Data-driven learning in vocabulary instruction for South Korean learners of English

Abstract

Corpus-based tools and techniques not only facilitate the description of learners' language but can also be used in educational settings to design targeted classroom activities. This approach is known as data-driven learning (DDL). Access to corpus data enables learners to observe and analyze patterns in real language use, addressing their specific needs and fostering learning autonomy. While several studies have examined the effectiveness of DDL in teaching various language skills, few have investigated its impact highlighting the implications for specific cultural groups. Adopting a cumulative knowledge building perspective, this study systematically synthesizes and builds upon previous empirical research to advance our understanding of the effectiveness of DDL for South Korean learners of English. The purpose is paper to (1) survey different DDL activities piloted in vocabulary instruction across various English language teaching contexts in Korea; (2) determine the effectiveness of DDL for vocabulary instruction for this demographic; and (3) explore Korean learners' attitudes towards DDL. To do so, empirical studies were systematically identified using the Korea Citation Index with the keywords “data-driven learning,” “corpus-based,” “vocabulary,” “lexis,” “Korea,” and “Korean.” The results indicate that DDL is generally welcomed by and effective for students in this demographic. By building on cumulative findings, this study provides empirical foundation for curriculum development, classroom practices, and teacher training. Tailoring DDL activities to the Korean context can maximize their effectiveness addressing learners' unique linguistic challenges.