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iSAT：一款供教师使用的视觉学习分析工具。

iSAT: a visual learning analytics tool for instructors.

作者信息

Majumdar Rwitajit, Iyer Sridhar

机构信息

1Inter-disciplinary Program in Educational Technology, Indian Institute of Technology Bombay, Powai, Mumbai, 400076 India.

2Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, 400076 India.

出版信息

Res Pract Technol Enhanc Learn. 2016;11(1):16. doi: 10.1186/s41039-016-0043-3. Epub 2016 Sep 1.

DOI:10.1186/s41039-016-0043-3
PMID:30613249
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6302866/
Abstract

Interactive Stratified Attribute Tracking (iSAT) is a visual analytics tool for cohort analysis. In this paper, we show how instructors can use iSAT to visualize transitions of groups of students during teaching-learning activities. Interactive visual analytics gives the instructor the affordance of understanding the dynamics of the class of students and their activities from the data collected in their own teaching-learning context. We take an example of a peer instruction (PI) activity and describe how iSAT can be used to analyze its clicker responses. During PI, typically instructors only use histograms to visualize the distribution of clicker responses in the pre- and post-discussion phases. We show that the use of iSAT to analyze clicker data in real time to trace transitions of participants' responses during various voting phases can support them in planning for their post-PI activities. Seven patterns of transitions that emerge are , , , , , , and . We interpret them in the context of the example. Such transition patterns are neither available in multiple histograms of individual voting phase nor generated in real time to be visualized as a flow diagram. We had conducted two workshops to introduce iSAT to the instructors and demonstrated the workflow of using iSAT with their dataset. Here, we report usefulness and usability data collected from those workshops. In conclusion, we highlight the power of iSAT for instructors to do cohort analysis in their teaching-learning practice.

摘要

交互式分层属性跟踪（iSAT）是一种用于队列分析的可视化分析工具。在本文中，我们展示了教师如何使用iSAT来可视化教学活动中学生群体的转变。交互式可视化分析使教师能够从他们自己的教学环境中收集的数据来理解学生班级及其活动的动态。我们以同伴教学（PI）活动为例，描述了如何使用iSAT来分析其课堂反馈器的回答。在同伴教学期间，教师通常只使用直方图来可视化讨论前和讨论后阶段课堂反馈器回答的分布情况。我们表明，使用iSAT实时分析课堂反馈器数据以跟踪各个投票阶段参与者回答的转变，可以帮助他们规划同伴教学后的活动。出现的七种转变模式是 ， ， ， ， ， 和 。我们结合示例对它们进行解释。这种转变模式在单个投票阶段的多个直方图中既不可用，也不会实时生成以可视化为流程图。我们举办了两次研讨会，向教师介绍iSAT，并展示了使用iSAT处理他们数据集的工作流程。在此，我们报告从这些研讨会收集的有用性和可用性数据。总之，我们强调了iSAT在教师教学实践中进行队列分析的强大功能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04d4/6302866/9c17da1d0512/41039_2016_43_Fig16_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04d4/6302866/98e492212ec0/41039_2016_43_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04d4/6302866/d9abbed52756/41039_2016_43_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04d4/6302866/3c68649ac6b7/41039_2016_43_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04d4/6302866/6f8ff1e8da33/41039_2016_43_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04d4/6302866/6461927392e1/41039_2016_43_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04d4/6302866/ab4568f551f9/41039_2016_43_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04d4/6302866/955acbe50e07/41039_2016_43_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04d4/6302866/a7988f5cbe14/41039_2016_43_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04d4/6302866/4a2bc09ae7a1/41039_2016_43_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04d4/6302866/dd7a4a0be1c5/41039_2016_43_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04d4/6302866/6f5cef995593/41039_2016_43_Fig11_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04d4/6302866/fd29fb2bd62f/41039_2016_43_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04d4/6302866/9605816076a9/41039_2016_43_Fig14_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04d4/6302866/0074b0487916/41039_2016_43_Fig15_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04d4/6302866/9c17da1d0512/41039_2016_43_Fig16_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04d4/6302866/98e492212ec0/41039_2016_43_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04d4/6302866/d9abbed52756/41039_2016_43_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04d4/6302866/3c68649ac6b7/41039_2016_43_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04d4/6302866/6f8ff1e8da33/41039_2016_43_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04d4/6302866/6461927392e1/41039_2016_43_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04d4/6302866/ab4568f551f9/41039_2016_43_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04d4/6302866/955acbe50e07/41039_2016_43_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04d4/6302866/a7988f5cbe14/41039_2016_43_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04d4/6302866/4a2bc09ae7a1/41039_2016_43_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04d4/6302866/dd7a4a0be1c5/41039_2016_43_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04d4/6302866/6f5cef995593/41039_2016_43_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04d4/6302866/4b38c6691bee/41039_2016_43_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04d4/6302866/fd29fb2bd62f/41039_2016_43_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04d4/6302866/9605816076a9/41039_2016_43_Fig14_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04d4/6302866/0074b0487916/41039_2016_43_Fig15_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04d4/6302866/9c17da1d0512/41039_2016_43_Fig16_HTML.jpg

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