| 英文摘要 |
This paper aims to bridge the gap between linguistic research and the application of Data-Driven Learning (DDL) in teaching. Over the past three decades, DDL has been empirically proven to enhance second language acquisition compared to traditional teaching methods. However, DDL remains largely confined to higher education institutions due to several factors: the complexity of corpus platform operation, the difficulty of linguistic analysis, the challenges of training teachers, and the limited focus on teaching details in existing research. Given the compelling evidence of DDL’s effectiveness in second language learning, this paper seeks to introduce this valuable learning approach into Mandarin Chinese classrooms by offering two specific teaching recommendations for hands-off DDL models that can be directly applied in teaching practice. The first model, based on a collostructional analysis (Yeh et al., forthcoming), explores the synonymous verbs ''擦'' (Ca), ''塗'' (Tu), and ''抹'' (Mo). The second model employs data from the ''Corpus of Contemporary Taiwanese Mandarin'' (COCT) to explore the distinctions between near-synonyms''買'' (Mai) and ''購'' (Gou). To enable Mandarin language instructors to replicate and implement the hands-off DDL models in their lesson preparation, this article begins by explaining how to interpret linguistic analysis data. It then provides step-by-step guidance on operating corpus platforms, selecting target corpus data, and designing hands-off DDL teaching models, highlighting the key teaching details that require attention. The goal is to enable DDL to enter Mandarin Chinese classrooms more widely, benefiting both teachers and learners. |