Social network analysis of a gamified e learning course: Small world phenomenon and network metrics as predictors of academic performance
Social networks and gamification are having an important and growing role in education. Social networks provide unknown communication and connection possibilities while games have the potential to engage students. This paper analyzes the structure of the social network resulting from a gamified social undergraduate course as well as the influence that student's position has on learning achievement. In a semester long experiment, a social networking site was delivered to students providing gamified activities and enabling social interaction and collaboration. Social network analysis was used to build the network graph and to compute four measures of the overall network and nine measures for each participant. Individual measures were then assessed as predictors of students' achievement using three different methods: correlation, principal component analysis and multiple linear regressions. The resulting social network has 167 actors and 2505 links, and it can be characterized as a small world. All analyses agreed on the potential of structural metrics as predictors of learning achievement but they differ in the measures considered as significant. A moderate correlation was found between most centrality measures and learning achievement. A social gamified system is used to gather information about the social network.The underlying social network is analyzed using Social Network Analysis (SNA).The resulting social network of a gamified course is a small world.Network metrics are assessed as predictors of learning achievement.Predictive models differ significantly and have limited representativity.
Comput. Hum. Behav.