Resumen de Social network analysis in eLearning environments: a study of learner's interactions from several perspectives
Karina Lorena Cela Rosero
This research is based on the confluence of two research lines: eLearning and social network analysis (SNA). ELearning has already demonstrated its potential for profoundly affecting society, but its effectiveness depends on the appropriate use of technology. Technology should take on an important role during on-line training, but it should not itself become the goal. Instead, it should be a tool to empower educators to draw from different sources to create on-line educational activities that involve multiple applications promoting communication and interaction among participants as much as possible. Ideally, eLearning courses should promote and be driven by social networks that emerge as a result of discussion or interaction between students as well as between students and tutors. This makes eLearning similar, in practice, to numerous other human activities that require extensive interaction and collaboration, including social support networks, groups of professional collaborators, and staff within organisations. Researchers in these other areas have used SNA to gain detailed insights into how individuals work and develop together. In contrast, SNA has yet to be applied extensively in eLearning. Therefore the present work sought to use it to analyse patterns of students' social behaviour under different conditions and from different theoretical perspectives. First, the existing literature applying SNA to eLearning was systematically reviewed in order to take stock of what has already been investigated. On the basis of these findings, three SNA-based research studies were carried out: one focused on how learning styles may influence academic performance and participation, another focused on understanding how eLearners collaborate on joint projects, and a third examined the effectiveness of a ""flipped class"" format in a course. In the study on collaborative projects, the advantages of SNA for quantitative analysis of large amounts of interaction data were combined with the power of content analysis (CA) for qualitative analysis of the type and depth of interaction in order to provide more comprehensive social behaviour analysis. The findings of the three experimental studies that were undertaken complement and extend the literature on learner networks in eLearning environments. They also offer preliminary insights to help teachers and instructional designers improve eLearning courses that include collaborative components or flipped class designs. These insights need to be verified in diverse eLearning settings involving larger numbers of students, so the present studies have provided research questions for several years to come. Indeed, the present studies establish the usefulness of SNA for analysing various aspects of eLearning in greater detail than traditional research tools allow, opening the door to potentially entirely new lines of investigation aimed at improving eLearning outcomes. "
