How to Assist Tutors to Rebuild Groups Within an ITS by Exploiting Traces. Case of a Closed Forum.

Mohammed Salihoun, Fatima Guerouate, Naoual Berbiche, Mohamed Sbihi


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.


Indicators; Intelligent Tutoring System; Online Collaborative Work; Traces

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Copyright (c) 2017 Mohammed Salihoun, Fatima Guerouate, Naoual Berbiche, Mohamed Sbihi

International Journal of Emerging Technologies in Learning (iJET) – eISSN: 1863-0383
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