  For more than 20 years, social network analysis of student collaboration
networks has focused on a student's centrality to predict academic performance.
And even though a growing amount of sociological literature has supported that
academic success is contagious, identifying central students in the network
alone does not capture how peer interactions facilitate the spread of academic
success throughout the network. Consequently, we propose novel predictors that
treat academic success as a contagion by identifying a student's learning
community, consisting of the peers that are most likely to influence a
student's performance in a course. We evaluate the importance of these learning
communities by predicting academic outcomes in an introductory college
statistics course with 103 students. In particular, we observe that by
including these learning community predictors, the resulting model is 68 times
more likely to be the correct model than the current state-of-the-art
centrality network models in the literature.
