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The recent emergence of several low-cost, high resolution, multimodal sensors has greatly facilitated the ability for researchers to capture a wealth of data across a variety of contexts. Over the past few years, this multimodal technology has begun to receive greater attention within the learning community. Specifically, the Multimodal Learning Analytics community has been capitalizing on new sensor technology, as well as the expansion of tools for supporting computational analysis, in order to better understand and improve student learning in complex learning environments. However, even as the data collection and analysis tools have greatly eased the process, there remain a number of considerations and challenges in framing research in such a way that it lends to the development of learning theory. Moreover, there are a multitude of approaches that can be used for integrating multimodal data, and each approach has different assumptions and implications. In this paper, I describe three different types of multimodal analyses, and discuss how decisions about data integration and fusion have a significant impact on how the research relates to learning theories.