Engineers learn to write: Coaching the art of noticing with writing samples

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.