Community detection and social presence in students’ discussion fora

Abstract

In Distance Education (DE) the importance of community is often emphasized, as according to many experts in the field, the strong sense of community among learners improves the quality of teaching and learning. In this study, we use the Social Network Analysis (SNA) approach to detect the communities formed in the collaboration networks created by the actors’ (students and tutors) interactions in the Discussion Fora of a Learning Management System (LMS), in a Distance Learning setting within an Open University. Data from the actors’ communications in two Module Discussion Fora (MDF) are selected for creating graphs and applying appropriate algorithms, the Girvan-Newman and the Louvain algorithm, to reveal the communities and analyze their synthesis. As Discussion Fora within LMSs enhance the formation of communities, our study intends to visualize the dynamic interactions that emerge in such environments and provide the Coordinators and the Tutors of the Modules with useful information to improve the students’ learning outcomes. Such knowledge is valuable, especially in DE, where there is a lack of daily face-to-face interaction of a typical classroom. Furthermore, as social presence is essential for successful online teaching and learning, the absence of forum communities or the loose participation in such communities, from both Tutors and students, should alert the Coordinators to make suggestions and effectively manage the discussions and interactions among learners.