Modeling SNA result to improve learning community
Abstract The use of Social Network Analysis (SNA) for online learning communities’ analysis is common and usually performed after the ending of semester. Yet, even if such analysis is very useful, it is costly, and cannot be performed many times during the semester. In this paper, we present a model of automated SNA based inference, for a large- scale community, taking into account specific environment of developing higher education system. The model is designed so to send automated reminders to all users, according their activity in the period of two weeks. One additional analysis after the mid-term exams checks if activity matches performance. It has crucial role in directing both students and educators towards the common goal: success at the final exams. The presented model enables inference on user attributes, which are stored in student model ontology. As such, the model is a step in the development of semantic and adaptive learning environment.