The use of corpora in English language classrooms has been linked to positive learning outcomes (e.g. Yilmaz, 2017; Chen & Flowerdew, 2018; Lin, 2021) as evidenced by research in the field of data driven learning (DDL, Johns, 1991). Although DDL studies have mostly used ready-made corpora like the British National Corpus or the Corpus of Contemporary Academic English, only few studies have promoted learner generated corpora (see Charles, 2015 for personal corpora). This is an important research gap, given that learner generated materials promote learner autonomy (Choi & Nunan 2022) and are regarded positively by the students (e.g. Bakla, 2018). Based on this background and based on the need to use authentic, learner-relevant materials in technical English courses, I designed a technical English course which encourages students to compile a corpus of academic texts relevant to their studies. 18 undergraduate and master’s students studying in a variety of engineering programs were first instructed on the use of a corpus linguistic software (Anthony, 2022). The student-generated technical English corpus (300.000 words), then, has been implemented to conduct a number of tasks and activities in the classroom, including “the use of visual items”, “writing clear instructions”, and “technical vocabulary in context”. In order to understand students’ perspectives on the use of student-generated corpus, their reflection texts have been analysed using thematic analysis (Braun & Clarke, 2006). In this presentation, I will first describe the distinctive characteristics of this learner generated corpus, and will then, based on the students’ reflections, illustrate the affordances and challenges of using such corpus. Although most of the learners found this approach useful for understanding technical vocabulary in context, few students reported limitations regarding the use of the software. Implications for the development of corpus literacy in higher education and implementation of learnergenerated corpora will be given.

References  

Anthony, L. (2022). AntConc (Version 4.0.0) [Computer software]. Waseda University. https://www.laurenceanthony.net/software/antconc/  

Bakla, A. (2018). Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study. Computers & Education, 125, 14-38.

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative research in psychology, 3(2), 77-101.  

Charles, M., Leńko-Szymańska, A., & Boulton, A. (2015). Same task, different corpus: The role of personal corpora in EAP classes. In Multiple Affordances of Language Corpora for Data-driven Learning (Vol. 69, pp. 131–154). John Benjamins Publishing Company. https://doi.org/10.1075/scl.69.07cha  

Chen, M., & Flowerdew, J. (2018). Introducing data-driven learning to PhD students for research writing purposes: A territory-wide project in Hong Kong. English for Specific Purposes (New York, N.Y.), 50, 97–112. https://doi.org/10.1016/j.esp.2017.11.004

Choi, J., & Nunan, D. (2022). Learner contributions to materials in language teaching. In The Routledge Handbook of Materials Development for Language Teaching (pp. 429440). Routledge.  

Lin, M. H. (2021). Effects of Data-Driven Learning on College Students of Different Grammar Proficiencies: A Preliminary Empirical Assessment in EFL Classes. SAGE Open, 11(3). https://doi.org/10.1177/21582440211029936  

Johns, T. (1991). Should you be persuaded: Two examples of data-driven learning. English Language Research Journal, 4, 1-16.  

Yılmaz, M. (2017). The Effect of Data-driven Learning on EFL Students’ Acquisition of Lexico-grammatical Patterns in EFL Writing. Eurasian Journal of Applied Linguistics (Online), 3(2), 75–88. https://doi.org/10.32601/ejal.460966