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A Technique for Conflict Detection in Collaborative Learning Environment by Using Text Sentiment

机译:用文本情绪进行协同学习环境冲突的技术

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Computer-Supported Collaborative Learning (CSCL) can give many benefits to students such as promoting creativity and sense of community, sharing abilities, etc. However, when groups of people work together, conflict is inevitable. Generally, conflict in any CSCL situation is uncomfortable, time consuming and counterproductive. It is hard to characterize a conflict because it can involve many factors -e.g., environmental factors, member's differences, etc. This paper proposes a technique to recognize conflicts in a group and the members involved in them by focusing in the socio-emotional interactions. As disagreements between group members generally cause negative emotions, and members can induce negative emotions to other members; then, a conflict between two or more members can be recognized when there are bidirectional negative messages in the same conversation thread. The proposed technique represents chat interactions as a digraph in which the nodes represent users and the edges indicate the transference of negative sentiments during the interactions. Then, a matrix of scaled commute times is used to detect clusters (subgroups having conflict). The validation of the technique shows promising results. The proposed technique is able to detect conflicts automatically, reducing the human effort required to detect these conflicts by other means.
机译:计算机支持的协作学习(CSCL)可以给学生很多好处,例如促进社区的创造力和责任感,共享能力,等等​​。然而,当一群人一起工作,冲突是不可避免的。一般来说,在任何情况下CSCL冲突是不舒服的,耗费时间和适得其反。这是很难表征冲突,因为它可能涉及的因素很多-e.g.,环境因素,成员的差异等提出了识别一组与冲突在社会情感的互动重点参与他们成员的技术。作为组成员之间的分歧通常造成负面情绪,和成员可诱导消极情绪其它成员;然后,当有在同一个会话线程双向负面信息两个或多个成员之间的冲突可以被识别。所提出的技术代表聊天相互作用为其中节点表示用户和边表示消极情绪的交互期间的转移有向图。然后,经缩放的通勤时间的矩阵被用于检测(具有冲突子组)的簇。的技术显示有希望的结果验证。所提出的技术能够自动检测冲突,减少通过其他手段来检测这些冲突所需的人力。

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