Peers affect individual's productivity in the workforce, in education, and in other team-based tasks. Using large-scale language data from an online college course, we measure the impacts of peer interactions on student learning outcomes and persistence. In our setting, students are quasi-randomly assigned to peers, and as such, we are able to overcome selection biases stemming from endogenous peer grouping. We also mitigate reflection bias by utilizing rich student interaction data. We find that females and older students are more likely to engage in student interactions. Students are also more likely to interact with peers of same gender and with peers from roughly the same geographic region. For students who are relatively less likely to be engaged in online discussion, exposure to more interactive peers increases their probabilities of passing the course, improves their grade in the course, and increases their likelihood of enrolling in the following academic term. This study demonstrates how the use of large-scale, text-based data can provide insights into students' learning processes.