Instructors often use writing samples as scaffolding to help students understand assignments and the instructor''s expectations. This paper combines a close analysis of communication genres with a multivariate statistical analysis of clustering among genres. The aim is to help instructors select effective samples by highlighting genre variation and clustering within a corpus of possible text samples. By “effective” I mean samples that (1) help students with rhetorical invention by giving them a range of options for meeting the writing challenges of the assignment and (2) help students notice and model high-level expertise in “rhetorical priming” [1], a key linguistic component of expert technical writing behavior. The paper''s findings and conclusions are relevant to engineering and professional communication instructors charged with meeting the Accreditation Board for Engineering and Technology (ABET) accreditation criteria [2] and with helping students understand and gain control of language differences between, for example, proposals, experimental reports, and applied case studies.
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