Addressing the challenges of Data-Driven Learning (DDL) in the ESP Classroom
Data-Driven Learning (DDL) has been shown to be an effective approach in the ESP classroom. Not only does it allow non-subject specialist instructors to identify relevant and useful information for the classroom, it also empowers learners to address their individual discipline-specific language needs. DDL has been shown to work with learners of different proficiency levels and ages, and in classrooms with minimal to advanced computer setups. However, previous reports have also shown that instructors hoping to introduce DDL into the ESP classroom face various challenges. These include finding a suitable corpora, knowing what to search for when a corpus is available, reducing data overload, interpreting and incorporating results as part of learning, and contextualizing findings in the target field and discourse community. They also include mundane challenges such as installing software on institution computer systems, dealing with system crashes and poor Internet connections, and providing support for learners who are not very computer-savvy. In this presentation, I will first present a case for using DDL in the ESP classroom. I will then explain how the various challenges associated with DDL can be minimized or fully addressed through careful preparation, effective classroom practices, and the use of innovative support tools. Finally, I will discuss how ESP administrators and instructors can contribute to future DDL tools and methods development.