Corpus-driven language learning: A scientometric analysis of contemporary trends and their trajectories for pedagogical innovation within the Indonesian context
DOI:
https://doi.org/10.26555/bs.v45i1.1214Keywords:
Corpus-driven, Language learning, Indonesian, Pedagogical innovationAbstract
In the contemporary landscape of language education, the integration of technology and data-driven approaches has emerged as a significant catalyst for pedagogical innovation. This study investigates contemporary trends and trajectories in corpus-driven language learning (CDLL) through a scientometric analysis of 1,118 journal articles indexed in Scopus from 2014 to 2024. This study selected articles that substantively explored CDLL principles and applications using empirical methodologies, excluding those lacking direct CDLL focus or empirical data to capture the most recent decade of research. Employing bibliometric analysis and keywords co-occurrence visualization with VOSviewer, the study identified key themes, prominent actors, and emerging patterns in CDLL research, aiming to inform pedagogical innovation within the Indonesian context. Results revealed a significant growth in CDLL publications, with research concentrated in China, the United States, and the United Kingdom. Keyword analysis identified four distinct thematic clusters: (1) computational linguistics and artificial intelligence (37%), highlighting the integration of deep learning, language modeling, and machine translation in language learning; (2) specialized applications of CDLL (22%), particularly in information management; (3) human-centered language learning (21.5%), emphasizing social interaction, cognitive processes, and technology integration; and (4) foundational principles of CDLL (19.5%), encompassing corpus linguistics, language acquisition, and pedagogical practices. These findings underscore the growing prominence of CDLL in language education and its potential to transform pedagogical practices in Indonesia by leveraging technology, promoting learner autonomy, and integrating authentic language data into diverse learning contexts.
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