Suitability analysis of graph visualization algorithms for personalized study planning
Suitability analysis of graph visualization algorithms for personalized study planning
复制标题
图可视化算法对个性化学习计划的适用性分析
DOI：
10.1109/icstcc.2016.7790705
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Antons Mislevics
中科院分区：
文献类型：
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作者：
Raita Rollande;J. Grundspeņķis;Antons Mislevics
在之前的论文中，作者已经介绍了个性化学习规划框架，并实现了学习规划系统（SPS）原型，它允许创建个性化学习计划，然后规划课程学习，按所需顺序设置课程，并进行结构分析，从而检测图结构中最重要的节点。在本文中，作者描述了八种常见图形可视化算法的应用 - Tree、Circular、EfficientSugiyama Fruchterman-Reingold - FR、BoundedFR、ISOM、Kamada - Kawai - KK； LinLog - 用于在个性化学习计划系统中表示学习计划和课程结构。
In previous papers the present authors have covered personalized study planning framework, and also have implemented study planning system (SPS) prototype, which allows to create a personalized study program, and then to plan the course learning, setting the courses in the required sequence, and make structure analysis thus detecting the most significant nodes in the graph structure. In this paper authors describe the application of eight common graph visualization algorithms - Tree, Circular, EfficientSugiyama Fruchterman-Reingold - FR, BoundedFR, ISOM, Kamada - Kawai - KK; LinLog - for representing study plans and course structures in the personalized study planning system.