Effect of Data Driven Language Learning (DDLL) on EFL Learners: A Corpus-driven Language learning Approach


  • Muhammad Imran Shah Lecturer (Applied Linguistics), Government College University Faisalabad, Pakistan; Scholar of PhD (Applied Linguistics), University Uata Malaysia
  • Manvender Kaur Sarjit Singh School of Languages, Civilisation and Philosophy, College of Arts and Sciences, Universiti Utara Malaysia


Inductive Learning, Procedural Knowledge, Lexical collocations, corpora, Data-Driven Language Learning


Foreign language learners’ acquisition of linguistic input is more likely to increase if their attention is consciously drawn to linguistic features (Schmidth, 2009). The present research study explores the effect of data-driven language learning (DDLL) on Pakistani EFL undergraduates. A quantitative approach based on post-positivism underpinning the theory of noticing hypothesis (Flowerdew, 2015) has been applied on 100 undergraduates, comprising of 50 students for experimental and 50 for the controlled group from GC University Faisalabad Pakistan. A pre-test was conducted to evaluate the knowledge of collocation among the selected participants. Then, the experimental group was undergone explicit learning with the compilation of Adhoc corpora of editorials of Pakistani and British newspapers to look at collocations under the supervision of the teacher. In this case, learners became researchers and language became data (Boulton, 2018); the learners more learned about the things they attend to deliberately. Whereas, the control group of 50 students was treated through traditional approaches. The results of the post-test revealed that deliberate attention on lexical collocation with CDLL approach of language learning enhanced more knowledge among the experimental group. The study has strong pedagogical implications for academicians and researchers regarding the importance of CDLL in learning various linguistic features effectively.


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