Millions of people worldwide are currently enrolled in courses provided on large-scale learning platforms (aka ‘MOOCs’), typically collaborating in online discussion forums with thousands of peers. Current learning theory emphasises the importance of this group interaction for cognition. However, while a lot is known about the mechanics of group learning in smaller and traditionally organised online classrooms, fewer studies have examined participant interactions when learning “at scale.” Some studies have used clickstream data to trace participant behaviour; even predicting dropouts based on their engagement patterns. However, many questions remain about the characteristics of group interactions in these courses, highlighting the need to understand whether—and how—MOOCs allow for deep and meaningful learning by facilitating significant interactions. But what constitutes a “significant” learning interaction? In large-scale MOOC forums, with socio-culturally diverse learners with different motivations for participating, this is a non-trivial problem. MOOCs are best defined as “non-formal” learning spaces, where learners pick and choose how (and if) they interact. This kind of group membership, together with the short-term nature of these courses, means that relatively weak inter-personal relationships are likely. Many of the tens of thousands of interactions in the forum may have little relevance to the learning process. So can we actually define the underlying network of significant interactions? Only once we have done this can we explore firstly how information flows through the forums, and secondly the robustness of those interaction networks: in short, the effectiveness of the platform design for supporting group learning at scale. To explore these questions, we analysed data from 167,000 students registered on two business MOOCs offered on the Coursera platform. Almost 8000 students contributed around 30,000 discussion posts over the six weeks of the courses; almost 30,000 students viewed at least one discussion thread, totalling 321,769 discussion thread views. We first modelled these communications as a social network, with nodes representing students who posted in the discussion forums, and edges (ie links) indicating…