Toward the Establishment of a Data-Driven Learning Model: Role of Learner Factors in Corpus-Based Second Language Vocabulary Learning

We investigated how learner factors, such as vocabulary proficiency, strategy use, and working memory, are associated with successful corpus-based second language (L2) vocabulary learning, in which learners are encouraged to analyze and explore large, structured collections of authentic language data (i.e., corpora) to resolve their lexical issues (i.e., data-driven learning [DDL]). After measuring L2 vocabulary proficiency and working memory capacity, 35 South Korean college students performed a DDL activity during an English reading task using a think-aloud protocol to document their strategy use. Through this we identified participants' lexical inferencing strategy use, including DDL-focused strategies, based on qualitative coding. Using path analysis, we identified that participants' DDL-focused strategy use largely influenced their vocabulary acquisition and retention, highlighting the pedagogical advantages of these strategies for successful DDL. We found that participants' L2 vocabulary proficiency and working memory contributed to their vocabulary acquisition and retention, indicating the roles of these factors in managing cognitive load in DDL. Future investigation into the causal relationship between improved working memory and corpus-based L2 vocabulary learning and the role of other learner factors, including motivation and learning style, is needed to extend our understanding of DDL.