Network analytic techniques are particularly well suited to studying how students form groups, since interactions between people are affected by other interactions within the same community. Collaboration between students in class and out of class during a calculus-based, introductory physics course at a liberal arts college is described using networks. Students are represented by nodes, which are connected by edges, representing interactions between pairs of students. Both the nodes and the edges are associated with various covariates representing the characteristics of the student and the intensity of their collaboration. Exponential family random graph models (ERGMs), a network analytic technique analogous to logistic regression, are used to estimate the probability of the existence of a particular edge, based on the various covariates and the overall structure of the network. An extension to ERGMs, valued ERGMs, model the strength of the edges in addition to their existence. Both the binary and valued ERGMs found that reciprocal interactions, hierarchical interactions, and interactions within assigned groups are more likely to occur. The valued ERGM also found that students with higher course grades correlate with the strength of interactions that students report. There is some evidence that instructors may affect who students collaborate with outside of class.