This dissertation is three interlinked studies that pilot new methods for combining corpus linguistics and semantic network analysis (SNA) to understand and teach academic language. Findings indicate that this approach leads to a deeper understanding of technical writing and offers an exciting new avenue for writing curriculum.<br><br>The first phase is a corpus study of fixed and variable formulaic language (n-grams and p-frames) in academic engineering writing. The results were analyzed functionally, semantically and rhetorically. In contrast to previous n-gram analyses, the p-frame analysis found that variable phrases are often participant-oriented and communicate author stance. <br><br>The second phase combined corpus and network analysis tools to create educational materials. Several elements of successful design were highlighted. The final phase tested the materials in two classes with fifteen graduate students, finding evidence for the value of this novel approach.<br>