Visualizing learning management system data using context-relevant self-organizingmap
Visualizing learning management system data using context-relevant self-organizingmap
复制标题
使用上下文相关的自组织图可视化学习管理系统数据
DOI：
10.1109/smc.2014.6974469
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
Pitoyo Hartono and Kayo Ogawa
中科院分区：
文献类型：
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作者：
Miwa YAMAMOTO ,Yasuko MAEKAWA, Tomoharu NAKASHIMA;Kiyoko TOKUNAGA, Noriko ADACHI;Yoko MIYOSHI;小川賀代;小川賀代，ピトヨ ハルトノ;Pitoyo Hartono and Kayo Ogawa
在过去几年中，许多教育机构引入了多种形式的学习管理系统（LMS），其主要目标是从积累的学习数据中获取有意义的信息，然后用于提高这些机构的教育质量。提取信息最流行的技术之一是可视化表征信息的高维数据。在本研究中，我们建议利用上下文相关自组织图，这是一种独特的可视化算法，不仅保留高维数据的地形特征，还保留其上下文，用于可视化 LMS 数据。我们对现实世界 LMS 数据的初步实验表明，上下文相关自组织图能够提供传统自组织图无法提供的视觉信息。
In the last few years, many form of Learning Management Systems (LMS) have been introduced in many educational institutions with the main objective of obtaining meaningful information from the accumulated learning data to be then utilized for increasing the quality of the educations in those institutions. One of the most popular techniques for extracting information is by visualizing the high dimensional data that characterize the information. In this study, we propose to utilize Context-Relevant Self Organizing Map, a unique visualization algorithm that preserves not only the topographical characteristics of high dimensional data but also their context, for visualizing LMS data. Our preliminary experiments with real world LMS data show that the Context-Relevant Self-Organizing map is able to provide visual information which cannot be provided by the conventional Self-Organizing Map.