Examining corpus-based language pedagogy (CBLP) practices in data-driven learning (DDL) for low-proficiency L2 English learners: A meta-analysis - Jing Liu, Qing Ma (Educational Technology & Society)
This meta-analysis evaluated the effectiveness of data-driven learning (DDL) among low-proficiency L2 English learners, addressing the mixed results found in previous meta-analyses. The study incorporated 38 studies involving 2085 participants, yielding 37 effect sizes from control-experimental (C/E) studies and 42 from pre- and post-test (P/P) studies. The findings demonstrated that DDL had a medium effect in C/E studies (g = 0.71) and a large effect in P/P studies (g = 1.43). The moderator analyses, based on the corpus-based language pedagogy (CBLP) framework by Ma et al. (2022), examined 7 pedagogical moderators. The results reaffirmed the efficacy of DDL in teaching lexicogrammatical items and suggested DDL’s curriculum flexibility; the duration of DDL did not significantly impact its effectiveness. Unique to this meta-analysis were findings that DDL was more effective for low-proficiency L2 learners of English when employing the following pedagogical strategies that cater to the cognitive-social nature of DDL: (1) utilizing paper-based concordancing to facilitate the pedagogical processing of corpus resources, (2) leveraging learners’ first language (L1) to improve comprehension of concordance meanings, (3) applying interactive communication with teacher verbal guidance or teacher verbal feedback attuned to learner responsiveness, and (4) providing teacher support in collaborative work to reduce the collaborative cognitive load on learners. Finally, this study proposed a holistic approach to CBLP design tailored to low-proficiency L2 learners, which presents an essential frontier for future research.