Examining the Effectiveness of Sketch Engine for Verb-noun Collocation Learning among Chinese Low-Proficiency Learners

Developing collocational competence is essential for Second Language proficiency, yet low-proficiency learners often struggle with verb-noun collocations due to the arbitrary nature of collocational patterns. While influential corpus tools such as the Corpus of Contemporary American English and the British National Corpus serve as valuable resources for collocation learning, they also pose significant cognitive challenges for low-proficiency learners. Therefore, this study investigated the effectiveness of corpus tools integrated with pedagogical practices in facilitating verb-noun collocation learning among Chinese low-proficiency learners, to identify the most suitable tool tailored to their specific learning needs. A quantitative research method was employed, utilizing an intervention design with pre-and post-tests to compare the impact of using Sketch Engine for Language Learning (SkELL) and COCA in enhancing learners’ verb-noun collocation proficiency. Furthermore, the eight-week intervention integrated SkELL and COCA into collocation lessons respectively, each designed within the framework of Corpus-Based Language Pedagogy (CBLP), presenting how strategic instruction maximized the potential of corpus tools in facilitating learners’ improvement in verb-noun collocation proficiency. The findings of the study revealed that SkELL significantly improved verb-noun collocation proficiency among Chinese low-proficiency EFL learners, attributed to its intuitive interface, simplified search functions, and scaffolded learning environment. Moreover, this study offers valuable contributions to the design of corpus-based instruction by demonstrating the effectiveness of SkELL as a corpus tool tailored to the needs of Chinese low-proficiency EFL learners. The study concluded with insights on pedagogical implications and recommendations for further research.