In this decade, the use of learning management systems (LMS) does not cease to increase, becoming one of the most popular approaches adopted and widely used in the learning process. Learners' online activities generate a huge amount of unused data that is wasted because traditional learning analyses are not able to process them. In this regard, a large collection of applications/tools have emerged to conduct research in educational data mining (EDM) and / or learning analysis (LA). This study looks into the recent applications/tools of Big Data technologies in education and presents some of the most widely used, accessible, and powerful tools in this field of research. The majority of these tools are for researchers with the purpose of conducting research on educational data mining and learning analysis.
Abstract-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 development of e-learning systems has started a revolution for instructional content delivering, learning activities, and social communication. Although the benefits of e-learning have been discussed in various previous studies; it is a critical issue of better understanding the reasons why some learners are isolated during the e-learning experience. This paper proposes a multi-dimensional model on tracking interactions between learners via four dimensions: (1) an affective, (2) a participative, (3) an interactive and (4) a technical one. Tracking course learner’s behaviour within the learning environment can provide the tutor a large set of indicators. The main objective of this study is to provide the tutor the opportunity to rebuild the working groups based on theses different indicators. The analytical results strongly support the appropriateness of the proposed model through tutor's satisfaction. The findings of an empirical study of 12 work‐based teams are discussed.
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