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Using metrics and cluster analysis for analyzing learner video viewing behaviours in educational videos

机译:使用指标和聚类分析来分析教育视频中的学习者视频观看行为

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On line video is a powerful tool for e-learning and this is evident from a number of reports, research papers and university initiatives, which portray that online video is becoming an important medium for delivering educational content. Therefore, research that focuses on how students view educational videos becomes of particular interest and in previous work we argued that in order to efficiently analyze learner viewing behavior we should deploy tools that log the learner activity and assist usage analysis and data mining. Working towards this direction, a framework for recording and analyzing learner behavior was presented together with findings of applying the framework into educational settings. In this paper, we continue this work by presenting a set of metrics that can be derived from the framework and be used to measure learner engagement and video popularity. These metrics in conjunction with the data mining method of clustering are then used to gain insights into learner viewing behavior.
机译:在线视频是一种强大的电子学习工具,从许多报告,研究论文和大学计划中可以看出,在线视频正成为提供教育内容的重要媒介。因此,针对学生如何观看教学视频的研究变得尤为重要,在先前的工作中,我们认为,为了有效地分析学习者的观看行为,我们应该部署记录学习者活动并协助使用情况分析和数据挖掘的工具。朝着这个方向努力,提出了一个记录和分析学习者行为的框架,以及将该框架应用于教育环境的发现。在本文中,我们通过提供一组可以从框架中得出的指标来继续这项工作,这些指标可以用来衡量学习者的参与度和视频受欢迎程度。然后,将这些指标与聚类的数据挖掘方法结合使用,以深入了解学习者的观看行为。

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