Redes profesionales en procesos educativos en línea

In the present study, an online continuing medical education course is analyzed as it evolves. We investigate the benefits of using Social Network Analysis and Data Mining techniques to predict and improve the students’ performance, as well as to tailor the course into suitable groups of students and teachers, in order to enhance the course results. Specifically, we use information from participants, shared prior to the course, as the motor to create an acquaintance network, which evolves as the course evolves. These social links between participants allow us to characterize their interactions in relationship to the course, hence, establishing the role they play in their social network, for example, as hubs or outliers. In particular, this allows us to group participants
according to their social closeness. Moreover, using Educational Data Mining techniques, we can forecast the students’ potential performance in the course and design instructional strategies accordingly. This research shows the importance of working in multidisciplinary teams to address social problems, and also, to bring forth to education research knowledge and techniques that have been successfully used in other research fields.