Scientific communication demands more than the mere listing of empirical findings or assertion of beliefs. Arguments must be constructed to motivate problems, expose weaknesses, justify higher-order concepts, and support claims to be advancing the field. Researchers learn to signal clearly in their writing when they are making such moves, and the progress of natural language processing technology has made it possible to combine conventional concept extraction with rhetorical analysis that detects these moves. To demonstrate the potential of this technology, this short paper documents preliminary analyses of the dataset published by the Society for Learning Analytics, comprising the full texts from primary conferences and journals in Learning Analytics and Knowledge (LAK) and Educational Data Mining (EDM). We document the steps taken to analyse the papers thematically using Edge Betweenness Clustering, combined with sentence extraction using the Xerox Incremental Parser's rhetorical analysis, which detects the linguistic forms used by authors to signal argumentative discourse moves. Initial results indicate that the refined subset derived from more complex concept extraction and rhetorically significant sentences, yields additional relevant clusters. Finally, we illustrate how the results of this analysis can be rendered as a visual analytics dashboard
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