Open AccessShould We Use It in Our Classrooms: An Analysis of Data-Driven Learning Research
Author(s) -
Kai Bao
Publication year - 2021
Publication title -
english linguistics research
Language(s) - English
Resource type - Journals
eISSN - 1927-6036
pISSN - 1927-6028
DOI - 10.5430/elr.v10n3p66
Subject(s) - variety (cybernetics) , set (abstract data type) , computer science , applied linguistics , field (mathematics) , perception , corpus linguistics , linguistics , mathematics education , psychology , natural language processing , artificial intelligence , philosophy , mathematics , neuroscience , pure mathematics , programming language
Corpus linguistics has become increasingly important to both language researchers and teachers over the past three decades. As a popular practice of corpus linguistics, Data-Driven Learning (DDL) sees a rapidly growing body of research as well as instruction in the field. There is, however, a lack of comprehensive literature reviews that summarize the effectiveness, learners’ perception, as well as factors affecting the success of DDL to guide its practices. In response, this study analyzes previous DDL research to show the feasibility of the activities in EFL classrooms. For the purpose, we collected and analyzed relevant research articles from 19 journals in the discipline of applied linguistics. Our analysis revealed that while DDL has been proved generally effective in improving learners’ target language proficiency with respect to a variety of linguistic aspects, a set of its drawbacks have been elicited from the learners. The results indicate the instructors’ need to take into account the learner as well as technique background before the introduction of DDL into their classrooms.