This work is based on the premise that the interactional construction of meaning is as important in online settings as it is face-to-face, especially in collaborative learning. Yet most studies of online learning use quantitative methods that assign meaning to contributions in isolation and aggregate over many sessions, obscuring the situated procedures by which participants accomplish learning through the affordances of online media. Methods for studying the interactional construction of meaning are available, but have largely been developed for brief episodes of face-to-face data, and need to be adapted to online learning where media resources, time scale, and synchronicity differ. In order to resolve this tradeoff, we have prototyped an abstract transcript notation to support sequential and interactional analysis of distributed and asynchronous interactions. The paper describes applications to data derived from asynchronous interaction of dyads and small groups.