使用分层属性跟踪(SAT)图进行学习分析

Rwitajit Majumdar, Sridhar V. Iyer
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引用次数: 7

摘要

我们已经创建了一个称为分层属性跟踪(SAT)图的可视化表示,以解释在学习分析数据中隐含的趋势。SAT图是一个统一的图形,可以跟踪数据集中的单个属性值,并根据研究人员设置的标准对它们进行分层。SAT图表示样品在地层间跨属性的过渡。本文介绍了SAT图,并说明了如何生成、解释和分析SAT图。我们相信,SAT图表生成的过程将使我们能够在学习数据上探索更深入的研究问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using Stratified Attribute Tracking (SAT) Diagrams for Learning Analytics
We have created a visual representation called Stratified Attribute Tracking (SAT) Diagram to explicate trends that are otherwise implicit in learning analytics data. SAT Diagram is a unified graph that enables tracking individual attribute values in a dataset and stratifying them according to criteria set by the researcher. SAT diagram represents the transition of samples between strata across attributes. In this paper we introduce the SAT diagram and illustrate how to generate, interpret and analyze them. We believe the process of SAT diagram generation would enable exploring deeper research questions on learning data.
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