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Regular version of the site

Book chapter

Software development for corpus research in English studies: the experience of the National Research University Higher School of Economics, Perm, Russia

Smirnova E. A., Strinyuk S. A., Lanin V.

In recent years, English has become lingua franca in the spheres of higher education and science in Russia: more and more university courses are delivered in English, university students and academics take part in international conferences and workshops, Russian scholars strive to publish their research findings in international peer-reviewed journals.

Such a shift of focus makes the ability to write a good quality academic text a necessary skill in the modern academic environment. However, as our experience of EAP practitioners shows, Russian L2 writers, having a good command of General English, often find it challenging to conform to the conventions of English academic discourse when writing their research papers or project proposals. Despite the existence of various types of software which can check grammar and/or style of a text (e.g. Grammarly, Ginger, Language Tool) and even provide feedback about errors (Dreschler et al., 2019), to our knowledge, there are no programmes focusing on linguistic characteristics of an academic text. Besides, in the existing literature there appears to be no clear rubric for academic writing assessment. The application Paper Cat developed by a team of teachers and students from the National Research University Higher School of Economics, Perm, Russia, is aimed at facilitating students’ and researchers’ writing in English by identifying the most significant features of academic discourse. We used the world accumulated knowledge in EAP to develop the software that is able to assess an academic text against a set of criteria, i.e. academic discourse markers, selected from academic style guides, handbooks and research articles on EAP. Evaluating the ‘quality of academic discourse’ of the text in terms of style can be automated by using software to tag style markers in that text. At the heart of this approach is creating a repository of patterns which are needed to extract the markers mentioned above. The quality of an L2 academic writing is assessed against a set of criteria worked out on the basis of competent writing features analysis.

In book

WAC Clearinghouse, 2020.