In this paper we propose a method to assess key collaborative processes during computer-supported group work based on physiological signals and eye-movements. Synchronous interpersonal multimodal signals from 30 dyads were recorded while collaborating remotely. Features measuring how much collaborators' eye-movements and physiology are coupled were extracted from the obtained time series and two regression models were trained to assess collaboration. Results show that the two coupling measures can be used to predict collaborative processes such as grounding and convergence. Assessing those processes is a major step toward the development of remote collaborative interfaces able to adapt to the users' social interactions.