A Dynamic Topic Model of Learning Analytics Research
A Dynamic Topic Model of Learning Analytics Research
复制标题
学习分析研究的动态主题模型
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
R. Klamma
中科院分区：
文献类型：
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作者：
M. Derntl;Nikou Günnemann;R. Klamma
关于学习分析和教育数据挖掘的研究自2008年第一次教育数据挖掘会议(EDM)以来发表，并在2011年成立学习分析和知识会议(LAK)后获得了势头。本文从视觉分析的角度对2008年至2012年LAK数据集中的主题动态进行了分析。使用概率、动态主题挖掘算法对数据集进行处理。为了使LAK研究人员和利益相关者能够对生成的主题模型进行探索和可视化分析，我们开发并部署了DVITA，这是一个用于动态主题模型的基于Web的浏览工具。本文基于LAK数据集中所有论文的主题模型，探索了数据挑战中关于LAK的过去、现在和未来的问题的答案。我们还简要描述了用户如何使用D-VITA自己探索LAK主题模型。
Research on learning analytics and educational data mining has been published since the rst conference on Educational Data Mining (EDM) in 2008 and gained momentum through the establishment of the Learning Analytics and Knowledge (LAK) conference in 2011. This paper addresses the LAK Data Challenge from the perspective of visual analytics of topic dynamics in the LAK Dataset between 2008 and 2012. The data set was processed using probabilistic, dynamic topic mining algorithms. To enable exploration and visual analysis of the resulting topic model by LAK researchers and stakeholders we developed and deployed DVITA, a web-based browsing tool for dynamic topic models. In this paper we explore answers to the questions about past, present, and future of LAK posed in the Data Challenge based on a topic model of all papers in the LAK Dataset. We also briey describe how users can explore the LAK topic model on their own using D-VITA.