Data-driven learning, second language acquisition and languages other than English: Connecting the dots

The overall purpose of data-driven learning (DDL) is to help language learners in ways that other approaches are unable to. By relying on resources and techniques based on corpus linguistics, DDL provides learners with the opportunity to interact with authentic language data and discover linguistic patterns emerging from use. But what do language learners need help with specifically? Can DDL provide such help in ways that other approaches cannot? And can it do so in the context of languages other than English (LOTEs)? To address these questions, we must turn to second language acquisition (SLA) as a whole, DDL pedagogical principles, and LOTEs specificities. We must also consider the overlaps between these three areas and ‘connect the dots’. In this chapter, I seek to do so by means of a case study on L2 Italian phraseology. By identifying the linguistic variables pertaining to the learning aims, based on the available research evidence, the study attempts to show how we can determine whether DDL can help learners in ways that other approaches cannot.