Principled pattern curation to guide data-driven learning design

Insights from corpus linguistics (CL) have informed language learning and materials design, among many other areas. An important nexus between CL is the use of Data-Driven Learning (DDL), which draws on data in classroom brings opportunities for inductive discovery. Within ethos DDL, learners are encouraged to discover patterns and, so doing, foster more complex cognitive processes such as making inferences. While studies DDL concur success this approach, it still perceived a marginal practice. Its far has been largely limited intermediate advanced level higher education settings (Boulton Cobb 2017). This paper aims offer guiding principles how might wider application across all levels (not just at Intermediate above) set out exemplars their different proficiency. Based insights second acquisition (SLA) learner research (LCR), focus will be identifying curation that differentiated stage learning. In particular, we keen build recent work looks SLA through lens usage-based (UB) models (that is, view being acquired exposure language).