The ability to capture large amounts of data that describe the interactions of learners becomes useful when one has a framework in which to make sense of the processes of learning in complex learning environments. Through the analysis of such data, one is able to understand what is happening in these networks; however, deciding which elements will be of most interest in a specific learning context and how to process, visualize, and analyze large amounts of data requires the use of analytical tools that adequately support the phases of the research process. In this article, we discuss the selection, processing, visualization, and analysis of multiple elements of learning and learning environments and the links between them. We discuss, using the cases of two learning environments, how structure affects the behavior of learners and, in turn, how that behavior has the potential to affect learning. This approach will allow us to suggest possible ways of improving future designs of learning environments.

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