We propose three extensions for the relational event framework to model the co-evolution of multiple network event streams which are increasingly available thanks to the explosive growth of online applications. Firstly, a flexible stratification approach is considered to allow for more complex data structures with many types of nodes and events. Secondly, an inference method that combines nested case-control sampling with stratification is discussed to scale the approach to very large data sets. Finally, a suite of new temporal and network statistics is introduced to reflect the potentially complex dependencies among events and observed heterogeneities on nodes and edges.

The empirical value of new extensions is demonstrated through an analysis of social learning in Massive Open Online Coureses (MOOCs). In particular, three modeling problems are considered from the network perspective: (1) the utility of social factors, performance indicators, and clickstream behaviors in the prediction of course dropout, (2) the social and temporal structure of learner interactions across discussion threads, and (3) the forms of mutual dependence of social learning interactions on prior learning success, and future learning success on forms of prior social learning interaction.