• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>IEEE Global Engineering Education Conference >Educational data mining and data analysis for optimal learning content management: Applied in moodle for undergraduate engineering studies
【24h】

Educational data mining and data analysis for optimal learning content management: Applied in moodle for undergraduate engineering studies

机译：用于最优学习内容管理的教育数据挖掘和数据分析：应用于Moodle为本科工程研究

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Educational data mining applies data mining methods and tools to education-related data, typically collected through the use of an e-learning platform. Data stored in an e-learning platform database include user-platform interaction events (counts of scrolls, mouse clicks or page loads), platform access times per session or in total, times between events and various assessment scores such as grades per quiz or per session test, final grades, etc. In the present paper we focus on the time between actions (TBA) taken by the learner while he/she interacts with the platform. TBA values relay information on the mode of interaction of an individual learner with the platform. The two major questions addressed are (i) whether TBA values follow any probability density function (PDF) and if so, which is the PDF that optimally fits the data, and (ii) whether the parameters of such optimally fitted PDFs might serve as features for the clustering of the learning content modules or sessions into clusters of similar characteristics or functionalities. Results verify that skewed (asymmetric) PDFs can be fitted on the TBA value histograms with adequate accuracy. Furthermore, the parameters of few optimally fitted PDFs, used as a feature vector, result in a meaningful clustering of learning content parts into clusters of similar “character”. Clustering results may then be used as a recommendation to the course designer / instructor, to improve content structure or to optimally distribute/sequence parts of the course material.
机译：教育数据挖掘将数据挖掘方法和工具应用于教育相关数据，通常通过使用电子学习平台收集。存储在电子学习平台数据库中的数据包括用户平台交互事件（滚动，鼠标点击或页面加载数），每次会话的平台访问时间或事件之间的时间和各种评估分数，例如每个测验等级或每个Qualet在本文中，会议测试，最终成绩等。我们专注于学习者在他/她与平台互动时所采取的行动（TBA）之间的时间。 TBA值与平台的单个学习者交互模式中继信息。解决的两个主要问题是（i）TBA值是否遵循任何概率密度函数（PDF），如果是的话，这是最佳地适合数据的PDF，并且（ii）是否可实现最佳PDF的参数可以作为特征对于将学习内容模块或会话群集成群集的相似特征或功能。结果验证偏斜（不对称）PDF可以在TBA值直方图上，具有足够的准确性。此外，少数最佳拟合的PDF的参数，用作特征向量，导致学习内容部件的有意义的聚类成相似“字符”的集群。然后可以将聚类结果用作课程设计师/教练的推荐，以改善内容结构或最佳地分配课程材料的序列部分。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号