Productive and effective collaborative learning is rarely a spontaneous phenomenon but rather the result of meeting a set of conditions, orchestrating and scaffolding productive interactions. Several studies have demonstrated that conflicts can have detrimental effects on student collaboration. Through the application of network science, and social network analysis in particular, this learning analytics study investigates the concept of group robustness; that is, the capacity of collaborative groups to remain functional despite the withdrawal or absence of group members, and its relation to group performance in the frame of collaborative learning. Data on all student and teacher interactions were collected from two phases of a course in medical education that employed an online learning environment. Visual and mathematical analysis were conducted, simulating the removal of actors and its effect on the group’s robustness and network structure. In addition, the extracted network parameters were used as features in machine learning algorithms to predict student performance. The study contributes findings that demonstrate the use of network science to shed light on essential elements of collaborative learning and demonstrates how the concept and measures of group robustness can increase understanding of the conditions of productive collaborative learning. It also contributes to understanding how certain interaction patterns can help to promote the sustainability or robustness of groups, while other interaction patterns can make the group more vulnerable to withdrawal and dysfunction. The finding also indicate that teachers can be a driving factor behind the formation of rich clubs of well-connected few and less connected many in some cases and can contribute to a more collaborative and sustainable process where every student is included.