Data-driven Learning: Aiming at the Bigger Picture


  • Tatyana Karpenko-Seccombe University of Huddersfield



data-driven learning, rhetorical functions, academic writing


There has been increasing interest in corpus-based teaching of rhetorical features in academic writing at the discourse level (Chen and Flowerdew 2018; Dong and Lu 2020; Moreno and Swales 2018). In line with this tendency, this paper explores the potential of using corpus tools in teaching rhetorical elements of academic writing and considers the ways in which wider aspects of academic writing can be addressed through the use of corpora, for example rhetorical moves in argumentation and counter-argumentation, authorial presence, evaluating an argument and problem–solution patterns. The paper places specific emphasis on practical suggestions for tasks and activities, locating these practical applications within the framework of existing corpus research. The tasks are based on the use of several corpus tools, Lextutor concordance, SkELL, BNC-English corpora and MICUSP. They are targeted at upper-intermediate and advanced second language learners—senior undergraduates, postgraduates and researchers—and can be used across multiple disciplines.




How to Cite

Karpenko-Seccombe, T. (2023). Data-driven Learning: Aiming at the Bigger Picture. Nordic Journal of English Studies, 22(1), 144–181.

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