To better understand the promising effects of data-driven learning (DDL) on language learning processes and outcomes, this study explored DDL learning events enabled by the Research Writing Tutor (RWT), a web-based platform that contains: an English language corpus annotated to enhance rhetorical input; a concordancer searchable for rhetorical functions; and an automated writing evaluation engine that generates rhetorical feedback. Guided by current approaches to teaching academic writing (Lea & Street, 1998; Lillis, 2001; Swales, 2004) and by Bereiter and Scardamalia’s (1987) knowledge-telling/knowledge-transformation model, we set out to examine whether and how direct corpus uses afforded by RWT impact novice native and non-native writers’ genre learning and writing improvement. In an embedded mixed-methods design, written responses to DDL tasks and writing progress from first to last drafts were recorded from 23 graduate students in separate onesemester courses at a US university. The qualitative and quantitative data sets were used for withinstudent, within-group, and between-group comparisons, the two independent variables for the latter being course section and language background. Our findings suggest that exploiting technologymediated corpora can foster novice writers’ exploration and, application, and production of genre conventions, enhancing development of rhetorical, formal, and procedural aspects of genre knowledge.