相关问题(即教师要在课堂上解决的问题)和不相关问题。实证结果和分析表明,与仅使用问题文本一起使用,个性化与问题文本一起使用会导致更好的分类准确性。利用问题和可用讲座材料之间的相关性以及讲座中提出的问题之间的相关性也是有益的。此外,经验结果还表明,停用词的消除导致问题之间更好的相关性估计,并导致更好的分类准确性。另一方面,尽管已显示出类似的功能在社区问答环境中可以有效地评估问题质量,但将学生对问题的投票纳入表决并不能提高分类的准确性。

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