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Technology mediated groups interact in surprising ways to produce coherent behavior that contributes to collective and individual ends. Traces of these activities are captured in system log data. In this report we use Erickson's application of human social intelligence to social computing as a frame for contrasting 3 studies of rich trace data captured from an online learning management system. First, we present a data-mining algorithm that is effective for identifying small group membership in dense networks. Second, we describe our use of social network analysis techniques to make online group development more visible. Third, we transform raw trace data into a weighted, social form that enables the development of prosthetics for social intelligence in online learning and work environments. Finally, we assert that thinking about "social intelligence prosthetics" will help the social computing research community to develop theory that considers the interwoven roles of computation, interaction design and data structure.