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 containing an English language corpus annotated to enhance rhetorical input, a concordancer that was searchable for rhetorical functions, and an automated writing evaluation engine that generated rhetorical feedback. Guided by current approaches to teaching academic writing (Lea & Street, 1998; Lillis, 2001; Swales, 2004) and the knowledge-telling/knowledge-transformation model of Bereiter and Scardamalia (1987), 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 one-semester courses at a US university. The qualitative and quantitative data sets were used for within-student, within-group, and between-group comparisons--the two independent variables for the latter being course section and language background. Our findings suggest that exploiting technology-mediated corpora can foster novice writers' exploration and application of genre conventions, enhancing development of rhetorical, formal, and procedural aspects of genre knowledge.