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.
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.
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.