Data Analysis for Evaluation on Course Design and Improvement of ‘Cyberethics’ Moodle Online Courses

Data Analysis for Evaluation on Course Design and Improvement of ‘Cyberethics’ Moodle Online Courses
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“网络伦理学”Moodle 在线课程的课程设计评估和改进的数据分析

DOI:
10.1016/j.procs.2017.08.204
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发表时间:
2017
期刊:
Procedia Computer Science
影响因子:
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通讯作者:
Hiroshi Ueda and Motonori Nakamura
Hiroshi Ueda and Motonori Nakamura
中科院分区:
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文献类型:
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作者:
Ueda Hiroshi;Furukawa Masako;Yamaji Kazutsuna;Nakamura Motonori;Hiroshi Ueda and Motonori Nakamura

文献摘要

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本研究通过分析2013-2015学年超过20,000名用户的学习数据,对我们在日本使用的shibbolth -federated Moodle平台GakuNinMoodle上的安全意识教育在线课程进行了评估。我们开发了一种定制的数据收集方法来汇总每年每个学习对象的持续时间、完成状态和分数,我们使用这些数据来评估2015学年课程设计的变化。我们进一步使用来自Moodle分配模块的用户评论的文本挖掘来提取用户意见。与2013-2014学年相比,我们发现2015学年的完成率和最终考试成绩都有所提高。我们还发现,在分配了足够时间的用户和没有分配足够时间的用户之间出现了两极分化。此外,我们还获得了用户对完成课程所需时间和Moodle SCORM模块用户界面问题的投诉。这些数据分析将会很有用,因为Moodle是一个全球标准的学习管理系统。进一步的工作正在进行中,以近乎实时的方式分析学习数据和反馈。
This study evaluates our security awareness education online course on GakuNinMoodle, which is a Shibboleth-federated Moodle platform used in Japan, via an analysis of learning data from more than 20,000 users in the 2013–2015 academic years. We develop a customized data collection method to aggregate the duration, completion status, and score of each learning object each year, which we use to evaluate changes in course design for the 2015 academic year. We further use text mining of user comments from the Moodle assignment modules to extract user opinions. We found an improvement in the completion rate and final test score obtained in the 2015 academic year compared with 2013–2014. We also found that users became polarized between those who allotted sufficient time for the course and those who did not. Additionally, we obtained user complaints about the duration required to complete the course and problems with the user interface of the Moodle SCORM module. This data analysis will be useful because Moodle is a global-standard learning management system. Further work is underway to analyze learning data and feedback in near-real time.