Recently, learning analytics has become the focus in the interdisciplinary field of education technology. Among learning analytical approaches, social network analysis (SNA) plays a critical role in examining collective learning patterns. In this study, we collect the forum data in an undergraduate course from a university's online learning system. On the one hand, SNA is adopted to investigate the learners' social network characteristics including network structure and network positions. On the other hand, we adopt the Pearson correlation analysis to identify the relationship between social network positions (e.g., degree centrality, closeness centrality, betweenness centrality, prestige and influence) and learning outcomes of learners. The experimental results show that most high-performing learners are located in the core position of network. Moreover, there is a significantly positive correlation between learner's social network centrality and learning outcomes, and high-performing learners have higher prestige and influence in the forum. The indepth analyses could help teachers establish effective interactive mechanism that meets knowledge skills of different individuals, as well as guide learners to help each other in collaborative learning.
For example, REF explored the relationship between social centralities and outcomes and found that learners in the central position tended to perform better than their counter-parts.
Social Network Characteristics of Learners in a Course Forum and Their Relationship to Learning Outcomes
CSEDU
Computer Science