Within educational data mining, many statistical models capture the learning of students working individually. However, not much work has been done to extend these statistical models of individual learning to a collaborative setting, despite the effectiveness of collaborative learning activities. We extend a widely used model (the additive factors model) to account for the effect of collaboration on individual learning, including having the help of a partner and getting to observe/help a partner. We find evidence that models that include these collaborative features have a better fit than the original models for performance data and that learning rates estimated using the extended models provide insights into how collaboration benefits individual students' learning outcomes.