首页> 外文期刊>International Journal of Emerging Technologies in Learning (iJET) >How to Assist Tutors to Rebuild Groups Within an ITS by Exploiting Traces. Case of a Closed Forum.
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How to Assist Tutors to Rebuild Groups Within an ITS by Exploiting Traces. Case of a Closed Forum.

机译:如何通过利用跟踪来协助教师在ITS内重建组。封闭论坛的情况。

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Computer Supported Collaborative Learning (CSCL) is a new mode of teaching and one of the popular approaches for learning process. It allows virtual interactions between groups by providing tools such as: chat, internal email and discussion forums. One of the major problems caused by this learning process is the neglect and isolation of learners in groups, and usually is the cause of a heterogeneous group through social, cognitive or emotional ways. The method used is based on the exploitation of traces left on the online learning platform by learners and groups. The data collected from the environment can be observed and exploited in order to build social and cognitive indicators. Our approach is to design a model which assists the tutor to rebuild groups who are not homogeneous in order to prevent their isolation and abandonment. Our model offers the tutor the opportunity to rebuild the groups in an automatic way and based on the characteristics of quantitative indicators of all learners. Our work allowed us to test our algorithm from a functional and technical point of view and also identifies real variables from a collaborative online learning. It also allowed us to evaluate six different indicators proposed for this experiment, showing that they may assist the tutor to rebuild many groups again. The results show us that after the rebuilding groups, there has been a lot of participation in the forum and a considerable number of shares and documents deposited to the forum for each group. This high frequency of interaction between learners, lead them to a fruitful collaboration, and a good quality work at the end. The integration of other more advanced indicators may provide to tutor a better visibility to rebuild the groups that face difficulties.
机译:计算机支持的协作学习(CSCL)是一种新的教学模式,也是学习过程中流行的方法之一。它通过提供诸如聊天,内部电子邮件和论坛等工具,允许组之间进行虚拟交互。这种学习过程引起的主要问题之一是小组中学习者的疏忽和孤立,通常是通过社交,认知或情感方式导致异类小组的原因。使用的方法基于学习者和团体在在线学习平台上留下的痕迹。可以观察和利用从环境中收集的数据,以建立社会和认知指标。我们的方法是设计一个模型,该模型可帮助导师重建非同质的组,以防止其孤立和遗弃。我们的模型为导师提供了机会,可以根据所有学习者的定量指标的特点,以自动方式重建小组。我们的工作使我们能够从功能和技术的角度测试算法,并从协作在线学习中识别出真正的变量。它还使我们能够评估为该实验提出的六个不同指标,表明它们可以帮助教师再次重建许多小组。结果表明,在重建小组之后,论坛中有很多参与者,每个小组都有大量的份额和文件存放到论坛中。学习者之间如此频繁的互动,导致他们进行富有成果的合作,并最终获得了高质量的作品。其他更先进的指标的集成可以为教师提供更好的可视性,以重建面临困难的小组。

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