The Use of Corpus and Network Analysis in Teaching Engineering Eap Phrases
Abstract
This dissertation is composed of 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 pframes) in academic engineering writing. The results were analyzed functionally, semantically and rhetorically. While previous n-gram analyses highlighted how engineering writing relies on text-oriented phrases (Hyland 2008a), the p-frame analysis found that variable phrases are often participant-oriented and communicate author stance. The p-frames also demonstrated that prepositional phrase and passive verb constructions were key structures for author stance. The second phase combined corpus and network analysis tools to create educational materials. Several elements of successful design were highlighted, including how to best combine corpus and SNA tools, the role of a linguistically knowledgeable designer, and the creation of a framework that can visualize rich insights into the rhetorical, semantic, and syntactic nuances of formulaic language. Given the complexity of engineering writing, students need clear materials that highlight accessible findings and allow them to practice and master formulaic language. Thus, the final phase tested the materials in two classes with fifteen graduate students, finding evidence for the value of this novel approach. The major benefits were that students learned synonyms for overused items; experimented with fresh terms while practicing common syntactic structures and moves; and developed skills in identifying and employing author stance.
Degree
Ph.D.
Advisors
Roberts, Purdue University.
Subject Area
Design|Linguistics
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