Exploring relationships between individual differences and learning outcomes in data-driven learning: A meta-analytic path analysis approach

초록

Although the pedagogical benefits of data-driven learning (DDL) are well established in language learning, the role of individual differences—such as learners' demographic, cognitive, and affective factors—has received limited systematic attention. To address this gap, this study employed meta-analytic structural equation modeling to identify individual difference variables and analyze the structural relationships influencing language learning outcomes in DDL. Based on 25 studies (N = 1094), this analysis identified the following key predictors of DDL effectiveness: learners' working memory, L2 proficiency, positive perceptions of DDL, and familiarity with DDL (including corpus-consultation skills). Structural relationships revealed that L2 proficiency (β = 0.20, SE = 0.07, p = .003) and familiarity with DDL (β = 0.06, SE = 0.03, p = .046) significantly impacted learning outcomes. Additional analyses explored potential moderating factors, such as target-language domains, L1-L2 relatedness, instructional mode, and a concordancing approach; however, no significant moderating effects were observed.

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