Abstract
Emergence of universities towards “digital university” has already been present for some years. The use of digital is largely developed to ensure a good quality of education. Universities therefore use large-scale learning management systems to manage the interaction between learners and teachers. Teachers can provide online training and educational materials for students following their classes and courses, monitor their participation and evaluate their performance. Students can use interactive features such as discussion threads, videoconferences, and discussion forums. These online tools make it possible to create new social networks or connect online social interactions. This will allow us to understand the structure of this complex network and extract useful information. In this article, we report our research on the detection of student learning communities based on learner activity. We found that it is possible to group students in communities through their messages and response structures using standard community detection algorithms. Also, that their behaviours can be strongly correlated with their closest peers who belong to the same community.
Highlights
Learning using educational technologies has become an integral part of modern schools[1]
Instructors can share class materials online, have an online discussion forum, or complete questionnaires and homework submissions online. This in turn provides a wealth of new behavioural data that we can use to group students into communities using standard community detection algorithms to create qualitative and accessible software systems that will allow teachers to constantly improve their educational approaches
We found that it is possible to group students in communities through their messages and their response structures using standard community detection algorithms
Summary
Learning using educational technologies has become an integral part of modern schools[1]. Instructors can share class materials online, have an online discussion forum, or complete questionnaires and homework submissions online This in turn provides a wealth of new behavioural data that we can use to group students into communities using standard community detection algorithms to create qualitative and accessible software systems that will allow teachers to constantly improve their educational approaches. In paper [10] they have shown that learners belonging to these communities, homogeneous in terms of performance, are not united by their incoming motivations to register for the course nor by their level of prior experience Until today, these results have only been found in MOOCs and the user forum, where almost all of the relevant interactions in the course occur online and where the relationship between students is the direct connection between each other’s.
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