Automatic data integration from Moodle course logs to pivot tables for time series cross section analysis
Automatic data integration from Moodle course logs to pivot tables for time series cross section analysis
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DOI：
10.1016/j.procs.2017.08.222
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发表时间：
2017-09
期刊：
影响因子：
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通讯作者：
Konomu Dobashi
中科院分区：
文献类型：
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作者：
Konomu Dobashi
本文描述了一种Moodle课程日志和数据透视表功能的数据集成方法，用于分析使用Moodle课程材料的面对面混合学习中学生的材料页面浏览行为。开发的方法通过预处理Moodle课程日志将数据与数据透视表集成在一起，并生成一个时间序列横截面（TSCS）表，该表可以可视化学生的课程材料页面视图。在Moodle页面上进行的实验发现，在实际的课程中收集的实际材料的表可视化的整体和个人的观点。在课堂上对教师关于课程材料的指示的反应也可以通过生成的TSCS表来可视化。而且，由于可以清楚地识别晚打开课程项目或不打开课程项目的学生，因此该方法可以作为改进未来课程的参考。
This paper describes a data integration method for Moodle course logs and pivot table functions to analyze the behavior of students’ material page views in face-to-face blended learning using Moodle course materials. The developed method integrates the data with a pivot table by preprocessing Moodle course logs and generates a time series cross section (TSCS) table that visualizes the student’s course material page views. Experiments conducted on Moodle page views of actual materials collected during actual lessons found that the table visualizes both overall and individual viewpoints. Reactions to teacher instructions on course materials during class can also be visualized by the generated TSCS table. Moreover, because students who open course items late or do not open them can be identified clearly, the method can be used as a reference for improving future classes.