Tracing writing progression in English for academic purposes: A data-driven possibility in the post-COVID era in Hong Kong

Abstract

It is rare to use “big data” in writing progression studies in the field of second language acquisition around the globe. The difficulty of recruiting participants for longitudinal studies often results in sample sizes that are too small for quantitative analysis. Due to the global pandemic, students began to face more academic and emotional challenges, and it became more important to track the progression of their writing across courses. This study utilizes big data in a study of over 4,500 students who took a basic English for Academic Purposes (EAP) course followed by an advanced one at a university in Hong Kong. The findings suggest that analytics studies can provide a range of insights into course design and strategic planning, including how students’ language use and citation skills improve. They can also allow researchers to study the progression of students based on the level of achievement and the time elapsed between the two EAP courses. Further, studies using mega-sized datasets will be more generalizable than previous studies with smaller sample sizes. These results indicate that data-driven analytics can be a helpful approach to writing progression studies, especially in the post-COVID era.