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 student’s 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.

Computers in Human Behavior