Analysis of Meaning Making in Online Learning

This paper reports on our efforts to deepen the analysis of online collaborative learning. Most studies of online learning use quantitative methods that assign meaning to contributions in isolation and aggregate over many sessions, obscuring the actual 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 do not scale well to online learning where media resources, time scale, and synchronicity all differ. In order to resolve this tradeoff, we are developing an analytic method that scales up sequential and interactional analysis to longer term distributed and asynchronous interactions. The paper describes applications to data derived from asynchronous interaction of dyads and small groups. Our long-term objective is to obtain a deep understanding of how learning is accomplished in technology-mediated interactions that take place at multiple time scales in different media.