Innovation in Language Learning

Edition 17

Accepted Abstracts

Improving ESP Students’ Academic Writing through Data-Driven Learning: An Exploratory Study of Master 2 Accounting Students

Nassima Kaid, Djillali Liabes University (Algeria)

Abstract

University students often need a language that meets their specific needs and academic disciplines. However, many students struggle with the shift from General English to English for Specific Purposes. Most ESP instructors focus on developing students’ specialized lexis over enhancing their writing skills. Thus, achieving competency in English academic writing remains one of these students’ most serious problems. In this paper, I highlight the potential of corpus consultation by using the “AntConc” tool to improve the academic writing skills of Master 2 Students in Finance and Accounting. This exploratory research provides preliminary findings on incorporating corpora consultation approaches for ESP students in the Algerian university setting.

 

Keywords

Data-Driven Learning, English for Specific Purposes, Writing skill, Algerian University, Academic lexis

 

REFERENCES

[1] Ackerley, K. (2021). “Exploiting a genre-specific corpus in ESP writing: Students’ preferences and strategies,” in M. Charles and A. Frankenberg-Garcia, (Eds.), Corpora in ESP/EAP Writing Instruction: Preparation, Exploitation, Analysis, 1st ed. (pp. 78–99). New York, NY: Routledge. https://doi.org/10.4324/9781003001966

[2] Anthony, L. (2005).  “AntConc: design and development of a freeware corpus analysis toolkit for the technical writing classroom,” in Professional Communication Conference. IPCC 2005. Proceedings. International, 2005, pp. 729–737.

[3] Chen, M. and Flowerdew, J. (2018). A critical review of research and practice in data-driven learning (DDL) in the academic writing classroom, International Journal of Corpus Linguist., 23 (3), 335–369. https://doi.org/10.1075/ijcl.16130.che

[4] Chitez, M., & Bercuci, L. (2019). Data-driven learning in ESP university settings in Romania: multiple corpus consultation approaches for academic writing support. In F. Meunier, J. Van de Vyver, L. Bradley & S. Thouësny (Eds), CALL and complexity – short papers from EUROCALL 2019 (pp. 75-81). Research-publishing.net. https://doi.org/10.14705/rpnet.2019.38.98

[5] Corino, E., Onesti, C. (2019).  Data-Driven Learning: A Scaffolding Methodology for CLIL and LSP Teaching and Learning. Front. Educ., 4 (7), 1-12.  https://doi.org/10.3389/feduc.2019.00007

[6] Granger, S., Paquot, M. (2009). Lexical verbs in academic discourse: a corpus-driven study of learner use. In: Charles M.; Pecorari D.; Hunston S., Academic Writing: At the Interface of Corpus and Discourse (pp.193-214).  Continuum: London and New York. http://hdl.handle.net/2078.1/75731

[7] Khairas, E. E. (2019). Using AntConc Software as English Learning Media: The Students’ Perception, Epigram, 16 (2), 189-194.

[8] Swales, J. (1990).  Genre analysis: English in academic and research settings, (14th ed.). Cambridge: Cambridge University Press.

[9] Weningtyas P. I., Suhatmady, B., Asih, Y.U., Wardani, I., Ramadhan, A., & Anastasya, D. (2021). Using Concordance Software to Generate Academic Words in Applied Linguistics. Educational Studies: Conference Series, 1 (1), 31-41.

[10] Yan, H. (2022). A Blended System for Data-Driven Learning of English for Specific Purposes. iJET ,17 (12), 121-134. https://doi.org/10.3991/ijet.v17i12.29653

 

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